Model predictive control algorithm with iterative learning compensation for disturbances

摘要

An algorithm of model predictive control with iterative learning compensation was proposed for unknown state and output disturbances in repeatable process control. Within the framework of model predictive control, the algorithm utilizes model prediction errors from previous runs to compensate system model disturbance, reduces the effects of unknown disturbances with prediction model and improves the control performance of repeatable process. The convergence and robustness of the algorithm are analyzed. The effectiveness of proposed scheme is illustrated by simulation results.