A new approach to model reference adaptive control

Abstract

In classical model reference adaptive control, the goal is to design a controller to make the closed-loop system act like a prespecified reference model in the face of significant plant uncertainty. Typically, the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that asymptotic matching is achieved. However, the controller is typically highly nonlinear, and the closed loop system can exhibit undesirable behavior, such as large transients or a large control signal, especially if the initial parameter estimates are poor. Furthermore, its ability to tolerate time-varying parameters is typically limited. Here, we propose an alternative approach. Rather than estimating the plant or parameters, instead we estimate what the control signal would be if the plant parameters and plant state were known and the ideal LTI compensator were applied. We end up with a linear periodic controller. Our assumptions are reasonably natural extensions of the classical ones to the time-varying setting; we allow rapid parameter variations, although we add a compactness requirement. We prove that we can obtain arbitrarily good tracking, explore the benefits and limitations of the approach, and provide a simulation study.