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  • 1.
    Abdalmoaty, Mohamed
    KTH, Reglerteknik.
    Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. During the last decade, several numerical methods have been developed to approximately solve the maximum likelihood problem. A class of algorithms that attracted considerable attention is based on sequential Monte Carlo algorithms (also known as particle filters/smoothers) and particle Markov chain Monte Carlo algorithms. These algorithms were able to obtain impressive results on several challenging benchmark problems; however, their application is so far limited to cases where fundamental limitations, such as the sample impoverishment and path degeneracy problems, can be avoided.

    This thesis introduces relatively simple alternative parameter estimation methods that may be used for fairly general stochastic nonlinear dynamical models. They are based on one-step-ahead predictors that are linear in the observed outputs and do not require the computations of the likelihood function. Therefore, the resulting estimators are relatively easy to compute and may be highly competitive in this regard: they are in fact defined by analytically tractable objective functions in several relevant cases. In cases where the predictors are analytically intractable due to the complexity of the model, it is possible to resort to {plain} Monte Carlo approximations. Under certain assumptions on the data and some conditions on the model, the convergence and consistency of the estimators can be established. Several numerical simulation examples and a recent real-data benchmark problem demonstrate a good performance of the proposed method, in several cases that are considered challenging, with a considerable reduction in computational time in comparison with state-of-the-art sequential Monte Carlo implementations of the ML estimator.

    Moreover, we provide some insight into the asymptotic properties of the proposed methods. We show that the accuracy of the estimators depends on the model parameterization and the shape of the unknown distribution of the outputs (via the third and fourth moments). In particular, it is shown that when the model is non-Gaussian, a prediction error method based on the Gaussian assumption is not necessarily more accurate than one based on an optimally weighted parameter-independent quadratic norm. Therefore, it is generally not obvious which method should be used. This result comes in contrast to a current belief in some of the literature on the subject. 

    Furthermore, we introduce the estimating functions approach, which was mainly developed in the statistics literature, as a generalization of the maximum likelihood and prediction error methods. We show how it may be used to systematically define optimal estimators, within a predefined class, using only a partial specification of the probabilistic model. Unless the model is Gaussian, this leads to estimators that are asymptotically uniformly more accurate than linear prediction error methods when quadratic criteria are used. Convergence and consistency are established under standard regularity and identifiability assumptions akin to those of prediction error methods.

    Finally, we consider the problem of closed-loop identification when the system is stochastic and nonlinear. A couple of scenarios given by the assumptions on the disturbances, the measurement noise and the knowledge of the feedback mechanism are considered. They include a challenging case where the feedback mechanism is completely unknown to the user. Our methods can be regarded as generalizations of some classical closed-loop identification approaches for the linear time-invariant case. We provide an asymptotic analysis of the methods, and demonstrate their properties in a simulation example.

  • 2.
    Abdalmoaty, Mohamed
    KTH, Reglerteknik.
    Learning Stochastic Nonlinear Dynamical Systems Using Non-stationary Linear Predictors2017Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The estimation problem of stochastic nonlinear parametric models is recognized to be very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the maximum likelihood estimator and the optimal mean-square error predictor using Monte Carlo methods. Albeit asymptotically optimal, these methods come with several computational challenges and fundamental limitations.

    The contributions of this thesis can be divided into two main parts. In the first part, approximate solutions to the maximum likelihood problem are explored. Both analytical and numerical approaches, based on the expectation-maximization algorithm and the quasi-Newton algorithm, are considered. While analytic approximations are difficult to analyze, asymptotic guarantees can be established for methods based on Monte Carlo approximations. Yet, Monte Carlo methods come with their own computational difficulties; sampling in high-dimensional spaces requires an efficient proposal distribution to reduce the number of required samples to a reasonable value.

    In the second part, relatively simple prediction error method estimators are proposed. They are based on non-stationary one-step ahead predictors which are linear in the observed outputs, but are nonlinear in the (assumed known) input. These predictors rely only on the first two moments of the model and the computation of the likelihood function is not required. Consequently, the resulting estimators are defined via analytically tractable objective functions in several relevant cases. It is shown that, under mild assumptions, the estimators are consistent and asymptotically normal. In cases where the first two moments are analytically intractable due to the complexity of the model, it is possible to resort to vanilla Monte Carlo approximations. Several numerical examples demonstrate a good performance of the suggested estimators in several cases that are usually considered challenging.

  • 3.
    Abdalmoaty, Mohamed
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Coimbatore Anand, Sribalaji
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Teixeira, André
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Privacy and Security in Network Controlled Systems via Dynamic Masking2023In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 56, no 2, p. 991-996Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a new architecture to enhance the privacy and security of networked control systems against malicious adversaries. We consider an adversary which first learns the system using system identification techniques (privacy), and then performs a data injection attack (security). In particular, we consider an adversary conducting zero-dynamics attacks (ZDA) which maximizes the performance cost of the system whilst staying undetected. Using the proposed architecture, we show that it is possible to (i) introduce significant bias in the system estimates obtained by the adversary: thus providing privacy, and (ii) efficiently detect attacks when the adversary performs a ZDA using the identified system: thus providing security. Through numerical simulations, we illustrate the efficacy of the proposed architecture

  • 4.
    Abdalmoaty, Mohamed
    et al.
    KTH, Reglerteknik.
    Henrion, D.
    Rodrigues, L.
    Measures and LMIs for optimal control of piecewise-affine systems2013In: 2013 European Control Conference, ECC 2013, IEEE , 2013, p. 3173-3178, article id 6669627Conference paper (Refereed)
    Abstract [en]

    This paper considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector field, polynomial Lagrangian and semialgebraic input and state constraints. The OCP is first relaxed as an infinite-dimensional linear program (LP) over a space of occupation measures. This LP is then approached by an asymptotically converging hierarchy of linear matrix inequality (LMI) relaxations. The relaxed dual of the original LP returns a polynomial approximation of the value function that solves the Hamilton-Jacobi-Bellman (HJB) equation of the OCP. Based on this polynomial approximation, a suboptimal policy is developed to construct a state feedback in a sample-and-hold manner. The results show that the suboptimal policy succeeds in providing a suboptimal state feedback law that drives the system relatively close to the optimal trajectories and respects the given constraints.

