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Research on improved sparrow search algorithm for PID controller parameter optimization

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Języki publikacji
EN
Abstrakty
EN
PID controllers are crucial for industrial control because of their simple structure and good robustness. In order to further improve the accuracy of PID controllers, this paper proposes an improved sparrow search algorithm (ISSA) to prevent the problem of the algorithm being prone to falling into the local optimum at the late stage of iteration. Based on the standard sparrow search algorithm, the position update formula and the step size control parameter are optimized to help quickly jump out of the local, and to obtain the optimal solution in the whole domain. Finally, to verify the accuracy and stability of the improved algorithm, nine standard test functions are first simulated. Then, the PID parameter optimization tests are finished with the chilled water and battery charging systems, where the lifting load and applying perturbation are carried out. Both the simulation and test results show that ISSA improves the convergence speed and accuracy, and performs better in terms of stability.
Rocznik
Strony
art. no. e147344
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China
autor
  • School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China
autor
  • School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China
autor
  • School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China
autor
  • College of Science – Computer Science, University of Arizona, Tucson, Arizona, USA
autor
  • Angang Steel Co. LTD, Anshan Iron & Steel, Anshan, Liaoning, China
Bibliografia
  • [1] D. Wei, Z. Wang, L. Si, and C. Tan, “Preaching-inspired swarm intelligence algorithm and its applications,” Knowledge-Based Syst., vol. 211, p. 106552, 2021, doi: 10.1016/j.knosys.2020.106552.
  • [2] S.B. Joseph, E.G. Dada, A. Abidemi, D.O. Oyewola, and B.M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems,” Heliyon, vol. 8, no. 5, p. e09399, 2022, doi: 10.1016/j.heliyon.2022.e09399.
  • [3] A. Najm and I. K. Ibraheem, “Nonlinear PID controller design for a 6-DOF UAV quadrotor system,” Eng. Sci. Technol., vol. 22, no. 4, pp. 1087–1097, 2019, doi: 10.1016/j.jestch.2019.02.005.
  • [4] P.N. Pugazhenthi, S. Selvaperumal, and K. Vijayakumar, “Non-linear PID controller parameter optimization using modified hybrid artificial bee colony algorithm for continuous stirred tank reactor,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 3, p. e137348, 2021, doi: 10.24425/bpasts.2021.137348.
  • [5] V. Vishnoi, S. Tiwari, and R. Singla, “Performance Analysis of Moth Flame Optimization-Based Split-Range PID Controller,” Mapan, vol. 36, no. 1, pp. 67–79, 2020, doi: 10.1007/s12647-020-00379-0.
  • [6] A. Manjula, L. Kalaivani, M. Gengaraj, R.V. Maheswari, S. Vimal, and S. Kadry, “Performance enhancement of SRM using smart bacterial foraging optimization algorithm based speed and current PID controllers,” Comput. Electr. Eng., vol. 95, p. 107398, 2021, doi: 10.1016/j.compeleceng.2021.107398.
  • [7] L. Dai, H. Lu, D. Hua, X. Liu, L. Wang, and Q. Li, “Research on Control Strategy of a Magnetorheological Fluid Brake Based on an Enhanced Gray Wolf Optimization Algorithm,” Appl. Sci., vol. 12, no. 24, p. 12617, 2022, doi: 10.3390/app122412617.
  • [8] J.-Y. Cao and B.-G. Cao, “Design of Fractional Order Controllers Based on Particle Swarm Optimization,” 2006 1st IEEE conference on industrial electronics and applications. IEEE, 2006.
  • [9] N. Soundirarrajan and K. Srinivasan, “Performance Evaluation of Ant Lion Optimizer–Based PID Controller for Speed Control of PMSM,” J. Test. Eval., vol. 49, no. 2, pp. 1104–1118, 2019, doi: 10.1520/JTE20180892.
  • [10] D. Potnuru, K. Alice Mary, and Ch. Sai Babu, “Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor,” Ain Shams Eng. J., vol. 10, no. 2, pp. 287–295, 2019, doi: 10.1016/j.asej.2018.07.005.
  • [11] K. Cao, Z. Li, Y. Gu, L. Zhang, and L. Chen, “The control design of transverse interconnected electronic control air suspension based on seeker optimization algorithm,” Proc. Inst. Mech. Eng. Part D-J. Automob. Eng., vol. 235, no. 8, pp. 2200–2211, 2021, doi: 10.1177/0954407020984667.
  • [12] H. Naga Sai Kalyan et al., “Seagull Optimization Algorithm–Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints,” Front. Energy Res., vol. 10, 2022, doi: 10.3389/fenrg.2022.921426.
