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Sampled-Data Based State and Parameter Estimation for State-Affine Systems with Uncertain Output Equation
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för systemteknik. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Reglerteknik.ORCID-id: 0000-0001-9279-110X
2018 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2018. nr 15, s. 491-496
Serie
IFAC-PapersOnLine, ISSN 2405-8963 ; 51:15
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-366103DOI: 10.1016/j.ifacol.2018.09.193ISI: 000446599200084OAI: oai:DiVA.org:uu-366103DiVA, id: diva2:1263771
Konferanse
SYSID 2018, July 9–11, Stockholm, Sweden
Forskningsfinansiär
EU, European Research Council, 320378Tilgjengelig fra: 2018-10-08 Laget: 2018-11-16 Sist oppdatert: 2018-12-14bibliografisk kontrollert

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