對(duì)于復(fù)雜的離散時(shí)間非線(xiàn)性系統(tǒng), 提出一種基于多模型的廣義預(yù)測(cè)控制方法. 通過(guò)在平衡點(diǎn)附近建立線(xiàn)性模型, 并用徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)來(lái)補(bǔ)償匹配誤差, 形成了非線(xiàn)性系統(tǒng)的多模型表示, 然后采用模糊識(shí)別方法作為切換法則, 并結(jié)合廣義預(yù)測(cè)控制構(gòu)成了多模型廣義預(yù)測(cè)控制器. 通過(guò)對(duì)連續(xù)發(fā)酵過(guò)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(xiàn)性系統(tǒng)；多模型；廣義預(yù)測(cè)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