Model Reference Adaptive Control with Linear-like Closed-loop Behavior

Mohamad T. Shahab, Daniel E. Miller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 languageEnglish (US)
Title of host publication2021 60th IEEE Conference on Decision and Control (CDC)
PublisherIEEE
Pages1069-1074
Number of pages6
ISBN (Print)978-1-6654-3660-1
DOIs
StatePublished - 2021

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