Sampled-Data Adaptive Observer For a Class of State Affine Output-Injection Nonlinear Systems

T. Folin (1) , Tarek Hamed-Ali (1) , Fouad Giri (2, 1) , Laurent Burlion (3) , Françoise Lamnabhi-Lagarrigue (4)

1 ENSICAEN - École Nationale Supérieure d'Ingénieurs de Caen
2 Equipe Automatique - Laboratoire GREYC - UMR6072
3 ONERA - The French Aerospace Lab [Toulouse]
4 L2S - Laboratoire des signaux et systèmes

The problem of observer design is addressed for output-injection nonlinear systems. A major difficulty with this class of systems is that the state equation involves an output-dependent term that is explicitly dependent on unknown parameters. As the output is only accessible to measurement at sampling times, the output-dependent term turns out to be (almost all time) subject to a double uncertainty, making previous adaptive observers inappropriate. Presently, a new hybrid adaptive observer is designed and shown to be exponentially convergent under ad-hoc conditions.

Domains: Computer Science [cs]; Automatic Control Engineering; Engineering Sciences [physics]; Automatic

T. Folin, Tarek Hamed-Ali, Fouad Giri, Laurent Burlion, Françoise Lamnabhi-Lagarrigue. Sampled-Data Adaptive Observer For a Class of State Affine Output-Injection Nonlinear Systems. IEEE Transactions on Automatic Control, 2016, 61 (2), pp.462-467. ⟨10.1109/TAC.2015.2437522⟩. ⟨hal-01260152⟩