Discrete Time System Multi-model Adaptive Control Based on Localization Method

By using `localization´ method, an improved algorithm of discrete time system multi-model adaptive control (MMAC) is given in this paper. The system to be controlled can be a deterministic system (noise free) or a stochastic system with bounded random disturbances, and the parameters of the system are unknown. Multiple models of the system are set up to cover the uncertainties of the system dynamics. Every sample time, by using localization method, only a few of models which are closer to the `true´ model of the system are selected, and a multi-model adaptive controller is formed based on the selected models, so the big computation burden of the multiple model algorithm is greatly reduced. It is proved that the closed loop system is stable when multi-model controller is used to a linear time-invariant system, and simulation results show the effectiveness of the proposed method