Candidate: Mohamad Shahab
Title: Step Tracking using Multi-Model Adaptive Control
Date: March 18, 2020
Time: 2:00 PM
Place: REMOTE PARTICIPATION
Supervisor(s): Miller, Daniel
Abstract:
Adaptive control is an approach used to deal with systems with uncertain or time-varying parameters. Here, we consider the problem of step tracking for a discrete-time plant with unknown plant parameters belonging to a compact uncertainty set. We carry out parameter estimation for a slightly modified plant; indeed, we cover the set of admissible parameters by a finite number of compact and convex sets, and use a parameter estimator based on the original Projection Algorithm for each. At each point in time, a switching algorithm is used to determine which estimates are used in the pole-placement based control law; our approach does not assume that the switching stops at any point in time. We prove that this adaptive controller guarantees desirable linear-like closed-loop behavior: exponential stability, a bounded noise gain and a convolution bound on the exogenous inputs. We also prove asymptotic tracking when the noise is constant.