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Publications (10 of 24) Show all publications
Nüssgen, A., Lerch, A., Degen, R., Irmer, M., de Fries, M., Richter, F., . . . Ruschitzka, M. (2025). Reinforcement Learning in Mechatronic Systems: A Case Study on DC Motor Control. Circuits and Systems, 16(1), 1-24
Open this publication in new window or tab >>Reinforcement Learning in Mechatronic Systems: A Case Study on DC Motor Control
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2025 (English)In: Circuits and Systems, ISSN 2153-1285, E-ISSN 2153-1293, Vol. 16, no 1, p. 1-24Article in journal (Refereed) Published
Abstract [en]

The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system performance. This paper examines the utilization of reinforcement learning as a control strategy, with a particular focus on its deployment in pivotal stages of the product development lifecycle, specifically between system architecture and system integration and verification. A controller based on reinforcement learning was developed and evaluated in comparison to traditional proportional-integral controllers in dynamic and fault-prone environments. The results illustrate the superior adaptability, stability, and optimization potential of the reinforcement learning approach, particularly in addressing dynamic disturbances and ensuring robust performance. The study illustrates how reinforcement learning can facilitate the transition from conceptual design to implementation by automating optimization processes, enabling interface automation, and enhancing system-level testing. Based on the aforementioned findings, this paper presents future directions for research, which include the integration of domain-specific knowledge into the reinforcement learning process and the validation of this process in real-world environments. The results underscore the potential of artificial intelligence-driven methodologies to revolutionize the design and deployment of intelligent mechatronic systems.

Place, publisher, year, edition, pages
Scientific Research Publishing, 2025
Keywords
Artificial Intelligence in Product Development, Mechatronic Systems, Reinforcement Learning for Control, System Integration and Verification, Adaptive Optimization Processes, Knowledge-Based Engineering
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-552258 (URN)10.4236/cs.2025.161001 (DOI)
Available from: 2025-03-12 Created: 2025-03-12 Last updated: 2025-03-14Bibliographically approved
de Fries, M., Irmer, M., Thomas, K. & Degen, R. (2024). Innovative Test Field Approach for Agricultural Applications. In: : . Paper presented at 2. Fachtagung TestRig.
Open this publication in new window or tab >>Innovative Test Field Approach for Agricultural Applications
2024 (English)Conference paper, Oral presentation with published abstract (Refereed)
National Category
Agricultural Science
Identifiers
urn:nbn:se:uu:diva-552303 (URN)
Conference
2. Fachtagung TestRig
Available from: 2025-03-12 Created: 2025-03-12 Last updated: 2025-03-14
Nüßgen, A., Degen, R., Irmer, M., Richter, F., Boström, C. & Ruschitzka, M. (2024). Leveraging Robust Artificial Intelligence for Mechatronic Product Development: A Literature Review. International Journal of Intelligence Science, 14(01), 1-21
Open this publication in new window or tab >>Leveraging Robust Artificial Intelligence for Mechatronic Product Development: A Literature Review
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2024 (English)In: International Journal of Intelligence Science, ISSN 2163-0283, E-ISSN 2163-0356, Vol. 14, no 01, p. 1-21Article, review/survey (Refereed) Published
Abstract [en]

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Place, publisher, year, edition, pages
Scientific Research Publishing, 2024
Keywords
Artificial Intelligence, Mechatronic Product Development, Knowledge Management, Data Analysis, Optimization, Human Experts, Decision-Making Processes, V-Cycle
National Category
Production Engineering, Human Work Science and Ergonomics Software Engineering Other Mechanical Engineering
Identifiers
urn:nbn:se:uu:diva-523604 (URN)10.4236/ijis.2024.141001 (DOI)
Available from: 2024-02-21 Created: 2024-02-21 Last updated: 2025-03-14Bibliographically approved
Irmer, M., Rosenthal, R., Nüßgen, A., Degen, R., Thomas, K. & Ruschitzka, M. (2023). Design of a Model-Based Optimal Multivariable Control for the Individual Wheel Slip of a Two-Track Vehicle. In: : . Paper presented at 23rd Stuttgart International Symposium, 4-5 July 2023, Stuttgart.
Open this publication in new window or tab >>Design of a Model-Based Optimal Multivariable Control for the Individual Wheel Slip of a Two-Track Vehicle
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2023 (English)Conference paper, Published paper (Other academic)
National Category
Vehicle and Aerospace Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-495652 (URN)
Conference
23rd Stuttgart International Symposium, 4-5 July 2023, Stuttgart
Available from: 2023-01-31 Created: 2023-01-31 Last updated: 2025-03-14
Irmer, M., Degen, R., Nüssgen, A., Thomas, K., Henrichfreise, H. & Ruschitzka, M. (2023). Development and Analysis of a Detail Model for Steer-by-Wire Systems. IEEE Access, 11, 7229-7236
Open this publication in new window or tab >>Development and Analysis of a Detail Model for Steer-by-Wire Systems
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2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 7229-7236Article in journal (Refereed) Published
Abstract [en]

