Observer Design Based on Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network

作     者：Xin Wen Xin Li 

作者机构：Department of Aeronautics and Astronautics Shenyang Aerospace University Shenyang 110136 China School Astronautics Nanjing University of Aeronautics and Astronautics Nanjing 210016 China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2016年第21卷第5期

页      面：544-551页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 

主　　题：Takagi-Sugeno-Kang （TSK） fuzzy model activation functions state observer nonlinear systems simulation 

摘      要：In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network（SRCPFWNN） for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network（SRWNN） to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang（TSK） model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory（DACT） and a back propagation-based learning algorithm, all parameters of the consequent parts are updated online in the SRCPFWNN. On this basis, we propose a design method using an adaptive state observer based on an SRCPFWNN for nonlinear systems. Using the Lyapunov function, we then prove the stability of this observer design method. Our simulation results confirm that the observer can accurately and quickly estimate the state values of the system.