  • 5.
    Abdalmoaty, Mohamed
    et al.
    Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
    Hjalmarsson, Håkan
    Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
    Linear Prediction Error Methods for Stochastic Nonlinear Models2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, p. 49-63Article in journal (Refereed)
    Abstract [en]

    The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

  • 6.
    Abdalmoaty, Mohamed
    et al.
    Division of Decision and Control Systems in the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm, Sweden.
    Hjalmarsson, Håkan
    Division of Decision and Control Systems in the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm, Sweden.
    Wahlberg, Bo
    Division of Decision and Control Systems in the School of Electrical Engineering and Computer Science at KTH Royal Institute of Technology, Stockholm, Sweden.
    The Gaussian MLE versus the Optimally weighted LSE2020In: IEEE signal processing magazine (Print), ISSN 1053-5888, E-ISSN 1558-0792, Vol. 37, no 6, p. 195-199Article in journal (Refereed)
    Abstract [en]

    In this note, we derive and compare the asymptotic covariance matrices of two parametric estimators: the Gaussian Maximum Likelihood Estimator (MLE), and the optimally weighted Least-Squares Estimator (LSE). We assume a general model parameterization where the model's mean and variance are jointly parameterized, and consider Gaussian and non-Gaussian data distributions.

  • 7.
    Abdalmoaty, Mohamed
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Noise reduction in Laguerre-domain discrete delay estimation2022In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 6254-6259Conference paper (Refereed)
    Abstract [en]

    This paper introduces a stochastic framework for a recently proposed discrete-time delay estimation method in Laguerre-domain, i.e. with the delay block input and output signals being represented by the corresponding Laguerre series. A novel Laguerre-domain disturbance model allowing the involved signals to be square-summable sequences is devised. The relation to two commonly used time-domain disturbance models is clarified. Furthermore, by forming the input signal in a certain way, the signal shape of an additive output disturbance can be estimated and utilized for noise reduction. It is demonstrated that a significant improvement in the delay estimation error is achieved when the noise sequence is correlated. The noise reduction approach is applicable to other Laguerre-domain problems than pure delay estimation.

  • 8.
    Abdalmoaty, Mohamed R. H.
    et al.
    KTH, Reglerteknik.
    Eriksson, Oscar
    KTH, Programvaruteknik och datorsystem, SCS.
    Bereza, Robert
    KTH, Reglerteknik.
    Broman, David
    KTH, Programvaruteknik och datorsystem, SCS.
    Hjalmarsson, Håkan
    KTH, Reglerteknik.
    Identification of Non-Linear Differential-Algebraic Equation Models with Process Disturbances2021In: 2021 60th IEEE Conference on Decision and Control (CDC), IEEE, 2021, p. 2300-2305Conference paper (Refereed)
    Abstract [en]

    Differential-algebraic equations (DAEs) arise naturally as a result of equation-based object-oriented modeling. In many cases, these models contain unknown parameters that have to be estimated using experimental data. However, often the system is subject to unknown disturbances which, if not taken into account in the estimation, can severely affect the model's accuracy. For non-linear state-space models, particle filter methods have been developed to tackle this issue. Unfortunately, applying such methods to non-linear DAEs requires a transformation into a state-space form, which is particularly difficult to obtain for models with process disturbances. In this paper, we propose a simulation-based prediction error method that can be used for non-linear DAEs where disturbances are modeled as continuous-time stochastic processes. To the authors' best knowledge, there are no general methods successfully dealing with parameter estimation for this type of model. One of the challenges in particle filtering  methods are random variations in the minimized cost function due to the nature of the algorithm. In our approach, a similar phenomenon occurs and we explicitly consider how to sample the underlying continuous process to mitigate this problem. The method is illustrated numerically on a pendulum example. The results suggest that the method is able to deliver consistent estimates.

  • 9.
    Abdalmoaty, Mohamed R. H.
    et al.
    KTH, Reglerteknik.
    Rojas, Cristian R.
    KTH, Reglerteknik.
    Hjalmarsson, Håkan
    KTH, Reglerteknik.
    Identification of a Class of Nonlinear Dynamical Networks2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 868-873Article in journal (Refereed)
    Abstract [en]

    Identifcation of dynamic networks has attracted considerable interest recently. So far the main focus has been on linear time-invariant networks. Meanwhile, most real-life systems exhibit nonlinear behaviors; consider, for example, two stochastic linear time-invariant systems connected in series, each of which has a nonlinearity at its output. The estimation problem in this case is recognized to be challenging, due to the analytical intractability of both the likelihood function and the optimal one-step ahead predictors of the measured nodes. In this contribution, we introduce a relatively simple prediction error method that may be used for the estimation of nonlinear dynamical networks. The estimator is defined using a deterministic predictor that is nonlinear in the known signals. The estimation problem can be defined using closed-form analytical expressions in several non-trivial cases, and Monte Carlo approximations are not necessarily required. We show, that this is the case for some block-oriented networks with no feedback loops and where all the nonlinear modules are polynomials. Consequently, the proposed method can be applied in situations considered challenging by current approaches. The performance of the estimation method is illustrated on a numerical simulation example.

  • 10.
    Abdalmoaty, Mohamed Rasheed
    et al.
    KTH, Reglerteknik.
    Hjalmarsson, Håkan
    KTH, Reglerteknik.
    Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem2018In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 15, p. 784-789Article in journal (Refereed)
    Abstract [en]

    The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presented and discussed.

  • 11.
    Abdalmoaty, Mohamed Rasheed
    et al.
    KTH, Reglerteknik.
    Hjalmarsson, Håkan
    KTH, Reglerteknik.
    Consistent Estimators of Stochastic MIMO Wiener Models based on Suboptimal Predictors2018In: 2018 IEEE Conference on Decision and Control (CDC), IEEE, 2018, p. 3842-3847Conference paper (Refereed)
    Abstract [en]

    We consider a parameter estimation problem in a general class of stochastic multiple-inputs multiple-outputs Wiener models, where the likelihood function is, in general, analytically intractable. When the output signal is a scalar independent stochastic process, the likelihood function of the parameters is given by a product of scalar integrals. In this case, numerical integration may be efficiently used to approximately solve the maximum likelihood problem. Otherwise, the likelihood function is given by a challenging multidimensional integral. In this contribution, we argue that by ignoring the temporal and spatial dependence of the stochastic disturbances, a computationally attractive estimator based on a suboptimal predictor can be constructed by evaluating scalar integrals regardless of the number of outputs. Under some conditions, the convergence of the resulting estimators can be established and consistency is achieved under certain identifiability hypothesis. We highlight the relationship between the resulting estimators and a recently proposed prediction error method estimator. We also remark that the method can be used for a wider class of stochastic nonlinear models. The performance of the method is demonstrated by a numerical simulation example using a 2-inputs 2-outputs model with 9 parameters.

  • 12.
    Abdalmoaty, Mohamed Rasheed
    et al.
    KTH, Reglerteknik.
    Hjalmarsson, Håkan
    KTH, Reglerteknik.
    Simulated Pseudo Maximum Likelihood Identification of Nonlinear Models2017In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 50, no 1, p. 14058-14063Article in journal (Refereed)
    Abstract [en]

    Nonlinear stochastic parametric models are widely used in various fields. However, for these models, the problem of maximum likelihood identification is very challenging due to the intractability of the likelihood function. Recently, several methods have been developed to approximate the analytically intractable likelihood function and compute either the maximum likelihood or a Bayesian estimator. These methods, albeit asymptotically optimal, are computationally expensive. In this contribution, we present a simulation-based pseudo likelihood estimator for nonlinear stochastic models. It relies only on the first two moments of the model, which are easy to approximate using Monte-Carlo simulations on the model. The resulting estimator is consistent and asymptotically normal. We show that the pseudo maximum likelihood estimator, based on a multivariate normal family, solves a prediction error minimization problem using a parameterized norm and an implicit linear predictor. In the light of this interpretation, we compare with the predictor defined by an ensemble Kalman filter. Although not identical, simulations indicate a close relationship. The performance of the simulated pseudo maximum likelihood method is illustrated in three examples. They include a challenging state-space model of dimension 100 with one output and 2 unknown parameters, as well as an application-motivated model with 5 states, 2 outputs and 5 unknown parameters.

  • 13. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A robust sliding mode controller with internal model for closed-loop artificial pancreas2010In: Medical and Biological Engineering and Computing, ISSN 0140-0118, E-ISSN 1741-0444, Vol. 48, no 12, p. 1191-1201Article in journal (Refereed)
  • 14. Abu-Rmileh, Amjad
    et al.
    Garcia-Gabin, Winston
    Zambrano, Darine
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Internal model sliding mode control approach for glucose regulation in type 1 diabetes2010In: Biomedical Signal Processing and Control, ISSN 1746-8094, Vol. 5, no 2, p. 94-102Article in journal (Refereed)
  • 15. Agüero, Juan C.
    et al.
    Goodwin, Graham C.
    Lau, Katrina
    Wang, Meng
    Silva, Eduardo I.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Three-degree of freedom adaptive power control for CDMA cellular systems2009In: Proc. 28th Global Telecommunications Conference, IEEE Communications Society, 2009, p. 2793-2798Conference paper (Refereed)
  • 16. Ahmed-Ali, Tarek
    et al.
    Tiels, Koen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-data adaptive observer for state-affine systems with uncertain output equation2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 103, p. 96-105Article in journal (Refereed)
  • 17. Ahmed-Ali, Tarek
    et al.
    Tiels, Koen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Schoukens, Maarten
    Giri, Fouad
    Sampled-Data Based State and Parameter Estimation for State-Affine Systems with Uncertain Output Equation2018Conference paper (Refereed)
    Abstract [en]

    The problem of sampled-data observer design is addressed for a class of state- and parameter-affine nonlinear systems. The main novelty in this class lies in the fact that the unknown parameters enter the output equation and the associated regressor is nonlinear in the output. Wiener systems belong to this class. The difficulty with this class of systems comes from the fact that output measurements are only available at sampling times causing the loss of the parameter-affine nature of the model (except at the sampling instants). This makes existing adaptive observers inapplicable to this class of systems. In this paper, a new sampled-data adaptive observer is designed for these systems and shown to be exponentially convergent under specific persistent excitation (PE) conditions that ensure system observability and identifiability. The new observer involves an inter-sample output predictor that is different from those in existing observers and features continuous trajectories of the state and parameter estimates.

  • 18.
    Aksakal, Can
    et al.
    Istanbul Technical University, Energy Institute.
    Sisman, Altug
    Istanbul Technical University, Energy Institute.
    On the Compatibility of Electric Equivalent Circuit Models for Enhanced Flooded Lead Acid Batteries Based on Electrochemical Impedance Spectroscopy2018In: Energies, E-ISSN 1996-1073, Vol. 11, no 1, p. 118-Article in journal (Refereed)
    Abstract [en]

    Electric equivalent circuit (EEC) models have been widely used to interpret the innerdynamics of all type of batteries. Added to this, they also have been used to estimate state of charge(SOC) and state of health (SOH) values in combination with different methods. Four EEC models areconsidered for enhanced flooded lead acid batteries (EFB) which are widely used in micro hybridvehicles. In this study, impedance and phase prediction capabilities of models throughout a frequencyspectrum from 1 mHz to 10 kHz are compared with those of experimental results to investigate theirconsistency with the data. The battery is charged, discharged, and aged according to appropriatestandards which imitates a lifetime of a micro hybrid vehicle battery under high current partialcycling. Impedance tests are repeated between different charge and health states until the end of thebattery’s lifetime. It is seen that adding transmission line elements to mimic the high porous electrodeelectrolyte interface to a double parallel constant phase element resistance model (ZARC) can increasethe model data representing capability by 100%. The mean average percentage error (MAPE) ofthe conventional model with respect to data is 3.2% while the same value of the transmission lineadded model found as 1.6%. The results can be helpful to represent an EFB in complex simulationenvironments, which are used in automobile industry.

  • 19.
    Albaba, Adnan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Patient-Specific Electrocardiogram Monitoring by Model-Based Stochastic Anomaly Detection2020In: 2020 European Control Conference (ECC), 2020, p. 735-740Conference paper (Refereed)
    Abstract [en]

    A novel model-based method for patient-specific detection of deformed electrocardiogram (ECG) beats is proposed and tested. Five parameters of a patient-specific nonlinear ECG model are estimated from data by nonlinear least-squares optimization. The normal variability of the model parameters is captured by estimated probability density functions. A binary classifier, based on stochastic anomaly detection methods, along with a pre-tuned classification threshold, is employed for detecting the abnormal ECG beats. We demonstrate the utility of the proposed approach by validating it on annotated arrhythmia data recorded under clinical conditions.

