基于改进递推预测误差神经网络算法的极点配置PID控制方法

针对工业控制中系统模型参数通常未知的特点，利用改进递推预测误差算法为基础的神经网络系统参数辨识方法，设计了极点配置自校正数字PID控制器.相比于基于梯度学习算法的神经网络辨识方法和通常的PID控制器，该方法具有参数辨识结构简单、神经元权值调整可持续且计算速度快、所采用的数字PID控制器鲁棒性强等优点.最后的数值仿真结果验证了本文算法及控制方法的有效性.

PID Control Based on Pole-assignment and Modified Recursive Prediction Error Algorithm for Neural Networks

Since the parameters in control system models are usually unknown in industrial applications, this paper tries to identify the system parameters by using the modified recursive prediction error algorithm for neural networks, and then design a self tuning PID controller via the pole-assignment method. Compared with the neural network identification based on the gradient learning algorithm and conventional PID, the method in this paper has simple structure of parameters, sustainable adjustment of neuron weights and quick calculation speed. Furthermore, this digital PID controller also enjoys good performance and easy application. And the simulation results verify that the effectiveness of this identification algorithm as well as the controller in this paper.