Steer-by-wire systems represent a key technology for highly automated and autonomous driving. In this context, robust steering control is a fundamental precondition for automated vehicle lateral control. However, there is a need for improvement due to degrees of freedom, signal delays, and nonlinear characteristics of the plant which are unconsidered in the design models for the design of current steering controls. To be able to design an extremely robust steering control, suitable optimal models of a steer-by-wire system are required. Therefore, this paper presents an innovative nonlinear detail model of a steer-by-wire system. The detail model represents all characteristics of a real steer-by-wire system. In the context of a dominance analysis of the detail model, all dominant characteristics of a steer-by-wire system, including parameter dependencies, are identified. Through model reduction, a reduced model of the steer-by-wire system is then developed that can be used for a subsequent robust control design. Furthermore, this paper compares the steer-by-wire system with a conventional electromechanical power steering and shows similarities as well as differences.

Place, publisher, year, edition, pages
IEEE, 2023
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-495645 (URN)10.1109/access.2023.3238107 (DOI)000922817400001 ()
Available from: 2023-01-31 Created: 2023-01-31 Last updated: 2025-03-14Bibliographically approved
Degen, R., Ott, H., Overath, F., Schyr, I. C. C., Klein, F., Leijon, M. & Ruschitzka, M. (2023). Development of a Lidar Model for the Analysis of Borderline Cases Including Vehicle Dynamics in a Virtual City Environment in Real Time. International Journal of Automotive Technology, 24(4), 955-968
Open this publication in new window or tab >>Development of a Lidar Model for the Analysis of Borderline Cases Including Vehicle Dynamics in a Virtual City Environment in Real Time
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2023 (English)In: International Journal of Automotive Technology, ISSN 1229-9138, E-ISSN 1976-3832, Vol. 24, no 4, p. 955-968Article in journal (Refereed) Published
Abstract [en]

Advanced driver assistance systems are an important step on the way towards the autonomous driving. However, there are new challenges in the release of increasingly complex systems. For the testing of those systems many test kilometers are necessary to represent sufficient diversity. Hence, the virtual testing of driver assistance systems brings new opportunities. In virtual environments, it is possible to run a much higher distance in a short time. Simultaneously, the complexity of the environment and the test scenarios are individually adjustable. It is possible to test scenarios that are not feasible in a real environment due to a risk of injury. A big challenge is the physical correct implementation of real vehicles and their components into the Virtual Reality. To enable a realistic virtual testing the vehicles surrounding sensors need to be modeled adequately. Thus, this paper presents an approach for the implementation of a Lidar model into a Virtual Reality. A physical Lidar model is combined with a real-time capable vehicle dynamics model to investigate the influence of vehicle movements to the sensor measurements. The models are implemented into a highly realistic virtual city environment. Finally, a test campaign shows the influence of the Lidars physics and the vehicle dynamics on the detection results.

Place, publisher, year, edition, pages
Springer NatureSpringer Nature, 2023
Keywords
Advanced driver assistance systems (ADAS), Autonomous mobility, Lidar simulation, Vehicle dynamics, Raytracing, Virtual environment, Sensor simulation
National Category
Vehicle and Aerospace Engineering
Identifiers
urn:nbn:se:uu:diva-509267 (URN)10.1007/s12239-023-0078-6 (DOI)001033702600003 ()
Available from: 2023-08-23 Created: 2023-08-23 Last updated: 2025-02-14Bibliographically approved
Irmer, M., Degen, R., Thomas, K. & Ruschitzka, M. (2023). Direct Discrete Design of a Multivariable LQG Compensator with Combined Discretization applied to a Steer-by-Wire System. In: : . Paper presented at Automotive meets Electronics.
Open this publication in new window or tab >>Direct Discrete Design of a Multivariable LQG Compensator with Combined Discretization applied to a Steer-by-Wire System
2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

