Self-Tuning PID Controller Based on Improved BP Neural Network
In order to solve the difficult problem that how to reduce the overshot and shorten the regulating time of the PID controller based on BP neural network,a self-tuning PID controller based on improved BP neural network is presented. The parameters of the PID controller are calculated by an improved BP Neural Network according to the input and output and the error of the PID controller.It is introduced the dynamic adjustment for activation function in the output layer, and the dynamic adjustment for learning rate to improve the Fletcher-Reeves conjugate gradient method. In the simulation experiments in the Matlab 7.0, two plants are selected to test the performance of the proposed PID controller,and also the rand noise are added to the input to test the robustness of them. From the simulation results,the overshoot are lower than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method;the regulating time is also shorter than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method;the proposed control algorithm is more robust than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method.
PID controller Improved Fletcher-Reeves conjugate gradient method self-tuning BP neural network
KAN Jiangming LIU Jinhao
Automation Department,Beijing Forestry University Beijing, P.R.China Transportation Department Beijing Forestry University Beijing, P.R.China
国际会议
长沙
英文
95-98
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）