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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 6023892, 12 pages
http://dx.doi.org/10.1155/2016/6023892
Research Article

Multiple Model Adaptive Tracking Control Based on Adaptive Dynamic Programming

1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
3School of International Studies, Communication University of China (CUC), Beijing 100024, China

Received 25 December 2015; Accepted 17 February 2016

Academic Editor: Filippo Cacace

Copyright © 2016 Kang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Adaptive dynamic programming (ADP) has been tested as an effective method for optimal control of nonlinear system. However, as the structure of ADP requires control input to satisfy the initial admissible control condition, the control performance may be deteriorated due to abrupt parameter change or system failure. In this paper, we introduce the multiple models idea into ADP, multiple subcontrollers run in parallel to supply multiple initial conditions for different environments, and a switching index is set up to decide the appropriate initial conditions for current system. By taking this strategy, the proposed multiple model ADP achieves optimal control for system with jumping parameters. The convergence of multiple model adaptive control based on ADP is proved and the simulation shows that the proposed method can improve the transient response of system effectively.