摘要
传统PID控制器存在控制参数无法在线调整、控制效果差等问题。为了解决这些问题,本文提出了一款基于改进型模糊神经网络的智能PID控制器。该控制器不仅融合了模糊控制的推理能力和神经网络的学习能力,还创造性地将模糊规则参数化,使模糊规则也可以在线调整,进而提高了控制的准确性。同时,通过建构新型激活函数——IThLU函数,有效地避免梯度消失及梯度爆炸现象的发生,提高了控制的响应性。最终的仿真实验结果表明:这种改进型模糊神经网络智能PID控制器可以实现控制参数的在线实时调整,提高系统的响应性、稳定性和准确性,是对PID控制算法的有效改进。
Traditional PID controllers have problems of the inability to adjust online control parameters,poor control effects,etc.so this paper proposes an intelligent PID controller based on an improved fuzzy neural network.The controller not only combines the reasoning ability of fuzzy control and the learning ability of neural network,but also creatively parameterizes the fuzzy rules so that the fuzzy rules can also be adjusted online,thereby improving the accuracy of control.At the same time,by constructing a new type of activation function—IThLU function,it can effectively avoid the occurrence of gradient disappearance and gradient explosion,and improve the responsiveness of control.The final simulation experiment results show that the intelligent PID controller of improved fuzzy neural network can realize online real-time adjustment of control parameters,improve the responsiveness,stability and accuracy of the system,and is an effective improvement to the PID control algorithm.
作者
吕晓丹
吴次南
LYU Xiaodan;WU Cinan(College of Big Data and Information Engineering,Guizhou University,Guiyang 550000,China)
出处
《数据采集与处理》
CSCD
北大核心
2021年第2期365-373,共9页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(11264007)资助项目。
作者简介
吕晓丹,女,硕士研究生,研究方向:机器学习、智能控制、医疗机器人等;通信作者:吴次南(1962-),男,教授,研究方向:光电子学、光谱学、理论物理等,E-mail:cnwu@gzu.edu.cn。
