Constrained Robust Model Predictive Control Of Discrete System With Uncertaint Disturbance Case

Model predictive control(MPC) , also known as receding horizon control(RHC),is a class of model-based control theories that use linear or nonlinear process models to forecast system behavior. It has been applied in industries widely and became a popular method ,which is due to the facts that they can offer good performance, be understood and formulated easily, robustness to the model uncertainty and accommodate input/output process constraints. The success of the MPC control performance depend on the accuracy of the open-loop prediction ,which in turn depend on the accuracy of the process models. but there usually occur the uncertainty in the process of simplify .In addition, the outer conditions may also affect the model accuracy. In order to assure the control performances, the model uncertainty must be considered in the course of controller designed. In this paper. the research is focused on the problem The main contents of this thesis are stated as follows: 1. The dissertation first gave a systematic summary on the development of Model Predictive Control and the mainly character, analysed the problem which is the present research may faced in the robust predictive control, and gave a classfy of the familiar uncertainty or disturbance. 2. A summarized was made that the robust model predictive control methods based on the linear matrix inequalities and an on-line and an off-line calculate theories was made that aimed at multi-model uncertain system. the result indicated that it guaranteed a good performance. then gave an robust model predictive control design method based observer which has the similar performances with the state feedback system. 3. According to structure uncertain plant ,correlative robust model constraint predictive arithmetic is presented in this paper. the paper discussed the feedback condition with uncertainty, The simulation resulted that the control scheme is efficient.