摘要
工业用电加热炉作为一类大惯性、大时滞和参数时变的强非线性系统,其温度控制问题一直工业过程控制中的难题。提出一种新型的基于TSK模糊理论的模糊神经网络PID控制器,采用实数编码混沌量子遗传算法优化模糊神经网络的隶属函数参数和模糊TSK增益,具有较快的收敛速度和更强的优化能力。分析加热炉温度控制系统的原理和结构,阐述基于TSK模糊理论的模糊神经网络PID控制器的设计过程以及实数编码量子遗传算法的实现流程。通过工业用电加热炉的温度控制仿真和试验,验证了所提出的算法具有更好的动态性能、更高的稳态精度和更强的抗干扰能力。
As a class of great inertia,large delay,parameter time-varying strong nonlinear system,industrial electric heating furnace temperature control has been a problem of industrial process control. A novel fuzzy neural network PID controller based rISK fuzzy theory is proposed,which uses real-coded chaotic quantum genetic algorithm （RCQGA） to optimize fuzzy neural network membership function parameters and fuzzy TSK gain,has a faster convergence speed and more optimization capabilities. The principle and structure of furnace temperature control system is analyzed. The implementation process of fuzzy neural network PID controller design and the real-coded quantum genetic algorithm is illustrated. Through the simulation and experiment of industrial electric, heating furnace temperature control,the better dynamic performance,higher steady precision and stronger anti-interference ability of the proposed algorithm has been verified.
出处
《山东电力技术》
2014年第2期37-41,共5页
Shandong Electric Power
关键词
加热炉
模糊神经网络
遗传算法
温度控制
PLC
heating furnace
fuzzy neural network
genetic algorithm
temperature control
PLC
