摘要
针对粉尘仪检定装置在粉尘均匀性控制过程中存在非线性、多扰动等问题,提出一种改进粒子群优化(IPSO)算法的PID控制器参数优化策略。首先,对标准PSO算法添加随迭代次数逐渐递减的权重系数w;然后,引入余弦函数以及指数函数动态调整学习因子,保证了粒子在搜索最优解过程中的灵活性和学习能力,提高了算法的收敛速度和自适应能力;最后,利用IPSO算法设计一种IPSO-PID控制器。实验结果表明:经过IPSO算法优化后的PID控制器与传统PID控制器以及PSO优化后的PID控制器相比,具有响应速度快、超调量小、抗干扰性能以及自适应能力强等优点。
Aiming at the problems of nonlinearity and multiple disturbances in dust uniformity control process of dust meter verification device,a PID controller parameter optimization strategy based on improved particle swarm optimization(IPSO)algorithm is proposed.Firstly,this method adds weight coefficientsω,which gradually decrease with the number of iterations to the standard PSO algorithm.And then,cosine function and exponential function are introduced to dynamically adjust the learning factor,ensuring the flexibility and learning ability of the particles in the process of searching for the optimal solution,and improving convergence speed and self-adaptive ability of the algorithm.Finally,IPSO algorithm is used to design an IPSO-PID controller.The experimental results show that the PID controller optimized by the IPSO algorithm has the advantages of fast response speed,small overshoot,strong anti-interference performance and adaptive ability compared with the traditional PID controller and the PID controller optimized by PSO.
作者
史经灿
唐守锋
赵志伟
周楠
仝光明
翟少奇
SHI Jingcan;TANG Shoufeng;ZHAO Zhiwei;ZHOU Nan;TONG Guangming;ZHAI Shaoqi(School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China)
出处
《传感器与微系统》
CSCD
北大核心
2023年第7期70-73,共4页
Transducer and Microsystem Technologies
基金
国家重点研发计划资助项目(2017YFF0205500)。
关键词
粉尘仪检定装置
粉尘浓度均匀性
改进粒子群优化算法
PID控制
dust meter verification device
uniformity of dust concentration
improved particle swarm optimization(IPSO)algorithm
PID control
