Abstract
It is typically proven in adaptive control that asymptotic stabilization and tracking holds, and that at best a bounded-noise bounded-state property is proven. Recently, it has been shown in both the pole-placement control and the d-step ahead control settings that if, as part of the adaptive controller, a parameter estimator based on the original projection algorithm is used and the parameter estimates are restricted to a convex set, then the closed-loop system experiences linear-like behavior: exponential stability, a bounded gain on the noise in every p-norm, and a convolution bound on the exogenous inputs; this can be leveraged to provide tolerance to unmodelled dynamics and plant parameter time-variation. In this paper, we extend the approach to the more general Model Reference Adaptive Control (MRAC) problem and demonstrate that we achieve the same desirable linear-like closed-loop properties.
| Original language | English (US) |
|---|---|
| Title of host publication | 2021 60th IEEE Conference on Decision and Control (CDC) |
| Publisher | IEEE |
| Pages | 1069-1074 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-6654-3660-1 |
| DOIs | |
| State | Published - 2021 |
Bibliographical note
KAUST Repository Item: Exported on 2022-10-14Acknowledgements: Support for this work was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC).