摘要
由于分数阶PID相较于整数阶PID具有更多的参数自由度,更广的控制范围,以及更强的控制性能等优势,本文提出一种基于分数阶PID的多模型神经网络控制器。将已整定好控制参数的多个分数阶PID控制器的输入输出数据进行采集,并经过预处理后,利用神经网络强大的学习能力和泛化能力进行多模型训练,并将训练好的网络作为控制器。MABLAB仿真实验表明:在进行多模型控制时,该控制器的超调量为0,最长调节时间、上升时间、延迟时间分别为430.99 s、259.50 s、90.76 s,不仅兼具了多个分数阶PID的模型控制能力,而且比每个分数阶PID控制器都具有更优越的性能指标。
Since fractional-order PID has more parameter degrees of freedom,wider control range,and stronger control performance than integer-order PID,this paper proposes a multi-model neural network controller based on fractional-order PID.The input and output data of multiple fractional-order PID controllers with tuned control parameters are collected and preprocessed,and multi-model training is performed using the powerful learning and generalization capabilities of neural networks,and the trained network is used as the controller.MATLAB simulation experiments show that when performing multi-model control,the overshoot of the controller is 0,and the longest adjustment time,rise time and delay time are 430.99 s,259.50 s and 90.76 s respectively.It not only has the model control capabilities of multiple fractional-order PIDs,but also has better performance indicators than each fractional-order PID controller.
作者
唐军
张皓
陈伟军
TANG Jun;ZHANG Hao;CHEN Weijun(School of Electronic Information and Artificial Intelligence,Yibin Vocational and Technical College,Yibin,Sichuan 644000,China;School of Intelligent Manufacturing and Information Engineering,Ya’an Polytechnic College,Ya’an,Sichuan 625100,China;School of Electronics and Electrical Engineering,Lingnan Normal University,Zhanjiang,Guangdong 524048,China)
出处
《计算技术与自动化》
2025年第2期99-105,共7页
Computing Technology and Automation
基金
宜宾职业技术学院科研项目(24ZRYB-08)。
