Candidate: Craig Joseph Lalumiere
Title: Supervisory Adaptive Control Revisited: Linear-like Convolution Bounds
Date: July 29, 2022
Place: EIT 3142
Supervisor(s): Miller, Daniel
This is demonstrated in two cases: the first is the typical application of Supervisory Control - an integral control law is used to achieve step tracking in the presence of a constant disturbance. It is shown that the tracking error exponentially goes to zero when the disturbance is constant, and is bounded above by a linear convolution when it is not. The second case is a new application of Supervisory Control: it is shown that for a minimum phase plant, the d-step-ahead control law may be used to achieve asymptotic tracking of an arbitrary reference signal; in addition to the convolution bound, a crisp bound is found on the 1-norm of the tracking error when a disturbance is absent.