  • [13] Q. Jin, L. Qi, B. Jiang, and Q. Wang, “Novel improved cuckoo search for PID controller design,” Trans. Inst. Meas. Control, vol. 37, no. 6, pp. 721–731, 2014, doi: 10.1177/0142331214544211.
  • [14] J. Xue and B. Shen, “A novel swarm intelligence optimization approach: sparrow search algorithm,” Syst. Sci. Control Eng., vol. 8, no. 1, pp. 22–34, 2020, doi: 10.1080/21642583.2019.1708830.
  • [15] D. Wu, and C. Yuan, “Threshold image segmentation based on improved sparrow search algorithm,” Multimed. Tools Appl., vol. 81, no. 23, pp. 33513–33546, 2022, doi: 10.1007/s11042-022-13073-x.
  • [16] Z. Zhang, R. He, and K. Yang, “A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm,” Adv. Manuf., vol. 10, no. 1, pp. 114–130, 2021, doi: 10.1007/s40436-021-00366-x.
  • [17] G. Liu, C. Shu, Z. Liang, B. Peng, and L. Cheng, “A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV,” Sensors (Basel), vol. 21, no. 4, p. 1224, 2021, doi: 10.3390/s21041224.
  • [18] X. Zhou, J. Wang, H. Zhang, and Q. Duan, “Application of a hybrid improved sparrow search algorithm for the prediction and control of dissolved oxygen in the aquaculture industry,” Appl. Intell., vol. 53, no. 7, pp. 8482–8502, 2023, doi: 10.1007/s10489-022-03870-0.
  • [19] J. Hou, X. Wang, Y. Su, Y. Yang, and T. Gao, “Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm,” Appl. Intell., vol. 12, no. 14, 2022, doi: 10.3390/app12147332.
  • [20] X. Li, J. Gu, X. Sun, J. Li, and S. Tang, “Parameter identification of robot manipulators with unknown payloads using an improved chaotic sparrow search algorithm,” Appl. Intell., vol. 52, no. 9, pp. 10341–10351, 2022, doi: 10.1007/s10489-021-02972-5.
  • [21] Ch. Ouyang, F. Tang, D. Zhu, Y. Qiu, and Y. Liu, “Application of Improved Sparrow Search Algorithm in Concrete,” J. Phys.-Conf. Ser., vol. 2082, p. 012014, 2021, doi: 10.1088/1742-6596/2082/1/012014.
  • [22] X. Lv, X. Mu, J. Zhang, and Z. Wang, “Chaos Sparrow Search Optimization Algorithm,” J. Beijing Univ. Aeronaut. Astronaut., vol. 47, no. 08, pp. 1712–1720, 2021, doi: 10.13700/j.bh.1001-5965.2020.0298.
  • [23] Y. Fan, J. Shao, and G. Sun, “Optimized PID Controller Based on Beetle Antennae Search Algorithm for Electro-Hydraulic Position Servo Control System,” Sensors, vol. 19, no. 12, p. 2727, 2019, doi: 10.3390/s19122727.
  • [24] Guo, Z. Zhuang, J.-S. Pan, and S.-C. Chu, “Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm,” IEEE Access, vol. 9, pp. 8808–8819, 2021, doi: 10.1109/access.2021.3049421.
  • [25] Ashmi M, Anila M, Sivanandan K S, Jayaraj S, “Comparison of Z–N and PSO based tuning methods in the control strategy of prosthetic limbs application,” Journal of Theoretical and Applied Mechanics, vol.58, no. 4, pp. 841–851, 2020, doi: 10.15632/jtam-pl/125505.
  • [26] M. Ouyang, Y. Wang Y, Wu F, and Lin Y, “Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm,” Processes, vol.11, no. 5, pp. 1302, 2023, doi: 10.3390/pr11051302.
  • [27] Q. Zhu, M. Zhuang, H. Liu, and Y. Zhu, “Optimal Control of Chilled Water System Based on Improved Sparrow Search Algorithm,” Buildings, vol. 12, no. 3, 2022, doi: 10.3390/buildings12030269
  • [28] T. Wu, C. Zhou, Z. Yan, H. Peng, and L. Wu, “Application of PID optimization control strategy based on particle swarm optimization (PSO) for battery charging system,” International Journal of Low-Carbon Technologies, vol. 15, no. 4, pp. 528–535, 2020, doi: 10.1093/ijlct/ctaa020.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c5ec8783-f55e-4a06-a840-7156112a4b25
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