Disturbance compensation incorporated in predictive control system using a repetitive learning approach

In this paper, a disturbance compensation scheme is incorporated into a predictive control scheme using a repetitive learning approach. It has the following contributions. First, based on the assumption of the presence of both state and output disturbances, a predictive control algorithm is derived. Secondly, to estimate the disturbances, two feedforward disturbance learning schemes are proposed. Thirdly, the rigid mathematic proof is given to guarantee the convergence of the tracking error under the proposed disturbance learning laws used in conjunction with the predictive controller formulated. Finally, simulation results are provided to illustrate the good performance achievable by the proposed control law.