基于模糊神经网络PID算法的电阻炉温控系统

由于电阻炉温控系统是一个大惯性、大滞后、时变、且非线性的系统,采用传统PID控制不能解决系统的非线性、时变和PID参数的在线整定难等问题,为此提出一种控制算法—模糊神经网络PID算法。可根据电阻炉的温度的偏差及其变化率实时对PID的3个参数进行优化,达到具有最佳组合的PID控制,从而实现PID控制的自适应和智能化性能。使用MatLab的simulink仿真,通过传统PID与模糊神经网络PID阶跃响应曲线的比较,表明系统采用模糊神经网络PID算法具有更好的动、静态特性和自适应性,对突加的外部的扰动具有良好的抗干扰能力,具有实用价值。

Resistance Furnace Temperature Control System Based On The Fuzzy Neural Network PID Algorithm

The resistance furnace temperature control system is a big inertia, big lag, time varying and nonlinear system, traditional PID control can＇t solve system of nonlinear, time-varying and PID parameters online setting difficult problem, therefore put forward a kind of fuzzy neural network control algorithm, PID algorithm. According to the deviation and its rate of change of resistance furnace temperature real-time three parameters of PID optimization, to achieve a best combination of PID control, so as to realize the adap- tive and intelligent of PID control performance. Using MatLab simulink simulation, through traditional PID and fuzzy neural net- work PID comparison of step response curve, show that system USES fuzzy neural network PID algorithm has better dynamic and static characteristic and adaptability, and external disturbance oftu and has good anti-interference ability, has the practical value.