Research on Optimal Control of Main Steam Temperature Based on Improved Neural Network PID
Aiming at the problems of complicated parameter tuning and poor self-adaptability of PID cascade control system for main steam temperature in power plant,an improved neural network PID cascade control method is proposed.In order to re-duce the uncertainty of the main steam temperature control system,the main neural network PID controller in the cascade con-trol is trained based on the minimum error entropy(MEE)criterion,and the entropy of the tracking error is estimated recur-sively by using the rolling time domain window method to improve the operation efficiency of the algorithm.The main steam temperature error sequence and some measurable disturbances are sent to the input layer of the neural network PID controller to achieve the integration of feedback control and feedforward control and improve the anti-interference ability of the control sys-tem.Compared with the neural network PID controller using the minimum sum of square error(MSE)criterion,the neural PID controller using MEE can reduce the fluctuation of superheated steam temperature and reduce the randomness of the con-trol system.
main steam temperatureneural network PIDminimum error entropy criterionminimum sum of square error cri-terionuncertaintyanti-interference
万方数据
维普
