IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i18p3578-d268618.html
   My bibliography  Save this article

Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System

Author

Listed:
  • Zhihong Wu

    (Automatic Test Equipment and System Engineering Research Center of Shanxi Province, North University of China, Taiyuan 030051, China)

  • Ruifeng Yang

    (Automatic Test Equipment and System Engineering Research Center of Shanxi Province, North University of China, Taiyuan 030051, China)

  • Chenxia Guo

    (Automatic Test Equipment and System Engineering Research Center of Shanxi Province, North University of China, Taiyuan 030051, China)

  • Shuangchao Ge

    (Automatic Test Equipment and System Engineering Research Center of Shanxi Province, North University of China, Taiyuan 030051, China)

  • Xiaole Chen

    (Automatic Test Equipment and System Engineering Research Center of Shanxi Province, North University of China, Taiyuan 030051, China)

Abstract

Electric servo system (ESS) is a servo mechanism in a control system of an aircraft, a ship, etc., which controls efficiency and directly affects the energy consumption and the dynamic characteristics of the system. However, the control performance of the ESS is affected by uncertainties such as friction, clearance, and component aging. In order to improve the control performance of the ESS, a control technology combining particle swarm optimization (PSO) and finite time servo system control (FTSSC) was introduced into ESS. In fact, it is difficult to know the uncertain physical parameters of the real ESS. In this paper, the genetic algorithm (GA) was introduced into PSO and the inertia weight was improved, which increased the parameter optimization precision and convergence speed. A new feedback controller is proposed to improve response speed and reduce errors by using FTSSC theory. The performance of the controller based on PSO identification algorithm was verified by co-simulation experiments based on Automatic Dynamic Analysis of Mechanical Systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). Meanwhile, the proposed strategy was validated on the servo test platform in the laboratory. Compared with the existing control strategy, the control error was reduced by 75% and the steady-state accuracy was increased by at least 50%.

Suggested Citation

  • Zhihong Wu & Ruifeng Yang & Chenxia Guo & Shuangchao Ge & Xiaole Chen, 2019. "Analysis and Verification of Finite Time Servo System Control with PSO Identification for Electric Servo System," Energies, MDPI, vol. 12(18), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3578-:d:268618
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/18/3578/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/18/3578/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guanbin Gao & Fei Liu & Hongjun San & Xing Wu & Wen Wang, 2018. "Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN-PSO," Complexity, Hindawi, vol. 2018, pages 1-11, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhihong Wu & Ruifeng Yang & Chenxia Guo & Shuangchao Ge & Xiaole Chen, 2020. "Synthesis and Verification of Finite-Time Rudder Control with GA Identification for Electric Rudder System," Energies, MDPI, vol. 13(6), pages 1-18, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sabir, Zulqurnain & Said, Salem Ben & Baleanu, Dumitru, 2022. "Swarming optimization to analyze the fractional derivatives and perturbation factors for the novel singular model," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Xin Xu & Ahmed Shaker & Marwa S. Salem, 2022. "Automatic Control of a Mobile Manipulator Robot Based on Type-2 Fuzzy Sliding Mode Technique," Mathematics, MDPI, vol. 10(20), pages 1-18, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:18:p:3578-:d:268618. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.