基于改进型蚁狮算法的PID控制器参数优化
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摘要
由于被控对象往往具有高阶非线性等特点,传统PID(Proportion Integration Differentiation)控制器参数整定方法容易使控制器出现超调、震荡、性能变差等缺陷。为此,提出运用将蚂蚁和蚁狮的移动步长进行改进的蚁狮算法对参数进行优化,通过其互动关系,选择最佳蚁狮位置确定控制器参数,并与改进前蚁狮算法及其他优化算法进行了对比。仿真结果表明,基于改进型蚁狮算法的PID控制器具有较好的性能指标,相比于改进前蚁狮算法、遗传算法和粒子群算法,该算法具有较高的系统控制精度,以及较短的响应时间等优点,且算法实现更加简单,证明了该方法对于优化PID参数具有优越性和有效性,为PID控制器的参数优化提供了参考。
Because of the high order nonlinearity of the controlled object,the traditional PID( Proportion Integration Differentiation) controller of parameter tuning method can easily lead to overshoot,oscillation and poor performance for the controller. To solve this problem,the parameters are optimized by using the ant lion algorithm which improves the moving step size of ants and ant lions. The optimal position of ant lion is selected to determine the controller parameters through its interaction relationship,and the algorithm is compared with the former ant lion algorithm and other optimization algorithms. The simulation results show that the PID controller based on the improved ant lion algorithm has better performance index. Compared with the former ant lion algorithm,genetic algorithm and particle swarm optimization,this algorithm has the advantages of higher system control accuracy and shorter response time,and its implementation is simpler. This method has superiority and validity in optimizing the parameters of PID controller,and provides a reference for the parameter optimization of PID controller.
Because of the high order nonlinearity of the controlled object,the traditional PID( Proportion Integration Differentiation) controller of parameter tuning method can easily lead to overshoot,oscillation and poor performance for the controller. To solve this problem,the parameters are optimized by using the ant lion algorithm which improves the moving step size of ants and ant lions. The optimal position of ant lion is selected to determine the controller parameters through its interaction relationship,and the algorithm is compared with the former ant lion algorithm and other optimization algorithms. The simulation results show that the PID controller based on the improved ant lion algorithm has better performance index. Compared with the former ant lion algorithm,genetic algorithm and particle swarm optimization,this algorithm has the advantages of higher system control accuracy and shorter response time,and its implementation is simpler. This method has superiority and validity in optimizing the parameters of PID controller,and provides a reference for the parameter optimization of PID controller.
引文
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[12]PETROVIC M,PETRONIJEVIC J,MITIC M,et al. The Ant Lion Optimization Algorithm for Flexible Process Planning[J].Production Engineering,2015,18(2):65-68.
[13]TALATAHARI S. Optimum Design of Skeletal Structures Using Ant Lion Optimizer[J]. Int J Optim Civ Eng,2016,6(1):10-19.
[14]黄长强,赵克新.基于改进蚁狮算法的无人机三维航迹规划[J].电子与信息学报,2018,40(7):1532-1538.HUANG Changqiang,ZHAO Kexin. Three Dimensional Path Planning of UAV with Improved Ant Lion Optimizer[J]. Journal of Electronics&Information Technology,2018,40(7):1532-1538.
[15]ALI E S,ABD ELAZIM S M,ABDLAZIZ A Y. Ant Lion Optimization Algorithm for Renewable Distributed Generations[J].Energy(ELSEVIER),2016,116:445-458.
[16]GOODENOUGH J,MC GUIRE B,JAKOB E. Perspectives on Animal Behavior[M]. 3rd Edition. Hoboken:John Wiley&Sons,2010.
[17]姚禹,王博,高智.基于遗传算法的伺服系统PID控制参数整定[J].长春工业大学学报,2017,38(6):526-531.YAO Yu,WANG Bo,GAO Zhi. Parameter Adjustment of Motor Servo System Based on Genetic Algorithm[J]. Journal of Changchun University of Technology,2017,38(6):526-531.
[18]何东健,刘忠超,范灵燕.基于Matlab的PID控制器参数整定及仿真[J].西安科技大学学报,2006,26(4):511-514.HE Dongjian,LIU Zhongchao,FAN Lingyan. Parameter Tuning and Emulation of Controller Based on MATLAB[J]. Journal of Xi'an University of Science and Technology,2006,26(4):511-514.
[19]TAPAS KUMAR PANIGRAHI,AUROBINDO BEHERA,ARUN KU SAHOO. Novel Approach to Automatic Generation Control with Various Non-Linearities Using 2-Degree-of-Freedom PID Controller[J]. Energy Procedia,2017,138:464-469.
