Cumulative retrospective cost adaptive control with RLS-based optimization

We present a discrete-time adaptive control algorithm that is effective for multi-input, multi-output systems that are either minimum phase or nonminimum phase. The adaptive control algorithm requires limited model information, specifically, the first nonzero Markov parameter and the nonminimum-phase zeros of the transfer function from the control signal to the performance measurement. Furthermore, the adaptive control algorithm is effective for stabilization as well as command following and disturbance rejection, where the command and disturbance spectrum is unknown. The novel aspect of this adaptive controller is the use of a retrospective performance function which is optimized using a recursive least-squares algorithm.