An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

被引：121
|
作者：
Yang, Hongjun [1 ]
Liu, Jinkun [2 ]
机构：
[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
[2] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China
来源：
IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2018年 / 5卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Adaptive control; neural network (NN); nonlinear system; radial basis function; DISTURBANCE OBSERVER; MANIPULATOR; VEHICLE;
D O I：
10.1109/JAS.2017.7510820
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper focuses on designing an adaptive radial basis function neural network (RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives (CMD) system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
引用
收藏
页码：457 / 462
页数：6
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