A real-time adaptive control algorithm using neural nets with perturbation |
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Authors: | Yang Jian-gang Wang Kai Yang Hua-yong Zhang Jian-min |
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Affiliation: | (1) institute of Artificial Intelligence, Dept. of Computer Science, Yuquan Campus of Zhejiang University, 310027 Hangzhou, China;(2) The State Key Laboratory of Fluid Power Transmission and Control, Yuquan Campus of Zhejiang University, 310027 Hangzhou, China |
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Abstract: | This paper proposes an adaptive algorithm of neural nets with a special perturbation for a real time velocity control system of a VVVF (Variable Voltage Variable Frequency) hydraulic elevator. The weight vector of the neural network is adaptively adjusted by the LMS (Least Mean Square) with perturbation, so it is not necessary to know the nonlinear continuous function of the control system. The nonlinear velocity control system is considered as the controller output function in an adaptive controller model. The experimental results obtained from the VVVF hydraulic elevator showed that the neural nets controller using the perturbation algorithm proposed are much stabler and faster in dynamic response compared with the conventional PID (Proportion-Integration-Derivation) controller. Project (69775013) Supported by NSFC |
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Keywords: | neural nets real-time control VVVF hydraulic elevator |
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