BP神经网络模型预测控制算法的仿真研究

为克服被控对象参数变化导致控制精度降低的问题，研究了一种BP神经网络模型预测控制算法。借助最小二乘递推算法在线预测系统模型参数，利用BP神经网络在线预测PID参数以控制被控对象。该算法基于模型预测，首先在线性系统中验证其控制效果，然后将非线性问题作线性处理，采用BP神经网络模型预测PID控制器予以实现控制非线性系统。仿真曲线显示BP神经网络PID控制器用于线性系统可达到高精度控制要求；对于非线性系统有自适应及逼近任意函数的能力。仿真研究表明，该算法与传统BP神经网络PID控制器相比，其自适应能力更强，稳定性更好，控制精度更高。

Simulation on Predictive Control Algorithm Based on BP Neural Network Model

To overcome the problem of lower control precision caused by parameters varying of the controlled object,the paper proposed a sort of predictive control algorithm based on BP neural network model.In the paper,it applies the predictive parameter of PID controller based on BP neural network on line to control the controlled object,and the system model parameter was on line predicted by means of least recursive squares algorithm.The algorithm would be based on model prediction.It first validats its control eff...