On the way to highly automated and autonomous driving, a robustly designed steering system is a key component. Therefore, this article presents a direct discrete control design for modern steer-by-wire systems. The novel approach consists of a true multivariable control for both the driver´s steering torque and the rack position simultaneously using the requested torques of the downstream (AU) and upstream (FU) motor as control variables. For the control design, an optimal reduced plant model is used. It is derived from a detailed model of a steer-by-wire system with nine degrees of freedom. The reduced plant model is augmented by linear models for the reference and disturbance environment of the steer-by-wire system as well as discretized based on the characteristics of the input variables. For this augmented model, a direct discrete multivariable linear quadratic Gaussian (LQG) compensator design is performed. The proposed control design considers the entire environment of the real steering system. The direct discrete approach restores the good characteristics of the continuous control and ensures that the discretization does not have any adverse effects. As a result, the resulting discrete control system shows the same good dynamic characteristics as the continuous system and has excellent robustness characteristics. Hence, the presented control satisfies the requirements of a modern steering system and can be adapted to various driving situations.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-510739 (URN)
Conference
Automotive meets Electronics
Available from: 2023-09-02 Created: 2023-09-02 Last updated: 2025-03-14
Nüßgen, A., Degen, R., Irmer, M., Boström, C. & Ruschitzka, M. (2023). Intelligent analysis of components with regard to significant features for subsequent classification. In: : . Paper presented at 23rd Stuttgart International Symposium, Automotive and Engine Technology, 4-5 July 2023, Stuttgart.
Open this publication in new window or tab >>Intelligent analysis of components with regard to significant features for subsequent classification
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2023 (English)Conference paper, Published paper (Other academic)
Keywords
Knowledge Explosion, Data Mining, Machine Learning, Artificial Intelligence, Data Interpretation, Raytracing, Classification
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Sciences
Identifiers
urn:nbn:se:uu:diva-495655 (URN)
Conference
23rd Stuttgart International Symposium, Automotive and Engine Technology, 4-5 July 2023, Stuttgart
Available from: 2023-01-31 Created: 2023-01-31 Last updated: 2025-03-14
Nüssgen, A., Richter, F., Lerch, A., Degen, R., Irmer, M., Boström, C. & Ruschitzka, M. (2023). Intelligent Component Manufacturability Testing in Virtual Product Development. In: : . Paper presented at Artificial Intelligence und Machine Learning in der CAE-basierten Simulation, München, 23-24 Oktober, 2023. NAFEMS: International Association for the Engineering Modelling, Analysis and Simulation Community
Open this publication in new window or tab >>Intelligent Component Manufacturability Testing in Virtual Product Development
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2023 (German)Conference paper, Oral presentation with published abstract (Other academic)
Place, publisher, year, edition, pages
NAFEMS: International Association for the Engineering Modelling, Analysis and Simulation Community, 2023
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-523605 (URN)
Conference
Artificial Intelligence und Machine Learning in der CAE-basierten Simulation, München, 23-24 Oktober, 2023
Available from: 2024-02-21 Created: 2024-02-21 Last updated: 2025-03-12Bibliographically approved
Nüssgen, A., Richter, F., Krach, N., Irmer, M., Degen, R., Boström, C. & Ruschitzk, M. (2023). Robustness and Sensitivity of Artificial Neural Networks for Mechatronic Product Development. In: : . Paper presented at Automotive meets Electronics.
Open this publication in new window or tab >>Robustness and Sensitivity of Artificial Neural Networks for Mechatronic Product Development
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2023 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

In the fast-paced world of automotive development, the need for effective information exchange between different domains is becoming increasingly critical. The parallelized and agile nature of development demands early and frequent access to accurate information across various departments. However, deviations and uncertainties in data can lead to costly disruptions in the development process. Artificial intelligence based prediction models offer a potential solution by providing estimates based on previous projects or analyzed products, even in the early stages of development. While rough estimates may suffice initially, it is important to understand the accuracy of such predictions. This study therefore aims to evaluate the performance characteristics of different uncertainty analysis methods and assess their applicability in agile automotive development processes. By considering the specific requirements and constraints of each method, a decision tree is proposed to recommend suitable and situation-appropriate methods for performing uncertainty analyses in network prediction. The goal is to enhance data exchange between departments, mitigate disruptions, and ensure informed decision-making throughout the development process.

National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-510741 (URN)
Conference
Automotive meets Electronics
Available from: 2023-09-02 Created: 2023-09-02 Last updated: 2025-03-14
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1488-3778

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