線上基因演算之模糊類神經網路及其在非線性系統辨識與控制之應用(1/2)

Abstract

本計畫提出一種以基因演算為基礎輸出回授直接適應性模糊類神經控制器的設計法則，此控制器用以控制具未確定項之非線性動態系統。吾人使用一種reduced-form genetic algorithm (RGA)去調整模糊類神經控制器的權重因子，使得直接適應性模糊類神經控制器的權重因子可以基因演算方式線上調整。線上調整的適應函數是使用Lyapunov 設計方法推導。最後，加入監督式控制器確保控制系統的穩定性。

In this project, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned on-line via a GA approach. We use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. A new fitness function for on-line tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system.