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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (15): 630160-630160.doi: 10.7527/S1000-6893.2024.30160

• special column • Previous Articles    

Composite adaptive tracking control of aerospace electro⁃hydrostatic actuator servo system

Zhenhua DOU1, Kai GUO1(), Xiaoming HUANG2, Jie SUN1, Zheqing ZUO3, Shoujun ZHAO3   

  1. 1.School of Mechanical Engineering,Shandong University,Jinan 250061,China
    2.College of Mechanical and Electrical Engineering,Shandong University of Aeronautics,Binzhou 256600,China
    3.Beijing Institute of Precision Mechatronics and Controls,Beijing 100076,China
  • Received:2024-01-15 Revised:2024-03-06 Accepted:2024-04-10 Online:2024-05-11 Published:2024-04-25
  • Contact: Kai GUO E-mail:kaiguo@sdu.edu.cn
  • Supported by:
    Open Fund of Laboratory of Aerospace Servo Actuation and Transmission(LASAT-2022-A01-03);National Natural Science Foundation of China(52375452);Aeronautical Science Foundation of China(2023M0440Q3001)

Abstract:

In addressing challenges such as slow parameter convergence, suboptimal tracking performance, and rigorous persistent-excitation conditions encountered in the adaptive control methods within electro-hydrostatic actuator servo system, this study explores the development of a composite learning adaptive position tracking control algorithm. Leveraging the dynamics model of the electro-hydrostatic actuator servo system, the adaptive tracking controller is formulated through a combination of backstepping control and composite learning. Notably, parameter estimation is simultaneously guided by both tracking error and prediction error, ensuring convergence of both the tracking error and parameter estimation error under interval-excitation conditions, and the consistent stability of the controller is proved by the Lyapunov theory. The simulation and experimental findings indicate that, in contrast to traditional adaptive control, the proposed method eliminates the need for a cylinder acceleration signal and demonstrating superior trajectory tracking. Following the implementation of composite learning, the system's tracking error registers a significant 50% reduction and demonstrating heightened resilience.

Key words: adaptive control, electro-hydrostatic actuator servo system, composite learning, trajectory tracking, parameter convergence

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