Multiple-model switching control based on dynamic model bank

Multiple-model switching control(MMSC)based on a dynamic model bank is proposed to deal with discrete- time systems with bounded disturbance and parameter variations.An online learning algorithm is applied to build multiple models automatically,and optimize the model bank.At each sampling time,a model,which best matches the current dynamics of the system,is chosen;and the corresponding controller is applied to the system based on the switching index function with integral property.The closed-loop system stability is established;and the tracking error is proved to be asymptotically convergent.Simulation results confirm the validity of the proposed method.