摘要
本文提出了基于PID神经网络和Smith预估器的电热炉温度控制器。PID神经网络将PID控制器与具有自学习功能的神经网络相结合,综合了PID和神经网络控制各自的优点,改善参数时变对系统性能的影响。PIDNN-Smith控制器加入Smith预估器解决了加热炉的滞后影响,使控制器对加热炉的温度控制有了更好的效果。利用MATLAB软件对温控系统进行了仿真测试,并对测试结果进行了分析。
This paper proposes neural network and Smith predictor of electric heating furnace temperature controller based on PID. PIDNN combines PID controller with self-learning function of neural network, integrates the respective advantages of PID and neural network control, and improves the time-varying parameters effect of the system performance. PIDNN- Smith controller joins Smith predictor to solve the heating furnace of the delay effect, makes the controller to furnace temperature control has a better effect. Using MATLAB software to simulate the temperature control system, and the test results are analyzed.
出处
《自动化技术与应用》
2013年第4期10-13,共4页
Techniques of Automation and Applications
