Study on Adaptive Control with Neural Network Compensation |
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Authors: | SHAN Jian-feng HUANG Zhong-hua and CUI Zhan-zhong |
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Affiliation: | School of Mechatronics Engineering, Beijing Institute of Technology,Beijing 100081,China;School of Mechatronics Engineering, Beijing Institute of Technology,Beijing 100081,China;School of Mechatronics Engineering, Beijing Institute of Technology,Beijing 100081,China |
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Abstract: | A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed. |
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Keywords: | recurrent neural network neural network compensation general minimum variance control |
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