基于PSO算法和BP神经网络的PID控制研究

Research of PID Control Based on BP Neural Network and PSO Algorithm

针对PID控制中的参数整定的难点及基本BP算法收敛速度慢、易陷入局部极值的问题,提出利用PSO算法的全局寻优能力和较强的收敛性来改进BP网络的权值调整新方法,从而对PID控制的比例、积分、微分进行优化控制。该方法是在基本BP算法的误差反向传播的基础上,使粒子位置的更新对应BP网络的权值和阈值的调整,既充分利用了PSO算法的全局寻优性又较好地保持了BP算法本身的反向传播特点。仿真结果表明基于PSO算法的BP神经网络的PID优化控制具有较好的性能和自学习、自适应性。

In view of the difficulty of parameters setting of PID control and the limitations of slow convergence and local extreme values of BP algorithm,a new method to adjust weights of BP network is proposed using the global optimization ability and the strong convergence by PSO algorithm,so as to optimize the proportional,integral and differential of PID control. The new algorithm is based on the weight adjustments of error back propagation of BP algorithm,making the bats position updating to weight and threshold of BP network modification. The new algorithm can not only use the global optimization of PSO algorithm,but also contain the feature of error back propagation of BP algorithm. Experimental results show that the PID optimization control based on BP neural network has better performance and self learning and adaptive.