Identification and control of continuous-time nonlinear systems via dynamic neural networks
Ren, X.M.
Rad, A.B.
Chan, P.T.
Wai Lun Lo
Dept. of Electr. Eng., Hong Kong Polytech. Univ., China;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: June 2003
Volume: 50,
Issue: 3
On page(s): 478- 486
ISSN: 0278-0046
INSPEC Accession Number: 7653544
Digital Object Identifier: 10.1109/TIE.2003.812350
Current Version Published: 2003-06-05
Abstract
In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.
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