Multi-model adaptive control for discrete time systems

A multi-model adaptive control approach is proposed to improve the performance of adaptive control of discrete time invariant plant or time variant plant with jumping parameters and bounded disturbances. Multiple models of the plant are set up to cover the uncertainties of the plant dynamics. The localization method is used to reduce the computation burden of the multiple model algorithm. It is proved that the closed loop system is stable when multi-model pole assignment controller is used to a linear time-invariant plant with unknown parameters. The simulation results are given to show the usefulness of the proposed method.