基于PSO带式输送机PID控制器参数智能整定的适应度函数设计

doi:10.3901/JME.2023.22.444

机械工程学报 ›› 2023, Vol. 59 ›› Issue (22): 444-456.doi: 10.3901/JME.2023.22.444

扫码分享

基于PSO带式输送机PID控制器参数智能整定的适应度函数设计

吉建华^1,2,3, 苗长云^1,3, 李现国³, 刘意^1,3,4, 姬政海⁵

1. 天津工业大学机械工程学院天津 300387;
2. 天津仁爱学院信息工程系天津 301636;
3. 天津工业大学天津市光电检测技术与系统重点实验室天津 300387;
4. 天津工业大学工程教学实习训练中心天津 300387;
5. 国家能源集团乌海能源有限责任公司乌海 016000

收稿日期:2022-12-23 修回日期:2023-02-20 出版日期:2023-11-20 发布日期:2024-02-19
通讯作者: 苗长云(通信作者)，男，1962年出生，博士，教授，博士研究生导师。主要研究方向为现代通信网络与系统、光电检测技术、机械电子信息技术。E-mail：miaochangyun@163.com
作者简介:吉建华，男，1979年出生，博士，副教授。主要研究方向为信号分析与处理、控制系统理论与技术。E-mail：jjh3036@163.com
基金资助:
国家自然科学基金(51274150)、天津市重点研发计划科技支撑重点(18YFZCGX00930)和天津市重点研发计划成果转化接力(18YFJLCG00060)资助项目。

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

JI Jianhua^1,2,3, MIAO Changyun^1,3, LI Xianguo³, LIU Yi^1,3,4, JI Zhenghai⁵

1. School of Mechanical Engineering, Tiangong University, Tianjin 300387;
2. Department of Information Engineering, Tianjin Renai College, Tianjin 301636;
3. Tianjin Photoelectric Detection Technology and System Key Laboratory, Tiangong University, Tianjin 300387;
4. Center for Engineering Internship and Training, Tiangong University, Tianjin 300387;
5. Wuhai Energy Co., Ltd., National Energy Group, Wuhai 016000

Received:2022-12-23 Revised:2023-02-20 Online:2023-11-20 Published:2024-02-19

摘要/Abstract

摘要： 带式输送机是一种现代化生产中重要连续运输设备，为提高其速度控制性能，解决调速过程存在的平均加速度不合理，超调量和振荡大的问题，设计出一种变权综合型适应度函数。该适应度函数中的误差绝对值积分部分用于优化比例-积分-微分(Proportional-integral-derivative，PID)控制器的综合性能；加速度调节部分用于在符合现行带式输送机设计规范前提下设定平均加速度；超调量抑制部分用于实现被控对象的响应速度无超调和振荡；稳态误差抑制部分用于保证被控对象响应速度的稳态误差为零。仿真试验结果表明，采用基于变权综合型适应度函数的粒子群优化算法(Particle swarm optimization，PSO)智能整定PID控制器参数，实现带式输送机平稳调速，平均加速度符合带式输送机设计规范且可调，响应速度快，无超调、无振荡、零稳态误差，有效降低对带式输送机造成损坏，提高控制性能；其适应度函数的参数易于获取、PID控制器的初始参数可随机设置，参数整定过程简便，易于实现，利于工程应用，在煤炭、港口、电力、矿山、冶金、化工、农业等领域具有重要应用价值。

关键词: 带式输送机, 速度控制, 适应度函数, 比例-积分-微分控制器, 粒子群优化算法, 智能整定

Abstract: Belt conveyor is an important continuous transportation equipment in modern production. In order to improve its speed control performance and solve the problems of unreasonable average acceleration, large overshoot and oscillation in the speed regulation process, a variable weight comprehensive fitness function is designed. The fitness function consists of four parts：the part of integral absolute error(IAE) is responsible for optimizing the comprehensive control performance of the proportional-integral-derivative(PID) controller, the acceleration adjustment part to adjust the average acceleration on the premise of meeting the current design specification of the belt conveyor, the overshoot suppression part is used to realize the response speed of the controlled object without overshoot and oscillation; and the steady-state error suppression part is used to ensure that the steady-state error of the response speed of the controlled object is zero. The simulation results show that by using particle swarm optimization (PSO) algorithm intelligent tuning PID controller parameters based on variable weight comprehensive fitness function, the belt conveyor can realize smooth speed regulation, the average acceleration meets the belt conveyor design specification and is adjustable, the response speed is fast, there is no overshoot, oscillation and zero steady-state error. The damage to the belt conveyor is effectively reduced and the control performance is improved. The parameters of the fitness function are easy to obtain, the initial parameters of the PID controller can be set randomly, and the parameter setting process is simple and easy to implement, which is conducive to engineering application. The fitness function greatly improves the control performance of belt conveyor, and has certain theoretical significance and broad application value in coal, port, electric power, mining, metallurgy, chemical industry, agriculture and other fields.

