Control design for arbitrary complex nonlinear discrete-time systems based on direct NNMRAC strategy |
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Authors: | Shengquan Li Juan LiJinhao Qiu Hongli JiKongJun Zhu |
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Affiliation: | a The Key Laboratory of Education Ministry on Aircraft Structural Mechanics and Control, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China b School of Automation, Southeast University, Nanjing 210096, China |
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Abstract: | A novel scheme of neural network model reference adaptive control is proposed for arbitrary complex nonlinear discrete-time systems, i.e., non-minimum phase system, time-delay system and minimum phase system. An improved nearest neighbor clustering algorithm using an optimization strategy is introduced as the on-line learning algorithm to regulate the parameters of the RBFNN, which can simplify the neural network structure and accelerate the convergence speed. The clustering radius can be regulated automatically to guarantee the rationality of radius. Through constructing the pseudo-plant, the direct NNMRAC is also effective to the nonlinear non-minimum phase system. With the help of simulations, the control strategy based on direct RBFNN model reference adaptive control can not only make the multi-dimension nonlinear plants track multi-dimension reference signals quickly, but also endow the control systems with satisfying robustness. |
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Keywords: | RBFNN Nonlinear non-minimum phase system Nearest neighbor clustering algorithm Pseudo-system Direct model reference adaptive control |
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