  • 20.
    Alipour, Mohammad
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Structural Chemistry.
    Yin, Litao
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Structural Chemistry.
    Tavallaey, Shiva Sander
    ABB AB Corp Res, Forskargrand 7, SE-72178 Västerås, Sweden.;KTH, Dept Mech, Sch Sci, SE-10044 Stockholm, Sweden..
    Andersson, Anna Mikaela
    ABB AB Corp Res, Forskargrand 7, SE-72178 Västerås, Sweden..
    Brandell, Daniel
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Structural Chemistry.
    A surrogate-assisted uncertainty quantification and sensitivity analysis on a coupled electrochemical-thermal battery aging model2023In: Journal of Power Sources, ISSN 0378-7753, E-ISSN 1873-2755, Vol. 579, article id 233273Article in journal (Refereed)
    Abstract [en]

    High-fidelity physics-based models are required to comprehend battery behavior at various operating condi-tions. This paper proposes an uncertainty quantification analysis on a coupled electrochemical-thermal aging model to improve the reliability of a battery model, while also investigating the impact of parametric model uncertainties on battery voltage, temperature, and aging. The coupled model's high computing cost, however, is a significant barrier to perform uncertainty quantification (UQ) and sensitivity analysis (SA). To address this problem, a surrogate model - i.e, by simulating the outcome of a quantity of interest that cannot be easily computed or measured - based on the Gaussian process regression (GPR) theory and principle component analysis (PCA) is built, using a small collection of finite element simulation results as synthetic training data. In total, 43 variable electrochemical-thermal parameters as well as 13 variable aging parameters are studied and estimated. Moreover, the trained surrogate model is also used in the parameterization of the electrochemical and thermal models. The results show that the uncertainties in the input parameters significantly affect the estimations of battery voltage, temperature, and aging. Based on this sensitivity analysis, the most influential parameters affecting the above mentioned battery outputs are reported. This approach is thereby helpful for developing robust and reliable high-fidelity battery aging models with potential applications in digital twins as well as for synthetic data generation.

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  • 21. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Mendonça, Teresa
    Rocha, Paula
    A compartmental model-based control strategy for NeuroMuscular Blockade level2011In: Proc. 18th IFAC World Congress, International Federation of Automatic Control , 2011, p. 599-604Conference paper (Refereed)
  • 22. Almeida, Juliana
    et al.
    Martins da Silva, Margarida
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Mendonça, Teresa
    Contributions to the initialization of online identification algorithms for anæsthesia: the NeuroMuscular Blockade case study2010In: Proc. 18th Mediterranean Conference on Control and Automation, Piscataway, NJ: IEEE , 2010, p. 1341-1346Conference paper (Refereed)
  • 23. Alonso, Hugo
    et al.
    Mendonça, Teresa
    Lemos, João M.
    Wigren, Torbjörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    A simple model for the identification of drug effects2009In: Proc. 6th International Symposium on Intelligent Signal Processing, Piscataway, NJ: IEEE , 2009, p. 269-273Conference paper (Refereed)
  • 24.
    Alverbäck, Adam
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    LQG-control of a Vertical Axis Wind Turbine with Focus on Torsional Vibrations2012Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this thesis it has been investigated if LQG control could be used to mitigate torsional oscillations in a variable speed, fixed pitch wind turbine. The wind turbine is a vertical axis wind turbine with a 40 m tall axis that is connected to a generator. The power extracted by the turbine is delivered to the grid via a passive rectifier and an inverter. By controlling the grid side inverter the current is controlled and hence the rotational speed can be controlled. A state space model was developed for the LQG controller. The model includes both the dynamics of the electrical system as swell as the two mass system, consisting of the turbine and the generator connected with a flexible shaft. The controller was designed to minimize a quadratic criterion that punishes both torsional oscillations, command following and input signal magnitude. Integral action was added to the controller to handle the nonlinear aerodynamic torque.

    The controller was compared to the existing control system that uses a PI controller to control the speed, and tested usingMATLAB Simulink. Simulations show that the LQG controller is just as good as the PI controller in controlling the speed of the turbine, and has the advantage that it can be tuned such that the occurrence of torsional oscillations is mitigated. The study also concluded that some external method of dampening torsional oscillations should be implemented to mitigate torsional oscillations in case of a grid fault or loss of PWM signal.

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  • 25.
    Andersson, Axel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Optimized Tuning of Parameters for HVDC Dynamic Performance Studies2013Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    HVDC (High Voltage Direct Current) is used all over the world for transmission of electric power due to lower losses compared to traditional HVAC (High Voltage Alternating Current). However, the procedure of converting AC into DC puts great demand on the control system of the converter stations. These control systems need to be tuned properly to give the HVDC system the correct dynamics to handle variations in the network load and other disturbances. In this thesis, it was investigated if optimization algorithms can be used for tuning of the control parameters. Focus was on three parts of the control system, the Current Control Amplifier, Voltage Dependent Current Order Limiter and the Rectifier Alpha Minimum Limiter. The Nelder & Mead Simplex method was used and several different objective functions were tested, including combinations of integral square error, integral absolute error, rise time and overshoot. Several different fault cases and scenarios were tested and results of the optimization were compared to the manually tuned control system. It was found that the results of the optimization were comparable with the manually tuned parameters for many of the cases tested. The biggest issue encountered was that the optimization algorithm often finds a local minimum in the objective function, leading to a suboptimal solution. This issue could be solved by running the optimization several times, using different initial values. It is concluded that using optimization algorithms could be a useful tool for tuning of the HVDC control system.

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  • 26.
    Andersson, Carl
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Horta Ribeiro, Antônio
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Univ Fed Minas Gerais, Grad Program Elect Engn, Ave Antonio Carlos 6627, BR-31270901 Belo Horizonte, MG, Brazil.
    Tiels, Koen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Wahlström, Niklas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Deep convolutional networks in system identification2019In: Proc. 58th IEEE Conference on Decision and Control, IEEE, 2019, p. 3670-3676Conference paper (Refereed)
    Abstract [en]

    Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

  • 27.
    Andersson, Helena
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Cubo, Rubén
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    The impact of deep brain stimulation on a simulated neuron: Inhibition, excitation, and partial recovery2018In: Proc. 16th European Control Conference, IEEE, 2018, p. 2034-2039Conference paper (Refereed)
  • 28.
    Andersson, Hugo
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Mattsson, Viktor
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Senek, Aleksandar
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Implementation of PID control using Arduino microcontrollers for glucose measurements and micro incubator applications2015Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The task is to build a low-cost thermostat and design necessary elements to perform a study on water mixed glucose-impedance at different temperatures and cell growth in a temperature-controlled incubator housing a magnetic field of up to 3 mT. The incubator was designed in solidworks and made to fit petri dishes of two relevant sizes and necessary wiring. The coils designed to extend across the large of the incubator with six turns and a 4A current to yield a sixth of the required magnetic field, as field strength increases linearly with current and turns increasing either of these is advised, and a large enough homogenous field was observed to create a suitable environment for the study. A thermistor, temperature sensitive resistance, was used to get reading and a modified wheatstone bridge was used with a multiplying op-amp to stabilize and improve accuracy of readings. Using an arduino microprocessor utilizing a PID library to calculate the power needed from thermistor readings of ambient temperature and an H-bridge controller by PWM from the Arduino a thermostat capable of driving a peltier-cell was produced capable of raising, lowering and maintaining predefined temperatures. 

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  • 29.
    Andrén, Jakob
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Online Minimum Jerk Velocity Trajectory Generation: for Underwater Drones2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis studies real-time reference ramping of human input for remotely operated vehicles and its effect on system control, power usage, and user experience. The implementation, testing, and evaluation were done on the remotely operated Blueye Pioneer underwater drone.

