基于神经网络的PID自整定控制系统

摘要

The paper introduced a PID self-tuning control system which was established with neural network technology,gave structure of the system and analyzed structures and learning algorithms of BP neural network and RBF neural network in details. The system adopts three-layer BP neural network and its outputs are parameters of PID controller. It identifies controlled objects through variable-structure RBF neural network, provides gotten gradient information of output to input to BP neural network, and BP neural network optimizes parameters of PID controller according to the information. The simulation result showed that to nonlinear system whose parameter perturbation is larger, the system has fast convergence speed, strong ability of dynamic response, good stability and stronger robustness and adaptability.%文章介绍了一种应用神经网络技术建立的PID自整定控制系统,给出了系统结构,详细分析了BP神经网络和RBF神经网络的结构和学习算法.该系统采用3层BP神经网络,其输出为PID控制器的参数;通过变结构的RBF神经网络辨识控制对象,将得到的输出对输入的梯度信息提供给BP神经网络,BP神经网络根据该信息优化PID控制器参数.仿真结果表明,该系统对于参数扰动较大的非线性系统,其收敛速度快、动态响应能力强、稳定性好,且具有较强的鲁棒性和适应性.