Key words: belt conveyor, speed control, fitness function, proportional-integral-derivative controller, particle swarm optimization algorithm, intelligent tuning

中图分类号:

TH222
TP232

吉建华, 苗长云, 李现国, 刘意, 姬政海. 基于PSO带式输送机PID控制器参数智能整定的适应度函数设计[J]. 机械工程学报, 2023, 59(22): 444-456.

JI Jianhua, MIAO Changyun, LI Xianguo, LIU Yi, JI Zhenghai. Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO[J]. Journal of Mechanical Engineering, 2023, 59(22): 444-456.

参考文献

[1] 王毅颖,刘建功,刘扬. 煤矿井下电网有源电力滤波器接入位置和容量研究[J]. 煤炭学报, 2016, 41(12):3174-3180. WANG Yiying, LIU Jiangong, LIU Yang. Research on the allocation and sizing of active power filters in underground coal mine power system[J]. Journal of China Coal Society, 2016, 41(12):3174-3180.
[2] 杨小林,葛世荣,祖洪斌,等. 带式输送机永磁智能驱动系统及其控制策略[J]. 煤炭学报, 2020, 45(6):2116-2126. YANG Xiaolin, GE Shirong, ZU Hongbin, et al. Permanent magnet intelligent drive system and control strategy of belt conveyor[J]. Journal of China Coal Society, 2020, 45(6):2116-2126.
[3] 祝龙记,王汝琳. 直接转矩控制在带式输送机电气传动中的应用研究[J]. 煤炭学报, 2004, 29(3):376-380. ZHU Longji, WANG Rulin. Application of direct torque control in electric drive of belt conveyors[J]. Journal of China Coal Society, 2004, 29(3):376-380.
[4] 中国煤炭建设协会. 带式输送机工程设计规范:GB 50431-2008[S]. 北京:中国计划出版社, 2008. China Coal Construction Association. Code for design of belt conveyor engineering:GB 50431-2008[S]. Beijing:China Planning Press, 2008.
[5] 刘常海,胡敏,杨庆俊,等. 波浪能发电半物理仿真试验技术研究[J]. 机械工程学报, 2021, 57(10):286-296. LIU Changhai, HU Min, YANG Qingjun, et al. Hardware-in-the-loop simulation technology of wave power generation[J]. Journal of Mechanical Engineering, 2021, 57(10):286-296.
[6] 许景辉,王雷,谭小强,等. 基于SOA优化PID控制参数的智能灌溉控制策略研究[J]. 农业机械学报, 2020, 51(4):261-267. XU Jinghui, WANG Lei, TAN Xiaoqiang, et al. Application of PID control based on SOA optimization in intelligent irrigation system[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(4):261-267.
[7] 郝万君,强文义,胡林献,等. 基于改进粒子群算法的PID参数优化与仿真[J]. 控制工程, 2006, 13(5):429-432. HAO Wanjun, QIANG Wenyi, HU Linxian, et al. Optimization and simulation of PID parameters based on improved particle swarm algorithms[J]. Control Engineering of China, 2006, 13(5):429-432.
[8] 马平,潘洪洲,周宇华. 大惯性对象控制器参数寻优的目标函数研究[J]. 自动化仪表, 2011, 32(7):41-43. MA Ping, PAN Hongzhou, ZHOU Yuhua. Research on the objective function of optimal parameters for large inertia objects controller[J]. Process Automation Instrumentation, 2011, 32(7):41-43.
[9] 马鑫泰,何同祥. 基于改进粒子群算法的大迟延热工对象 PID参数优化[J]. 仪器仪表用户, 2019, 26(3):87-89. MA Xintai, HE Tongxiang. Optimization of PID parameters for large delay thermal engineering objects based on improved particle swarm optimization[J]. Instrumentation, 2019, 26(3):87-89.
[10] MUSTAFA N, HASHIM FH. Design of a predictive PID controller using particle swarm optimization[J]. International Journal of Electronics and Telecommunications, 2020, 66(4):737-743.
[11] 杨世勇,刘殿通. 一类大时滞对象控制器参数寻优目标函数仿真研究[J]. 烟台大学学报, 2020, 33(1):84-89. YANG Shiyong, LIU Diantong. Simulation study on objective function to optimize controller parameters for a class of long time-delay plants[J]. Journal of Yantai University, 2020, 33(1):84-89.
[12] 余向阳,朱咏,高春阳. 基于SICPSO算法的PIλDμ控制器参数整定[J]. 控制工程, 2020, 27(3):481-486. YU Xiangyang, ZHU Yong, GAO Chunyang. Tuning of fractional PIλDμ controller based on the SICPSO algorithm[J]. Control Engineering of China, 2020, 27(3):481-486.
[13] JI Jianhua, MIAO Changyun, LI Xianguo, et al. Research on speed control algorithm of belt conveyor based on controllable parameter PSO-PID[C]//7th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 2020.12.18- 2020.12.20.
[14] 牟乃夏,徐玉静,李洁,等. 遗传禁忌搜索算法收敛性和时间复杂度分析[J]. 河南理工大学学报, 2018, 37(4):118-122. MOU Naixia, XU Yujing, LI Jie, et al. Analyses of convergence and time complexity of genetic tabu search algorithm[J]. Journal of Henan Polytechnic University,2018, 37(4):118-122.
[15] 冯川放. 一种改进PSO和遗传算法相结合的企业生产配置算法[J]. 洛阳理工学院学报, 2016, 26(2):81-85, 89. FENG Chuanfang. Study on algorithm of production configuration for enterprises based on combination of improved PSO and genetic algorithm[J]. Journal of Luoyang Institute of Science and Technology, 2016, 26(2):81-85, 89.
