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TECHNICAL PAPERS

Adaptive H Control Using Backstepping Design and Neural Networks

[+] Author and Article Information
Yugang Niu, Xingyu Wang

School of Information Science and Engineering,  East China University of Science and Technology, Shanghai, 200237, People's Republic of China

James Lam

Department of Mechanical Engineering,  University of Hong Kong, Pokfulam Road, Hong Kong

Daniel W. Ho

Department of Mathematics,  City University of Hong Kong, Tat Chee Avenue, Hong Kong

J. Dyn. Sys., Meas., Control 127(3), 478-485 (Jun 24, 2004) (8 pages) doi:10.1115/1.1978905 History: Received March 27, 2003; Revised June 24, 2004

In this paper, the adaptive H control problem based on the neural network technique is studied for a class of strict-feedback nonlinear systems with mismatching nonlinear uncertainties that may not be linearly parametrized. By combining the backstepping technique with H control design, an adaptive neural controller is synthesized to attenuate the effect of approximation errors and guarantee an H tracking performance for the closed-loop system. In this work, the structural property of the system is utilized to synthesize the controller such that the singularity problem of the controller usually encountered in feedback linearization design is avoided. A numerical simulation illustrating the H control performance of the closed-loop system is provided.

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Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

y (- -) and yd (—), k1=k2=2

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Figure 2

Tracking error z1, k1=k2=2

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Figure 3

Control signal u(t), k1=k2=2

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Figure 4

y (- -) and yd (—), k1=k2=51

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Figure 5

Tracking error z1, k1=k2=51

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Figure 6

Control signal u(t), k1=k2=51

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Figure 7

Ŵ11(—) and V̂11(- -), k1=k2=2

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Figure 8

Ŵ21(—) and V̂21 (- -), k1=k2=2

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