On-Line Learning Control for Discrete Nonlinear Systems Via an Improved ADDHP Method

Authors:
Huaguang Zhang;Qinglai Wei;Derong Liu
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, People's Republic of China and Key Laboratory of Process Industry Automation, Ministry of Educat ...;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, People's Republic of China;Department of Electrical and Computer Engineering University of Illinois at Chicago 60607-7053 Chicago, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

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Abstract

This paper mainly discusses a generic scheme for on-line adaptive critic design for nonlinear system based on neural dynamic programming (NDP), more exactly, an improved action-depended dual heuristic dynamic programming (ADDHP) method. The principal merit of the proposed method is to avoid the model neural network which predicts the state of next time step, and only use current and previous states in the method, as makes the algorithm more suitable for real-time or on-line application for process control. In this paper, convergence proof of the method will also be given to guarantee the control to reach the optimal. At last, simulation result verifies the performance.