[16] 朱旭辉,倪志伟,程美英. 变步长自适应的改进人工鱼群算法[J]. 计算机科学, 2015, 42(2):210-216, 246. ZHU zuhui, NI Zhiwei, CHENG Meiying. Self-adaptive improved artificial fish swarm algorithm with changing step[J]. Computer Science, 2015, 42(2):210-216, 246.
[17] 左姣姣,倪志伟,朱旭辉,等. 融合协同进化人工鱼群算法和SVM的雾霾预测方法[J]. 模式识别与人工智能, 2018, 31(8):725-739. ZUO Jiaojiao, NI Zhiwei, ZHU Xuhui, et al. Haze prediction method combining co-evolution artificial fish swarm algorithm and support vector machine[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(8):725-739.
[18] 刘金明,王新生,梁清梅. 基于遗传模拟退火算法的QoS组播路由算法[J]. 计算机工程, 2007, 33(9):212-215. LIU Jinming, WANG Xinsheng, LIANG Qingmei. Algorithm of QoS multicast routing based on genetic simulated annealing algorithm[J]. Computer Engineering, 2007, 33(9):212-215.
[19] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 27 November-1 December 1995, 1944:1942-1948.
[20] 顾涛,李苏建,林莹璐,等. 周期式退火炉作批处理机的可重入批离散机流水车间调度[J]. 机械工程学报, 2020, 56(2):220-232. GU Tao, LI Sujian, LIN Yinglu, et al. Research on the re-entrant batch discrete flow shop scheduling for periodic annealing furnace as batch processor[J]. Journal of Mechanical Engineering, 2020, 56(2):220-232.
[21] 谭顿,陶建峰,王旭永. 基于改进粒子群算法的双液压马达同步控制策略[J]. 机械工程学报, 2020, 56(16):254-261. TAN Dun, TAO Jianfeng, WANG Xuyong. Synchronous control strategy of dual hydraulic motors based on improved particle swarm optimization algorithm[J]. Journal of Mechanical Engineering, 2020, 56(16):254-261.
[22] SHIN JJ, BANG H. UAV Path planning under dynamic threats using an improved PSO algorithm[J]. International Journal of Aerospace Engineering, 2020:1-17.
[23] 谢延敏, 岳跃鹏, 冯美强, 等. 基于水平集理论和Kriging模型的镁合金差温成形伪拉延筋设计及优化[J]. 机械工程学报, 2020, 56(12):92-98. XIE Yanmin, YUE Yuepeng, FENG Meiqiang, et al. Design and optimization of pseudo drawbeads in nonisothermal stamping of magnesium alloys based on level set theory and kriging model[J]. Journal of Mechanical Engineering, 2020, 56(12):92-98.
[24] WU Tiezhou, ZHOU Cuicui, YAN Zhe, et al. Application of PID optimization control strategy based on particle swarm optimization (PSO) for battery charging system[J]. International Journal Of Low-carbon Technologies, 2020, 15(4):528-535.
[25] LI Qizhong, YUE Yizheng, WANG Zhongqi. Adoption of computer particle swarm optimization algorithm under thermodynamic motion mechanism[J]. Thermal Science, 2020, 24(5):2707-2715.
[26] REZAEI F, SAFAVI HR. GuASPSO:A new approach to hold a better exploration-exploitation balance in PSO algorithm[J]. Soft Computing, 2020, 24(7):4855-4875.
[27] PRADHAN R, MAJHI S K, PRADHAN J K, et al. Optimal fractional order PID controller design using ant lion optimizer[J]. Ain Shams Engineering Journal, 2020, 11(2):281-291.
[28] ROJAS J P, OCHOA G V, DUARTE FORERO J. Comparative performance of a hybrid renewable energy generation system with dynamic load demand[J]. Applied Sciences-basel, 2020, 10(9):1-24.
[29] CHEN Gonggui, LI Zhijun, ZHANG Zhizhong, et al. An improved ACO algorithm optimized fuzzy PID controller for load frequency control in multi area interconnected power systems[J]. IEEE Access, 2020, 8:6429-6447.
[30] 王晓伟,张丽香. 基于模糊PID控制的带式输送机节能系统的仿真研究[J]. 煤矿机械, 2015, 36(2):243-245. WANG Xiaowei, ZHANG Lixiang. Simulation research of belt conveyor of energy-saving system based on fuzzy PID control[J]. Coal Mine Machinery, 2015, 36(2):243-245.
[31] 魏立峰,李雨桐,杨私,等. 多模型内模控制的湿化仪温控系统研究[J]. 电子器件, 2020, 43(3):667-671. WEI Lifeng, LI Yutong, YANG Si, et al. Research on temperature control system of humidifier controlled by multi model internal model[J]. Chinese Journal of Electron Devices, 2020, 43(3):667-671.
[32] 钟婧佳,杨功流,王汀,等. 惯性稳定平台的扰动观测器/不完全微分PID复合控制[J]. 导弹与航天运载技术, 2020, 373:72-77. ZHONG Jingjia, YANG Gongliu, WANG Ting, et al. Disturbance observer/incomplete differential PID compound control for inertial stabilized platform[J]. Missiles And Space Vehicles, 2020, 373:72-77.

