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 nonlinear 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.
Neural network Multi-step prediction Non-linear system Inverse dynamic control
Song Yongxian Zhang Hanxia Gong Chenglong He Naibao
The Institute of Electronic Engineering, Huaihai Institute of Technology, Lianyungang ,Jiangsu, 2220 The Institute of Electronic Engineering, Huaihai Institute of Technology, Lianyungang,Jiangsu, 22200
国际会议
长沙
英文
1979-1982
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）