基于单神经元模糊自适应PID控制的加热炉温度控制器设计

Design for Temperature Controller of Industrial Furnace Based on Single Neuron Fuzzy Self-adaptive PID Control

加热炉温度控制器要求精度高、动态性能好、抗干扰能力强、检测功能全,而常规的PID控制策略难以满足要求。为此,提出一种单神经元模糊自适应PID控制算法,为提高系统的控温精度和响应快速性,将模糊控制引入单神经元自适应PID控制中,提出单神经元模糊PID控制。它运用有监督的Hebb学习规则在线修正PID参数,而神经元的比例、积分、微分学习速率则由Sugeno模糊逻辑系统根据系统的误差和误差变化量大小进行调整,使控制系统对动态过程信息的利用程度达到最优。Matlab仿真和实验结果表明,系统不仅具有自学习、自适应能力和鲁棒性,且动态性能和稳态性能都优于经典PID控制,超调量减小,上升时间和调节时间均减小。

Temperature controller of industrial furnace requires precise temperature,good dynamic capability,strong anti - jamming capability and various supervising functions. But the control strategy of ordinary PID can not meet these requirements,then control strategy of single neuron fuzzy self - adaptive PID is presented. In order to improve the precision and response speed, fuzzy control is introduced into single neuron PID control to form single neuron fuzzy PID control, which uses the supervising Hebb learning rules to adjust PID parameters. The learning velocity of parameter of single neuron is adjusted based on Sugeno fuzzy logic according to error and error variation,and it can improve the application of the system dynamic process message. The results of simulation and experiment indicate that temperature controller has the abilities of self - study, self adapting,robustness and dynamic perforrnance.