PID神经网络在电炉控制系统中的应用

Application of PID Neural Network in Flectric Cooker Controlling Systems

常规PID控制器以其算法简单、可靠性高等优点，在工业生产得到了广泛应用。但是，PID控制器存在控制参数不易在线实时整定、难于对复杂对象进行有效控制等不足。利用神经网络自学习、自适应和非线性映射等特点，将神经网络和PID控制相结合，形成一种PID神经网终挖制系统，可对工业中使用的具有大时滞、慢时变、非线性特点的电炉系统进行有效辨识与控制。

General PID controller, because its algorithm is simple and high reliabihty,so has been widely used in industrial production. However, PID controller, there is not easy to line real-time control parameter tuning, is difficult for complex objects such as lack of effective control. Using neural network self-learning, adaptive and nonlinear mapping characteristics of neural network and PID control combmed to form a PID neural network control system can be used in industry with a large time lag, slow time-varying, nonlinear characteristics of electric systems for effective identification and control.