Identification and Adaptive Tracking Control of Systems with Set-Valued Observations
Yanlong Zhao, Chinese Academy of Sciences
Abstract:
This talk studies the adaptive tracking control for systems with
set-valued observations. An algorithm is proposed for parameter
identification, based on which an adaptive control law is designed
via the certainty equivalence principle. It is shown that the
identification algorithm is both almost surely and mean square
convergent, and asymptotically optimal comparing to the Cramer-Rao
Lower Bound. The closed-loop system is stable, and the adaptive
tracking control is asymptotically optimal. A numerical example is
given to demonstrate the effectiveness of the algorithms and the
main results obtained.