Systems Engineering and Electronics ›› 2024, Vol. 46 ›› Issue (4): 1287-1296.doi: 10.12305/j.issn.1001-506X.2024.04.17

• Systems Engineering • Previous Articles     Next Articles

Robust fault diagnosis for space robot manipulator based on adaptive super-twisting observer

Sheng GAO1,2, Hailong ZHANG3, Wei ZHANG1,2,*, Weiguo KONG1,2   

  1. 1. National State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
    3. Key Laboratory for Fault Diagnosis and Maintenance of Spacecraft in Orbit, Xi'an 710043, China
  • Received:2023-04-20 Online:2024-03-25 Published:2024-03-25
  • Contact: Wei ZHANG

Abstract:

In view of the problem of robust fault diagnosis of actuators in space robot manipulators a fault diagnosis method based on improved adaptive super-twisting observer is pro-posed. To attenuate the influence of external disturbances caused by the sophisticated space environment on the fault diagnosis results, an adaptive adjusting algorithm of the observer parameters is introduced based on the classical super-twisting observer, and the problem of over-estimation of the observer parameters and noise expansion are solved simultaneously. In addition, the smoothness and rapidity of the observer are improved by introducing additional fractional power less than 1 and linear term to further enhance the fault diagnosis effect. Then, the finite-time stability of the observer is analysed based on the Moreno-Lyapunov function algorithm, which proves that the estimation error of the observer can converge to a region of zero in finite time. Moreover, a residual generation method is proposed and a fault diagnosis scheme based on the adaptive threshold is further designed. Finally, the effectiveness of the proposed method is verified by simulations expriment of the asteroid sampling space three-link robot manipulator example.

Key words: space robot manipulator, actuator fault, fault diagnosis, residual imformation, adaptive super-twisting observer

CLC Number: 

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