Direct adaptive control for a class of nonlinear systems using multilayer neural networks

A new design scheme of direct adaptive neural network controller for a class of nonlinear systems with unknown function control gain is proposed in this paper. The design is based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs). By adopt...

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Bibliographic Details
Main Authors: Tianping Zhang, Qikuen Shen, Jiandong Mei, Yang Yi
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Adaptive systems
Control systems
Multi-layer neural network
Neural networks
Nonlinear control systems
Nonlinear systems
Programmable control
Sliding mode control
Upper bound
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Summary:A new design scheme of direct adaptive neural network controller for a class of nonlinear systems with unknown function control gain is proposed in this paper. The design is based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs). By adopting the adaptive compensation term of the upper bound function of the sum of residual and approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.
DOI:10.1109/ICARCV.2004.1468789