Nonlinear System Control Based on Multi-step Predicted and Neural Network Inverse

A multi-layer forward neural network acted as the inverse controller, which was trained with predictive optimization algorithm to compensate for disturbances and uncertain plant nonlinearities, and reverse control based on neural network is implemented in complicated non-linear system. The weights o...

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Bibliographic Details
Published in2010 International Conference on Intelligent Computation Technology and Automation Vol. 2; pp. 809 - 812
Main Authors Song Yongxian, Zhang Hanxia, Gong Chenglong, He Naibao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2010
Subjects
Control nonlinearities
Control systems
Inverse dynamic control
Multi-layer neural network
Multi-step prediction
Neural network
Neural networks
Non-linear system
Nonlinear control systems
Nonlinear dynamical systems
Nonlinear systems
Prediction algorithms
Predictive control
Predictive models
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Summary:A multi-layer forward neural network acted as the inverse controller, which was trained with predictive optimization algorithm to compensate for disturbances and uncertain plant nonlinearities, and reverse control based on neural network is implemented in complicated non-linear system. The weights of neural network inverse control were trained by multi-step predictive index function, thereby the system has the character of predictive control. The method has faster dynamic speed than general neural network inverse control, and has better performance of the response. The simulation results have shown the effectiveness of this method.
ISBN:9781424472796
1424472792
DOI:10.1109/ICICTA.2010.821