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A neural network model reference adaptive control for the nonlinear system with unavailable outputs |
Zeng Cheng; Zhao Baojun; He Peikun |
Dept. of Electronic Engineering Beijing Institute of Technology Beijing 100081 China |
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Abstract In this paper, by introducing an extended neural network model which can be easily identified on-line, a neural network model reference adaptive control method based on a feedforward-feedback structure is proposed for a class of nonlinear systems whose outputs are not measurable. A training algorithm with global convergence is offered, and the stability of the control system is analyzed. The simulation results show that this method is effective, anrl it has good robustness for both the selection of original network weights and the disturbance of plant parameters.
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Received: 26 October 2001
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