    The developed method uses minimum jerk trajectories for transitioning between varying target velocities with a constant end jerk target. It has a low computational cost and runs in real-time on the Blueye Pioneer underwater drone. The presented method produces a well-defined reference with continuous position, velocity, and acceleration states that can be used in the feedback loop.

    Experiments and simulations show that the method produces a smoother and more predictable motion path for the user. The motions are better suited for video recordings and remote navigation, compared to the direct usage of human input velocity. The smoother reference reduces the controller tracking error, the peak control input, and the energy usage. The introduced acceleration reference state is used for feedforward control on the system. It improves the feeling of controlling the drone by reducing the system lag, the position tracking error, and the rise time for velocity changes.

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  • 30.
    Anubhab, Ghosh
    et al.
    KTH Royal Institute of Technology.
    Abdalmoaty, Mohamed
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Chatterjee, Saikat
    KTH Royal Institute of Technology.
    Hjalmarsson, Håkan
    KTH Royal Institute of Technology.
    DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical modelsManuscript (preprint) (Other academic)
    Abstract [en]

    Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The majority of existing methods employ maximum likelihood or Bayesian estimation. However, these methods suffer from some limitations, most notably the substantial computational time for inference coupled with limited flexibility in application. In this work, we propose DeepBayes estimators that leverage the power of deep recurrent neural networks in learning an estimator. The method consists of first training a recurrent neural network to minimize the mean-squared estimation error over a set of synthetically generated data using models drawn from the model set of interest. The a priori trained estimator can then be used directly for inference by evaluating the network with the estimation data. The deep recurrent neural network architectures can be trained offline and ensure significant time savings during inference. We experiment with two popular recurrent neural networks -- long short term memory network (LSTM) and gated recurrent unit (GRU). We demonstrate the applicability of our proposed method on different example models and perform detailed comparisons with state-of-the-art approaches. We also provide a study on a real-world nonlinear benchmark problem. The experimental evaluations show that the proposed approach is asymptotically as good as the Bayes estimator. 

  • 31.
    Archetti, Joao Antonio Guedes
    et al.
    Univ Fed Juiz de Fora, Elect Engn Program, Multiplatform Simulat Lab, Juiz De Fora, MG, Brazil..
    Silva Junior, Dalmo C.
    Univ Fed Juiz de Fora, Elect Engn Program, Multiplatform Simulat Lab, Juiz De Fora, MG, Brazil.;Acad Univ Ctr, Juiz De Fora, MG, Brazil..
    de Medeiros, Lucio
    Lactec Inst, Curitiba, PR, Brazil..
    Salamanca, Henry L. L.
    Lactec Inst, Curitiba, PR, Brazil..
    Fuchs, Leonardo
    Lactec Inst, Curitiba, PR, Brazil..
    de Oliveira, Leonardo Willer
    Univ Fed Juiz de Fora, Elect Engn Program, Multiplatform Simulat Lab, Juiz De Fora, MG, Brazil..
    Gonçalves de Oliveira, Janaína
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Electricity. Univ Fed Juiz de Fora, Elect Engn Program, Multiplatform Simulat Lab, Juiz De Fora, MG, Brazil..
    Real time validation of a control system for microgrids with distributed generation and storage resources2023In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 223, article id 109683Article in journal (Refereed)
    Abstract [en]

    Faced with a scenario in which the increase in renewable energy generated near consumer centers can cause problems for the operation of the electrical network, the present work proposes a real-time simulation model for automation and control systems of electrical distribution networks with microgrids, distributed generation, and storage resources. The proposed model consists of a hardware-in-the-loop control with the aid of a simulation tool in conjunction with a Real-Time Digital Simulator (RTDS) and considers the dynamic behavior of switched elements and inverters. A communication platform using TCP/IP protocol between RTDS (power system) and MatLab/Simulink (optimization algorithms) allows the operation of the network in grid-connected and islanded mode, guaranteeing the computational time for experimental implementation. For the first mode, an algorithm is proposed to solve an optimal dispatch energy storage system problem. Second mode, an algorithm is proposed to solve a load shedding problem. The objective is to operate the microgrids optimally and evaluate the performance of a storage system based on real data from the state of Parana, in Brazil. Results show that the optimization algorithms are experimentally applicable, obtaining reasonable computational time to find optimal solutions and an assertive decision-making to meet the objectives. Thus, the proposed framework is a potential tool to validate algorithms for active management of microgrids in real-time simulation.

  • 32. Aslani, Mohammad
    et al.
    Seipel, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wiering, Marco
    Continuous residual reinforcement learning for traffic signal control optimization2018In: Canadian journal of civil engineering (Print), ISSN 0315-1468, E-ISSN 1208-6029, Vol. 45, no 8, p. 690-702Article in journal (Refereed)
  • 33. Axelson-Fisk, Magnus
    Distribution of Control Effort in Multi-Agent Systems: Autonomous systems of the world, unite!2020Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As more industrial processes, transportation and appliances have been automated or equipped with some level of artificial intelligence, the number and scale of interconnected systems has grown in the recent past. This is a development which can be expected to continue and therefore the research in performance of interconnected systems and networks is growing. Due to increased automation and sheer scale of networks, dynamically scaling networks is an increasing field and research into scalable performance measures is advancing.

    Recently, the notion gamma-robustness, a scalable network performance measure, was introduced as a measurement of interconnected systems robustness with respect to external disturbances.

    This thesis aims to investigate how the distribution of control effort and cost, within interconnected system, affects network performance, measured with gamma-robustness. Further, we introduce a notion of fairness and a measurement of unfairness in order to quantify the distribution of network properties and performance. With these in place, we also present distributed algorithms with which the distribution of control effort can be controlled in order to achieve a desired network performance. We close with some examples to show the strengths and weaknesses of the presented algorithms.

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  • 34.
    Ayotte, John
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Dynamic positioning of a semi-submersible, multi-turbine wind power platform2015Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As a growing market for offshore wind power has created a niche for deep-water installations, offshore floating wind solutions have become more and more viable as a renewable energy source. This technology is currently in development and as with many new technologies, many traditional design methods are found lacking. In the multi-turbine platform design investigated, turbine units are placed closely together to conserve material use and reduce cost, however with such tightly spaced turbines; wake interaction poses a threat to the productivity and the lifespan of the installation. In order to fully capitalize on the substantial increase in available wind energy far at sea, it is important that these floating parks operate in an optimal way. The platform investigated in this report sports 3, 6MW turbines which must be positioned such that wake interference is minimized; the platform must always bear a windward heading. 

