Multiple models switching control based on recurrent neural networks |
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Authors: | Jun-Yong Zhai Shu-Min Fei Xiao-Hui Mo |
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Affiliation: | (1) Research Institute of Automation, Southeast University, Nanjing, 210096, China;(2) Department of Information Technology, Jinling Institute of Technology, Nanjing, 210001, China |
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Abstract: | This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method. |
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Keywords: | Multiple models Switching control Recurrent neural networks Nonlinear systems |
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