Research Papers

Neural Network Direct Adaptive Control Strategy for a Class of Switched Nonlinear Systems

[+] Author and Article Information
Lei Yu

School of Automation,
Hangzhou Dianzi University,
Hangzhou 310018, China;
School of Mechanical and Electric Engineering,
Soochow University,
Suzhou 215021, China
e-mail: slender2008@163.com

Xiefu Jiang

School of Automation,
Hangzhou Dianzi University,
Hangzhou 310018, China

Shumin Fei

Key Laboratory of Measurement and
Control of Complex Systems of Engineering,
Ministry of Education,
Nanjing 210096, China

Jun Huang

School of Mechanical and
Electric Engineering,
Soochow University,
Suzhou 215021, China

Gang Yang

Digital Manufacture Technology
Key Laboratory of Jiangsu Province,
Huai'an 223003, China

Wei Qian

Henan Provincial Open Laboratory for
Control Engineering Key Discipline,
Jiaozuo 454000, China

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 11, 2013; final manuscript received March 15, 2016; published online May 17, 2016. Editor: Joseph Beaman.

J. Dyn. Sys., Meas., Control 138(8), 081001 (May 17, 2016) (7 pages) Paper No: DS-13-1195; doi: 10.1115/1.4033485 History: Received May 11, 2013; Revised March 15, 2016

This paper deals with the adaptive neural network (NN) switching control problem for a class of switched nonlinear systems. Radial basis function (RBF) NNs are utilized to approximate the unknown switching control law term which includes a neural network control term, a supervisory control term, and a compensation control term. Also, based on the average dwell-time, a direct adaptive neural switching controller is designed to heighten the robustness of switching system. We can prove to ensure stability of the resulting closed-loop system such that the output tracking performance can be well obtained and all the signals are kept bounded. Simulation results validate the tracking control performance and investigate the effectiveness of the proposed switching control method.

Copyright © 2016 by ASME
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Grahic Jump Location
Fig. 1

Tracking performance

Grahic Jump Location
Fig. 2

Tracking error performance




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