基于PSO带式输送机PID控制器参数智能整定的适应度函数设计

Design of Fitness Function for Intelligent Parameter Tuning of PID Controller on Belt Conveyor with PSO

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	原浩, 赵希梅. 一种双轴直驱平台新型同步渐近跟踪控制设计[J]. 机械工程学报, 2023, 59(5): 271-279.
[2]	李毅超, 王楠, 段彬, 康永哲, 张承慧. 基于深度学习和粒子群算法的锂离子电池核温估计方法[J]. 机械工程学报, 2023, 59(22): 69-78.
[3]	伍仰金, 郭茜婷, 郑传良, 郑峰, 潘郑楠, 张江云. 考虑储能共享的风光储集群联合优化运行及竞标策略^*[J]. 电气工程学报, 2023, 18(1): 219-227.
[4]	张磊, 郝建伟, 鲍久圣, 刘彤彦, 阴妍, 戴金昊, 牛振勇. 全永磁驱动带式输送机控制策略及动力学行为[J]. 机械工程学报, 2022, 58(21): 134-147.
[5]	范大莽, 关广丰, 熊伟, 王海涛. 考虑伺服阀零偏补偿的电液振动台随机振动模型参考滑模控制策略[J]. 机械工程学报, 2022, 58(13): 119-128.
[6]	蔡鑫祥, 撖奥洋, 周生奇, 菅学辉, 张智晟. 基于改进Bagging算法与模糊MP-LSTM融合的短期负荷预测模型^*[J]. 电气工程学报, 2022, 17(1): 164-170.
[7]	林琳, 高雪, 甄钊. 考虑含热电联产机组及可再生能源优先消纳的风-光-水-火联合优化调度[J]. 电气工程学报, 2021, 16(3): 85-91.
[8]	赵少迪, 鲍久圣, 徐浩, 阴妍, 谢厚抗, 刘同冈. 矿用带式输送机承载托辊的磁性液体润滑与密封性能[J]. 机械工程学报, 2021, 57(21): 211-219.
[9]	金石, 宋顺千, 金无痕, 姜旺. 新型复合转子双定子同步电机的复合滑模控制^*[J]. 电气工程学报, 2021, 16(2): 93-99.
[10]	苏福清, 匡洪海, 钟浩, 匡威, 陶成. 基于柯西变异改进粒子群算法的无功优化 ^*[J]. 电气工程学报, 2021, 16(1): 55-61.
[11]	赵金焕,马平. 计及DG功率不确定性的配电网多目标无功优化^*[J]. 电气工程学报, 2020, 15(3): 65-71.
[12]	孙帮成, 刘志明, 崔涛, 李明高, 李红, 王文军. 一种汽车列车结构及其路径跟踪控制方法[J]. 机械工程学报, 2018, 54(24): 181-188.
[13]	朱敏, 陈慧岩. 无人驾驶越野车辆纵向速度跟踪控制试验[J]. 机械工程学报, 2018, 54(24): 111-117.
[14]	牛江川, 韩利涛, 李素娟, 郭京波, 刘进志. 基于PSO-BP神经网络的盾构刀具配置研究[J]. 机械工程学报, 2018, 54(10): 167-172.
[15]	吴伟男, 朱秋国, 吴俊, 熊蓉. 基于粒子群优化算法的单腿机器人膝踝协调运动控制[J]. 机械工程学报, 2017, 53(15): 93-100.