    Maneuvering ocean going vessels has been practiced using automated dynamic positioning systems in the gas and oil industry for over 50 years, often employing submerged thrusters as a source of propulsion. These systems are mostly diesel powered and require extra operational maintenance, which would otherwise increase the cost and complexity of a floating wind farm. In this paper, it is suggested that the wind turbines themselves may be used to provide the thrust needed to correct the platform heading, thus eliminating the practical need for submerged thrusters. By controlling the blade pitch of the wind turbines, a turning moment (torque) can be exerted on the platform to correct heading (yaw) relative wind direction. Using the Hexicon H3-18MW platform as a starting point; hydrodynamic, aerodynamic and electromechanical properties of the system are explored, modeled and attempts at model predictive control are made. Preliminary results show that it is possible to control the H3’s position (in yaw) relative the wind using this novel method.

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    Dynamic positioning of a semi-submersible, multi-turbine wind power platform
  • 35.
    Barkefors, Annea
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Adapting an MSE controller for active noise control to nonstatic noise statisticsManuscript (preprint) (Other academic)
  • 36.
    Barkefors, Annea
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Linear Quadratic Gaussian Controllers for Feedforward Active Noise Control: Pushing Performance and Moving Towards Adaptive Control2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Active noise control is a research area focused on using destructive interference of sound fields to attenuate undesired noise. Methods for active noise control are best suited for low frequency noise, as the complexity of the problem grows rapidly with frequency. Coincidentally, passive means of damping have the opposite quality in that they work better for higher frequencies and become bulky and impractical for low frequencies. Applications for active noise control range from fan noise in ducts, noise-cancelling headphones and noise in cars to propeller induced aircraft cabin noise. In this comprehensive summary, the underlying principles of active noise control are presented and the control problem is discussed. Several aspects of the control system are introduced to give an introduction to the research papers that are the basis of this licentiate thesis. The work behind the thesis is focused on a Multiple-Input Multiple-Output (MIMO) Minimal Mean Square Error (MMSE) Linear Quadratic Gaussian (LQG) feedforward controller. This controller is shown to achieve uniform damping in an extended region in space and push the upper frequency that can be controlled. The influence of different design variables has been investigated, and the properties of the control path analyzed with consideration of its ability to suppress noise of prescribed spectral properties over an extended region. In this context, it has been shown how to use the reproducibility of the primary noise path by the control path as an indication of achievable performance for a given control system. Finally, the controller has been adapted to follow changes in the primary noise statistics, an approach that seems promising to considerably raise the performance of the controller.

    List of papers
    1. Extending the area silenced by active noise control using multiple loudspeakers
    Open this publication in new window or tab >>Extending the area silenced by active noise control using multiple loudspeakers
    2012 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference, 2012, p. 325-328Conference paper, Published paper (Refereed)
    Abstract [en]

    Active noise control is of increasing interest in e.g. cars, but the zone of noise damping becomes limited in reverberant environments. We investigate the possibility of extending this spatial zone significantly, by using multiple control loudspeakers. MIMO feedforward controllers designed by linear quadratic control theory are here shown to increase the limiting frequency for uniform damping in a 0.3×0.3 m test area, from 200 Hz to around 600 Hz.

    National Category
    Signal Processing
    Research subject
    Electrical Engineering with specialization in Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-181013 (URN)10.1109/ICASSP.2012.6287882 (DOI)000312381400080 ()978-1-4673-0044-5 (ISBN)
    Conference
    IEEE International Conference on Acoustics, Speech and Signal Processing, 25-30 March, 2012 ICASSP, Kyoto, Japan
    Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2014-05-07Bibliographically approved
    2. MIMO design of active noise controllers for car interiors: Extending the silenced region at higher frequencies
    Open this publication in new window or tab >>MIMO design of active noise controllers for car interiors: Extending the silenced region at higher frequencies
    2012 (English)In: 2012 American Control Conference, Montréal, Canada, 2012, p. 6140-6147Conference paper, Published paper (Refereed)
    National Category
    Signal Processing
    Research subject
    Electrical Engineering with specialization in Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-181008 (URN)
    Conference
    2012 American Control Conference, Montréal, Canada
    Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2014-05-07
    3. An investigation of a theoretical tool for predicting performance of an active noise control system.
    Open this publication in new window or tab >>An investigation of a theoretical tool for predicting performance of an active noise control system.
    2012 (English)In: 19th International Congress on Sound and Vibration, ICSV19, Vilnius, Lithuania, 2012, p. 1-8Conference paper, Published paper (Refereed)
    National Category
    Signal Processing
    Research subject
    Electrical Engineering with specialization in Signal Processing
    Identifiers
    urn:nbn:se:uu:diva-181014 (URN)
    Conference
    ICSV19, Vilnius, Lithuania
    Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2014-05-07
    4. Design and Analysis of Linear Quadratic Gaussian Feedforward Controllers for Active Noise Control
    Open this publication in new window or tab >>Design and Analysis of Linear Quadratic Gaussian Feedforward Controllers for Active Noise Control
    2014 (English)In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, E-ISSN 1558-7924, Vol. 22, no 12, p. 1777-1791Article in journal (Refereed) Published
    Abstract [en]

    A method for sound field control applied to active noise control is presented and evaluated. The method uses Linear Quadratic Gaussian (LQG) feedforward control to find a Minimal Mean Square Error (MMSE)-optimal linear sound field controller under a causality constraint. It is obtained by solving a polynomial matrix spectral factorization and a linear (Diophantine) polynomial matrix equation. An important component in the design is the control signal penalty term of the criterion. Its use and influence is here discussed and evaluated using measured room impulse responses. The results indicate that the use of a relatively simple, frequency-weighted penalty on individual control signals provides most of the benefits obtainable by the considered more advanced alternative. We also introduce and illustrate several tools for performance analysis. An analytical expression for the attainable performance clearly reveals the performance loss generated by having to use a causal controller instead of the ideal noncausal controller. This loss is largest at low frequencies. Furthermore, we introduce a measure of the reproducibility of the target noise sound field with given control loudspeaker setups and room transfer functions. It describes how well a controller that uses an input subspace of dimension equal to the effective rank of the system is able to reproduce a target sound field. This performance measure can e.g. be used to support the selection of good combinations of placements of control loudspeakers.

