Multi-Model Predictive Control for Nonlinear Systems Based on Mixed Logic
基于混合逻辑的非线性系统多模型预测控制

Big prediction errors are brought into being as the local linear model is used to predict the future output m the model prediction process for the existent multi-model predictive control algorithms. To solve this problem, this paper introduces causality relationship between multi-model of nonlinear process and output prediction into model predictive control framework in the term of constraint conditions, so that the nonlinear process can be described by a mixed-logic dynamic model. This paper also introduces switch rules into the multi-model predictive controller as a kind of pre- experiential knowledge. This new mixed logic dynamic model can characterize the nonlinear process entirely, thus solving the problem of model prediction and model switch for multi-model constrained nonlinear predictive control.