摘要
传统PID算法在控制具有大滞后、非线性、时变性等动态特性复杂的温度对象时,存在超调量大、参数无法自调节、模型自适应能力差、系统稳定性低等问题。为此,文章提出一种多重T-S型模糊神经网络PID温度控制算法。该算法根据PID算法的结构特点,利用T-S型模糊神经网络的单输出特性,建立能分别输出PID 3个参数的3重网络模型。MATLAB仿真实验结果表明,该算法与传统PID、BP神经网络PID,以及常规模糊神经网络PID等相比,超调量低,稳定性好,模型自适应性强,抗干扰能力强,综合性能指标好。
When the traditional PID algorithm controls temperature objects with complex dynamic characteristics such as large lag,nonlinearity,and time-varying,there are problems such as large overshoot,inability to self-adjust parameters,poor model adaptive ability,and low system stability.According to the structure characteristics of PID algorithm,using the single output characteristic of T-S type fuzzy neural network,this paper established a triple network model that can output three parameters of PID separately,and proposed multiple T-S type fuzzy neural network PID temperature control algorithm.In Matlab environment,simulation experiments were carried out and the results show that compared with traditional PID,BP neural network PID,and conventional fuzzy neural network PID,the algorithm in this paper has the lowest overshoot,the highest stability,the strongest model adaptability,the best anti-interference ability,and the best comprehensive performance index.
作者
张皓
涂雅培
高瑜翔
唐军
黄天赐
ZHANG Hao;TU Yapei;GAO Yuxiang;TANG Jun;HUANG Tianci(School of Intelligent Manufacturing and Information Engineering,Ya'an Polytechnic College,Ya'an 625100 China;College of Communication Engineering,Chengdu University of Information Technology,Chengdu 610225 China;School of Electronic Information and Artificial Intelligence,YiBin Vocational and Technical College,Yibin 644000 China)
出处
《西华大学学报（自然科学版）》
CAS
2023年第4期58-65,81,共9页
Journal of Xihua University:Natural Science Edition
基金
四川省教育厅高校创新团队项目(15TD0022)。