    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-223813 (URN)10.1109/TASLP.2014.2349856 (DOI)000341627500008 ()
    Funder
    Swedish Research Council, 2009-5527
    Available from: 2014-04-27 Created: 2014-04-27 Last updated: 2017-12-05Bibliographically approved
    5. Adapting an MSE controller for active noise control to nonstatic noise statistics
    Open this publication in new window or tab >>Adapting an MSE controller for active noise control to nonstatic noise statistics
    (English)Manuscript (preprint) (Other academic)
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-223814 (URN)
    Available from: 2014-04-27 Created: 2014-04-27 Last updated: 2014-05-07
  • 37.
    Barkefors, Annea
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Sternad, Mikael
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Brännmark, Lars-Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Design and Analysis of Linear Quadratic Gaussian Feedforward Controllers for Active Noise Control2014In: IEEE Transactions on Audio, Speech, and Language Processing, ISSN 1558-7916, E-ISSN 1558-7924, Vol. 22, no 12, p. 1777-1791Article in journal (Refereed)
    Abstract [en]

    A method for sound field control applied to active noise control is presented and evaluated. The method uses Linear Quadratic Gaussian (LQG) feedforward control to find a Minimal Mean Square Error (MMSE)-optimal linear sound field controller under a causality constraint. It is obtained by solving a polynomial matrix spectral factorization and a linear (Diophantine) polynomial matrix equation. An important component in the design is the control signal penalty term of the criterion. Its use and influence is here discussed and evaluated using measured room impulse responses. The results indicate that the use of a relatively simple, frequency-weighted penalty on individual control signals provides most of the benefits obtainable by the considered more advanced alternative. We also introduce and illustrate several tools for performance analysis. An analytical expression for the attainable performance clearly reveals the performance loss generated by having to use a causal controller instead of the ideal noncausal controller. This loss is largest at low frequencies. Furthermore, we introduce a measure of the reproducibility of the target noise sound field with given control loudspeaker setups and room transfer functions. It describes how well a controller that uses an input subspace of dimension equal to the effective rank of the system is able to reproduce a target sound field. This performance measure can e.g. be used to support the selection of good combinations of placements of control loudspeakers.

  • 38.
    Baumann, Dominik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence. Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland..
    Kowalczyk, Krzysztof
    Wroc Iaw Univ Sci & Technol, Dept Control Syst & Mechatron, Wroc Iaw, Poland..
    Tiels, Koen
    Eindhoven Univ Technol, Dept Mech Engn, Eindhoven, Netherlands..
    Wachel, Pawe L.
    Wroc Iaw Univ Sci & Technol, Dept Control Syst & Mechatron, Wroc Iaw, Poland..
    A computationally lightweight safe learning algorithm2023In: 2023 62nd IEEE Conference on Decision and Control, (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1022-1027Conference paper (Refereed)
    Abstract [en]

    Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response to this need, the field of safe learning has emerged with algorithms that can provide probabilistic safety guarantees without knowledge of the underlying system dynamics. Those algorithms often rely on Gaussian process inference. Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems. In this paper, we propose a safe learning algorithm that provides probabilistic safety guarantees but leverages the Nadaraya-Watson estimator instead of Gaussian processes. For the Nadaraya-Watson estimator, we can reach logarithmic scaling with the number of data points. We provide theoretical guarantees for the estimates, embed them into a safe learning algorithm, and show numerical experiments on a simulated seven-degrees-of-freedom robot manipulator.

  • 39.
    Baumann, Dominik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Aalto Univ, Dept Elect Engn & Automat, Espoo 00076, Finland..
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Safe Reinforcement Learning in Uncertain Contexts2024In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 40, p. 1828-1841Article in journal (Refereed)
    Abstract [en]

    When deploying machine learning algorithms in the real world, guaranteeing safety is an essential asset. Existing safe learning approaches typically consider continuous variables, i.e., regression tasks. However, in practice, robotic systems are also subject to discrete, external environmental changes, e.g., having to carry objects of certain weights or operating on frozen, wet, or dry surfaces. Such influences can be modeled as discrete context variables. In the existing literature, such contexts are, if considered, mostly assumed to be known. In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables. To achieve this, we derive frequentist guarantees for multiclass classification, allowing us to estimate the current context from measurements. Furthermore, we propose an approach for identifying contexts through experiments. We discuss under which conditions we can retain theoretical guarantees and demonstrate the applicability of our algorithm on a Furuta pendulum with camera measurements of different weights that serve as contexts.

  • 40.
    Baumann, Dominik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
    Solowjow, Friedrich
    Johansson, Karl Henrik
    Trimpe, Sebastian
    Identifying Causal Structure in Dynamical Systems2022In: Transactions on Machine Learning Research, E-ISSN 2835-8856Article in journal (Refereed)
  • 41.
    Bereza, Robert
    et al.
    KTH Royal Institute of Technology.
    Eriksson, Oscar
    KTH Royal Institute of Technology.
    Abdalmoaty, Mohamed R.-H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Broman, David
    KTH Royal Institute of Technology.
    Hjalmarsson, Håkan
    KTH Royal Institute of Technology.
    Stochastic Approximation for Identification of Non-Linear Differential-Algebraic Equations with Process Disturbances2022In: 2022 IEEE 61st Conference on Decision and Control (CDC), Institute of Electrical and Electronics Engineers (IEEE), 2022, p. 6712-6717Conference paper (Refereed)
    Abstract [en]

    Differential-algebraic equations, commonly used to model physical systems, are the basis for many equation-based object-oriented modeling languages. When systems described by such equations are influenced by unknown process disturbances, estimating unknown parameters from experimental data becomes difficult. This is because of problems with the existence of well-defined solutions and the computational tractability of estimators. In this paper, we propose a way to minimize a cost function-whose minimizer is a consistent estimator of the true parameters-using stochastic gradient descent. This approach scales significantly better with the number of unknown parameters than other currently available methods for the same type of problem. The performance of the method is demonstrated through a simulation study with three unknown parameters. The experiments show a significantly reduced variance of the estimator, compared to an output error method neglecting the influence of process disturbances. The proposed approach is also able to reduce the estimation bias of parameters that the output error method particularly struggles with.

  • 42.
    Bergman, Henrik Dan
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Microsystems Technology.
    Increasing the Writing Resolution for Electro-hydrodynamic 3D-Printing: by Active Steering of e-jet2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Additive manufacturing has grown considerably during the last couple of decades, whether it comes to the printing of metal structure or living cells. Additive manufacturing techniques relays on the successive addition of material to create the wanted structure. Among the diversity of these many printing techniques, electrohydrodynamic 3D-printing is of particular interest, as the technique has a promising outlook for high-resolution printing on the microscale. The technique is compatible with a myriad of thermoplastics, but its writing resolution is limited due to the inherent affect the manufacturing process has on the material. Electrostatic forces between already deposited fibres and the fibre in light affect the final position of printed fibre. This thesis evaluates the possibility to increase the writing resolution in melt electrohydrodynamic 3D printing by a closed-loop feedback system. Components were built and added to an already existing printing setup to implement in-situ measurements of the fibres position as well as active electrostatic guiding of the fibre. The setup consisted of a camera that determined the position of the fibre; the position was then used in a PID controller to calculate an appropriate potential. The potential was forwarded to a high voltage amplifier, connected to a steering electrode, mounted in the vicinity of the jet. The setup built for one-dimensional steering of the fibre improved the printing accuracy by ten times through suppressing the repulsive/attractive forces, where the process variable of the PID controller was measured. However, the precision decreased roughly four times as it was deposited on the substrate. The limitations of the system have been evaluated, and possible improvements for the two-dimensional control of the fibre are further discussed.