[2]邵裕森,戴先中.过程控制工程[M].北京:机械工业出版社,2005.SHAO Yusen,DAI Xianzhong. Process Control Engineering[M]. Beijing:China Machine Press,2005.
[3]史峰,王辉,郁磊,等. Matlab智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011.SHI Feng,WANG Hui,YU Lei,et al. 30 Cases of Intelligent Algorithm Using Matlab[M]. Beijing:Beihang University Press,2011.
[4]FENG Hao,YIN Chenbo,WENG Wenwen,et al. Robotic Excavator Trajectory Control Using an Improved GA Based PID Controller[J]. Mechanical Systems and Signal Processing,2018,105:153-168.
[5]WANG Jing,HUANG Yourui. Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network[J].Journal of Networks,2013,8(8):1726-1733.
[6]VINEET KUMAR B C,NAKRA A P MITTAL. Some Investigations on Fuzzy P+Fuzzy I+Fuzzy D Controller for NonStationary Process[J]. International Journal of Automation and Computing,2012,9(5):449-458.
[7]雷德明,严新平.多目标智能优化算法及其应用[M].北京:科学出版社,2009.LEI Deming,YAN Xinping. Multi Objective Intelligent Optimization Algorithm and Its Application[M]. Beijing:Sciences Press,2009.
[8]SEYED MOHAMMAD HOSSEIN MOUSAKAZEMI,NAVID AYOOBIAN,GHOLAM REZA ANSARIFAR. Control of the Reactor Core Power in PWR Using Optimized PID Controller with the Real-Coded GA[J]. Annals of Nuclear Energy,2018,118:107-121.
[9]王心,郭伟,魏妙.基于粒子群优化的分数阶PID滑模控制参数整定[J].测控技术,2017,36(12):63-66.WANG Xin,GUO Wei,WEI Miao. Parameter Tuning of Fractional-Order PID Sliding Mode Control Based on Particle Swarm Optimization[J]. Measurement&Control Technology,2017,36(12):63-66.
[10]王庆喜,储泽南.基于动态布谷鸟搜索算法的PID控制器参数优化[J].计算机测量与控制,2015,23(4):1215-1217.WANG Qingxi,CHU Zenan. Parameters Optimization of PID Controller Based on Dynamic Cuckoo Search Algorithm[J].Computer Measurement&Control,2015,23(4):1215-1217.
[11]MIRJALILI S. The Ant Lion Optimizer[J]. Adv Eng Softw,2015,83:80-98.
[12]PETROVIC M,PETRONIJEVIC J,MITIC M,et al. The Ant Lion Optimization Algorithm for Flexible Process Planning[J].Production Engineering,2015,18(2):65-68.
[13]TALATAHARI S. Optimum Design of Skeletal Structures Using Ant Lion Optimizer[J]. Int J Optim Civ Eng,2016,6(1):10-19.
[14]黄长强,赵克新.基于改进蚁狮算法的无人机三维航迹规划[J].电子与信息学报,2018,40(7):1532-1538.HUANG Changqiang,ZHAO Kexin. Three Dimensional Path Planning of UAV with Improved Ant Lion Optimizer[J]. Journal of Electronics&Information Technology,2018,40(7):1532-1538.
[15]ALI E S,ABD ELAZIM S M,ABDLAZIZ A Y. Ant Lion Optimization Algorithm for Renewable Distributed Generations[J].Energy(ELSEVIER),2016,116:445-458.
[16]GOODENOUGH J,MC GUIRE B,JAKOB E. Perspectives on Animal Behavior[M]. 3rd Edition. Hoboken:John Wiley&Sons,2010.
[17]姚禹,王博,高智.基于遗传算法的伺服系统PID控制参数整定[J].长春工业大学学报,2017,38(6):526-531.YAO Yu,WANG Bo,GAO Zhi. Parameter Adjustment of Motor Servo System Based on Genetic Algorithm[J]. Journal of Changchun University of Technology,2017,38(6):526-531.
[18]何东健,刘忠超,范灵燕.基于Matlab的PID控制器参数整定及仿真[J].西安科技大学学报,2006,26(4):511-514.HE Dongjian,LIU Zhongchao,FAN Lingyan. Parameter Tuning and Emulation of Controller Based on MATLAB[J]. Journal of Xi'an University of Science and Technology,2006,26(4):511-514.
[19]TAPAS KUMAR PANIGRAHI,AUROBINDO BEHERA,ARUN KU SAHOO. Novel Approach to Automatic Generation Control with Various Non-Linearities Using 2-Degree-of-Freedom PID Controller[J]. Energy Procedia,2017,138:464-469.