Neural adaptive chaotic control with constrained input using state and output feedback

doi:10.1088/1674-1056/24/1/010501

Abstract This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

Keywords: chaotic control neural adaptive control constrained input

Received: 16 May 2014
Revised: 28 June 2014
Accepted manuscript online:

PACS:

05.45.Gg

(Control of chaos, applications of chaos)

Fund: Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).

Corresponding Authors: Dong Hai-Rong
E-mail: hrdong@bjtu.edu.cn

Cite this article:

Gao Shi-Gen (高士根), Dong Hai-Rong (董海荣), Sun Xu-Bin (孙绪彬), Ning Bin (宁滨) Neural adaptive chaotic control with constrained input using state and output feedback 2015 Chin. Phys. B 24 010501

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Neural adaptive chaotic control with constrained input using state and output feedback

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