Application of RBF Neural Network in the Model-free Adaptive Control

作     者：Liu Bin.Author is with the Information and Control Engineering Department,Liaoning Shihua University,Liaoning province,Fushun,PR China Su Cheng-li.Author is with the Information and Control Engineering Department,Liaoning Shihua University,Liaoning province,Fushun,PR China Zhang Guang-hui.Author is with the Information and Control Engineering Department,Liaoning Shihua University,Liaoning province,Fushun,PR China Zhang Yong.Author is with the Instrument Management Workshop,Shenyang Oil and Gas Transmission Branch of PetroChina Company Limited,Liaoning province,Shenyang,PR China 

会议名称：《2011 Chinese Control and Decision Conference（CCDC）》

会议日期：2011年

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

基　　金：supported by the University Innovation Team in the Educational Department of Liaoning Province, China (2007T103,2009T062, LT2010058) the Liaoning Provincial Natural Science Foundation of China (20102127) 

关 键 词：RBF neural network non-parametric model adaptive control pseudo-partial derivative 

摘      要：To solve the impact of the unmodelled dynamics of the model process,model-free adaptive control based on RBF neural network is *** this algorithm nonlinear system is linearized by linearization of tight *** the system parameters are identified by the RBF neural network *** parameters are used to directly recursively compute model-free adaptive control *** controller is designed only by using I/O data of the controlled system,and no structural information or external testing signals are *** result shows that the proposed algorithm is an effective strategy with excellent tracking ability and strong robustness.