對于復雜的離散時間非線性系統, 提出一種基于多模型的廣義預測控制方法. 通過在平衡點附近建立線性模型, 并用徑向基函數神經網絡來補償匹配誤差, 形成了非線性系統的多模型表示, 然后采用模糊識別方法作為切換法則, 并結合廣義預測控制構成了多模型廣義預測控制器. 通過對連續發酵過程的計算機仿真,表明了該方法的有效性.
關鍵詞: 非線性系統；多模型；廣義預測控制；徑向基函數神經網絡
Abstract: A multiple model based generalized predictive control is provided for complex nonlinear discrete time system. The RBFNN, i.e. radial basis function neural network, is used to approximate the matching error of the local linear model, and the nonlinear system is modeled by the multiple linear model and neural network at different equilibrium operating point. A fuzzy recognized method and generalized predictive control algorithm are used to set
up the multi-model generalized predictive controller. From the simulation of continuous fermentation process, it can be seen that the controller proposed in this paper can give a better control performance for nonlinear system.
Key words: nonlinear system; multi-model; generalized predictive control; RBFNN