基于BP神经网络整定的PID控制

传统PID控制在控制系统中有广泛的应用，但是由于其在参数整定过程中对于对象模型过分依赖，并且参数一旦整定计算好后，在整个控制过程中都是固定不变的，而在实际系统中，由于系统状态和参数等发生变化时，过程中会出现状态和参数的不确定性，系统很难达到最佳的控制效果．为了改善传统PID控制的效果，又充分利用现有PID控制的研究成果，采用BP神经网络对PID参数进行整定，并对该系统进行了仿真分析．仿真结果表明，采用BP神经网络整定的PID控制较传统PID算法及BP网络算法都有较大程度的提高．

PID control based on BP neural network adjusting

Traditional PID control has been widely used in real applications. But for traditional PID control, mathematical model must exist when adjusting PID parameters, and the parameters are constant after adjusted. However, in real systems, when the system state and parameters change and become uncertain, system performance cannot keep in the ,best state. For the purpose of improving system performance of PID control and making use of the existing study fruit of PID control, BP neural network was used for adjusting PID parameters. After simulating, the results show that the algorithm based on BP neural network adjusting has better control characteristics than those of traditional PID and BP neural network.