基于改进PSO和模糊RBF神经网络的退火炉温控制

Temperature control of annealing furnaces based on improved PSO and fuzzy RBF neural network

为提高对具有大滞后,强耦合的退火炉温度控制系统的控制精度,采用模糊径向基函数(RBF)神经网络控制炉温,并采用改进粒子群优化(PSO)算法进行优化。利用模糊推理过程与RBF神经网络所具有的函数等价性,统一系统函数。在利用改进PSO算法对模糊RBF神经网络进行训练时,先利用改进PSO算法得到模糊RBF神经网络的初始权值和阀值,然后对其进行二次优化得到最终的权值和阀值。仿真结果表明:该文方法降低了超调量,缩短了响应时间,稳态误差很小,能够拟合参考模型的输出,控制效果明显优于常规PID控制。

In order to improve the control accuracy of temperature control systems of annealing furnaces with large time delay and strong coupling, the temperature of annealing furnaces is controlled by a fuzzy radial basis function （ RBF） neural network and optimized by an improved particle swarm optimization（PSO） algorithm. The system functions are unified using the function e-quivalency of the fuzzy inference process and RBF neural network. The initial weights and thresholds of the fuzzy RBF neural network are obtained by the PSO algorithm, and the final weights and thresholds are obtained by quadratic optimization when the fuzzy RBF neural network is trained by the improved PSO algorithm. The simulation results show that the method proposed here decreases the overshoot,shortens the response time,and the steady state error is small,which can fit the outputs of the reference model and is better than common PID control in control effects.