Adaptive control using retrospective cost optimization with RLS-based estimation for concurrent Markov-parameter updating

We present a discrete-time adaptive control law that is effective for systems that are MIMO and either minimum phase or nonminimum phase. The adaptive control algorithm provides guidelines concerning the modeling information needed for implementation. This information includes a sufficient number of Markov parameters to capture the sign of the high-frequency gain as well as the nonminimum-phase zeros. No additional information about the poles or zeros need be known. In this paper, recursive least-squares estimation is used for concurrent Markov parameter estimation. We present numerical examples to illustrate the algorithm´s effectiveness in handling nonminimum-phase zeros as plant changes occur.