基于多模型的非線性系統(tǒng)廣義預(yù)測控制

對于復(fù)雜的離散時(shí)間非線性系統(tǒng), 提出一種基于多模型的廣義預(yù)測控制方法. 通過在平衡點(diǎn)附近建立線性模型, 并用徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)來補(bǔ)償匹配誤差, 形成了非線性系統(tǒng)的多模型表示, 然后采用模糊識別方法作為切換法則, 并結(jié)合廣義預(yù)測控制構(gòu)成了多模型廣義預(yù)測控制器. 通過對連續(xù)發(fā)酵過程的計(jì)算機(jī)仿真,表明了該方法的有效性.
關(guān)鍵詞: 非線性系統(tǒng)；多模型；廣義預(yù)測控制；徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)
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