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Research of Velocity Control Based on Genetic Algorithm Training RBF Neural Network

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2 Author(s)
Min Ke ; Dept. of Mech. Eng., Zhejiang Univ., Hangzhou, China ; Ying, Ji

Ram velocity tracking control is an important process in injection molding control. Due to the nonlinearity of the injection system and the fluctuation of the system parameters during the process, traditional PID controller can't satisfy the requirement of precision injection. A method of utilizing RBF neural network to adjust PID control parameters is presented, which conquers the deficiency of traditional PID controller. Genetic algorithm is used to optimize the centers and widths of hidden layer and the weights between hidden layer and output layer of RBF neural network. Gradient descent method is used to adjust the PID controller parameters. Simulations are provided to evaluate the performance of the proposed injection velocity control system.

Published in:

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on  (Volume:2 )

Date of Conference:

Nov. 30 2009-Dec. 1 2009

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