Application to GA-Based Fuzzy Control for Nonlinear Systems with Uncertainty

In this study, we strive to combine the advantages of fuzzy theory, genetic algorithms (GA), H tracking control schemes, smooth control and adaptive laws to design an adaptive fuzzy sliding model controller for the rapid and efficient stabilization of complex and nonlinear systems. First, we utilize a reference model and a fuzzy model (both involvingrules) to describe and well-approximate an uncertain, nonlinear plant. The FLC rules and the consequent parameter are decided on via GA. A boundary-layer function is introduced into these updated laws to cover modeling errors and to guarantee that the state errors converge into a specified error bound. After this, a H tracking problem is characterized. We solve an eigenvalue problem (EVP), and derive a modified adaptive neural network controller (MANNC) to simultaneously stabilize and control the system and achieve H control performance. Keywords-Fuzzy control, Genetic algorithm

• Type: Journal
• Year: 2009
• Where: ACIIDS
• Authors: Po-Chen Chen, Ken Yeh, Cheng-Wu Chen, Chen-Yuan Chen
• Keywords: ACIIDS 2009, Adaptive, Database, Fuzzy Sliding Model, Genetic Algorithm