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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
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    References listed on IDEAS

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    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.
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    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.

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