以模糊神經網路為基礎的適應控制

Abstract

在本論文中,我們研究如何將模糊神經網路用在適應調節控制上.我們提出 一種適應控制的方法;在此方法中,我們設計一個模糊類神經控制器以及一 個鑑別器.我們以輸入輸出線性化的技巧討論在閉迴路中的模糊類神經控 制器的性能,並且用李亞普諾夫定理分析系統的收斂性.至於模糊類神經鑑 別器則用來提供動態系統的靈敏度來調整模糊類神經的參數.一些模擬結 果顯示模糊類神經在適應調節控制的應用有很好的效果. In this thesis, we investigate the possible application of fuzzy neural network to adaptive regulation control. We propose a control scheme including FNNC (fuzzy neural network controller) and FNNI (fuzzy neural network identifier) to adaptive regulation control. FNNC is discussed using input- ouput linearization on technique and is analyzed using Lyapunov theorem. FNNI is used for the backpropagation of errors through the plant to the controller.In particular, dynamic plant sensitivity is provided by the FNNI to adjust theparameters of FNNC. Several simulation results shows the merit of applying FNNto the adaptive control.