Title
Sampled-data adaptive observer for state-affine systems with uncertain output equation
Abstract
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 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.
Year
DOI
Venue
2019
10.1016/j.automatica.2019.01.006
Automatica
Keywords
Field
DocType
Adaptive observer,Sampled-data nonlinear systems
Affine transformation,Observability,Nonlinear system,Control theory,Identifiability,Sampling (statistics),Novelty,Adaptive observer,Observer (quantum physics),Mathematics
Journal
Volume
Issue
ISSN
103
1
0005-1098
Citations 
PageRank 
References 
0
0.34
9
Authors
4
Name
Order
Citations
PageRank
Tarek Ahmed-Ali124526.90
koen tiels2143.15
Maarten Schoukens38113.10
F. Giri411029.41