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  • 43.
    Bijl, Hildo
    et al.
    Delft University of Technology.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Optimal controller/observer gains of discounted-cost LQG systems2019In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, p. 471-474Article in journal (Refereed)
    Abstract [en]

    The linear-quadratic-Gaussian (LQG) control paradigm is well-known in literature. The strategy of minimizing the cost function is available, both for the case where the state is known and where it is estimated through an observer. The situation is different when the cost function has an exponential discount factor, also known as a prescribed degree of stability. In this case, the optimal control strategy is only available when the state is known. This paper builds onward from that result, deriving an optimal control strategy when working with an estimated state. Expressions for the resulting optimal expected cost are also given. 

  • 44. Bijl, Hildo
    et al.
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Artificial Intelligence.
    van Wingerden, Jan-Willem
    Verhaegen, Michel
    System identification through online sparse Gaussian process regression with input noise2017In: IFAC Journal of Systems and Control, ISSN 2468-6018, Vol. 2, p. 1-11Article in journal (Refereed)
  • 45. Bijl, Hildo
    et al.
    van Wingerden, Jan-Willem
    Schön, Thomas B.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Verhaegen, Michel
    Mean and variance of the LQG cost function2016In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 67, p. 216-223Article in journal (Refereed)
    Abstract [en]

    Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the resulting cost function. The contribution of this paper is a set of analytic expressions for the mean and variance of the LQG cost function. These expressions are derived using two different methods, one using solutions to Lyapunov equations and the other using only matrix exponentials. Both the discounted and the non-discounted cost function are considered, as well as the finite-time and the infinite-time cost function. The derived expressions are successfully applied to an example system to reduce the probability of the cost exceeding a given threshold.

  • 46. Birk, Wolfgang
    et al.
    Hostettler, Roland
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.
    Razi, Maryam
    Atta, Khalid
    Tammia, Rasmus
    Automatic generation and updating of process industrial digital twins for estimation and control: A review2022In: Frontiers in Control Engineering, E-ISSN 2673-6268, Vol. 3, article id 954858Article in journal (Refereed)
    Abstract [en]

    This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. 

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  • 47. Bisoffi, Andrea
    et al.
    Andersson Sundén, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Asp, E.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Binda, Federico
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Cecconello, Marco
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Conroy, Sean
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Dzysiuk, Nataliia
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Ericsson, Göran
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Eriksson, Jacob
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Hellesen, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Hjalmarsson, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Possnert, Göran
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Sjöstrand, Henrik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Skiba, Mateusz
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Weiszflog, Matthias
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Applied Nuclear Physics.
    Zychor, I.
    Hybrid cancellation of ripple disturbances arising in AC/DC converters2017In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 77, p. 344-352Article in journal (Refereed)
    Abstract [en]

    In AC/DC converters, a peculiar periodic nonsmooth waveform arises, the so-called ripple. In this paper we propose a novel model that captures this nonsmoothness by means of a hybrid dynamical system performing state jumps at certain switching instants, and we illustrate its properties with reference to a three phase diode bridge rectifier. As the ripple corrupts an underlying desirable signal, we propound two observer schemes ensuring asymptotic estimation of the ripple, the first with and the second without knowledge of the switching instants. Our theoretical developments are well placed in the context of recent techniques for hybrid regulation and constitute a contribution especially for our second observer, where the switching instants are estimated. Once asymptotic estimation of the ripple is achieved, the ripple can be conveniently canceled from the desirable signal, and thanks to the inherent robustness properties of the proposed hybrid formulation, the two observer schemes require only that the desirable signal is slowly time varying compared to the ripple. Exploiting this fact, we illustrate the effectiveness of our second hybrid observation law on experimental data collected from the Joint European Torus tokamak. 

  • 48.
    Björk, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Dynamic models with quantized output for modeling patient response to pharmacotherapy2010In: Proc. International Conference on Control Applications: CCA 2010, Piscataway, NJ: IEEE , 2010, p. 1029-1034Conference paper (Refereed)
    Abstract [en]

    This article presents a way of modeling patient response to a pharmacotherapy by means of dynamic models with quantized output. The proposed modeling technique is exemplified by treatment of Parkinson's disease with Duodopa ®, where the drug is continuously administered via duodenal infusion. Titration of Duodopa ® is currently performed manually by a nurse judging the patient's motor symptoms on a quantized scale and adjusting the drug flow provided by a portable computer-controlled infusion pump. The optimal drug flow value is subject to significant inter-individual variation and the titration process might take up to two weeks for some patients. In order to expedite the titration procedure via automation, as well as to find optimal dosing strategies, a mathematical model of this system is sought. The proposed model is of Wiener type with a linear dynamic block, cascaded with a static nonlinearity in the form of a non-uniform quantizer where the quantizer levels are to be identified. An identification procedure based on the prediction error method and the Gauss-Newton algorithm is suggested. The datasets available from titration sessions are scarce so that finding a parsimonious model is essential. A few different model parameterizations and identification algorithms were initially evaluated. The results showed that models with four parameters giving accurate predictions can be identified for some of the available datasets.

  • 49.
    Björklund, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Control Strategies for VSC-HVDC links in Weak AC Systems2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this master thesis control systems for a voltage-source converter HVDC connected to weak ac networks are investigated. HVDC stands for high voltage direct current and is a way to transfer power in the electrical power system. A HVDC uses direct current (dc) instead of alternate current (ac) to transfer power, which requires transformation between ac and dc since most power grids are ac networks. The HVDC uses converters to transform ac to dc and dc to ac and the converter requires a control system. A complete control system of a voltage source converter HVDC contains many different parts. The part investigated in this thesis is the active power control. Different structures containing PID controllers have been tested and evaluated with respect to stability and performance using control theory. The impact of weak ac networks has been evaluated in regards to the different control structures. The investigations have been conducted using mainly steady-state simulations. Based on the simulation and analyzes of the simulation results a promising control structure has been obtained and suggested for further investigation.

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  • 50.
    Björklund, Marcus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
    Fjärstedt, Eric
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
    Active Stabilizer: Independent Project in Electrical Engineering2017Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
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