A robust model reference adaptive control for non-minimum phase systems with unknown or time-varying delay

Abstract

In this paper, we present an indirect model reference adaptive control for SISO non-minimum phase systems with unknown or time-varying time delay. The parameter estimation scheme is a combined adaptive data filtering with a recursive least-squares algorithm with parameter projection and signal normalization. The problem of minimum phase of the plant is handled by an adaptive input–output data filtering. Hence, the zeros of the system-estimated model are relocated inside the unit circle. This estimated model, which is minimum phased, is then used for the control synthesis. It is shown that the adaptive input–output data filtering permits also to solve the difficult problem of model reference adaptive control in the case of unknown time-varying delay. The scheme robustness with respect to unmodelled dynamics and additive noise is also simultaneously improved. Finally, the results are illustrated by numerical examples.