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| Optimal Control in Cold Rolling Hydraulic Automatic Position System Based on RBF Neural Network with Memory Factor |
| WEI Li-xin1,2, ZHENG Cui-hong1, LI Ying1, WANG Hong-rui1 |
1.Key Lab of Ind Computer Ctrl Eng of Hebei Province in Yanshan University, Qinhuangdao,Hebei 066004, China
2.National Eng Res Center for Equipment and Technology of Cold Strip Rolling,Qinhuangdao,Hebei 066004, China |
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Abstract Aiming at the characteristics for cold rolling hydraulic aotomatic positom control system with multi-variable, strong coupling, higher order and time-varying, a radial basis function neural network introduced in memory factor is proposed which can adaptive tune PID parameters online.To improve network accuracy, improved shuffled frog leaping algorithm is used to offline fully optimize radial basis fanction neural network with memory factor, which can obtain the network structure and initial parameters simultaneously, and avoid the tedious network model training. And the test functions are applied to demonstrate the optimized network has good approximation ability.Then the optimized radial basis fanction neural network with memory factor that has self-correction function is used to finely tune PID parameters online, and simulation results show that the control system with fast track, small overshoot, strong adaptability is better than the traditional PID control and general radial basis function neural network PID control methods, which has practical value.
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Received: 28 January 2014
Published: 10 December 2015
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2016, Vol. 37 
