TY - JOUR
T1 - Adaptive EKF enhanced fault diagnosis and fault tolerant control for space manipulators with position measurements only
AU - Zhang, Teng
AU - Shi, Peng
AU - Li, Wenlong
AU - Yue, Xiaokui
N1 - Publisher Copyright:
© 2024
PY - 2024/5
Y1 - 2024/5
N2 - Space manipulators play a critical role in the operation of space robots. However, the failure of these manipulators can affect the control performance and even lead to instability. To improve the safety and robustness of space manipulators, this paper presents an efficient fault diagnosis and fault tolerant control method by integrating adaptive extended Kalman filter (AEKF) and sliding mode control (SMC). The proposed method uses AEKF for system estimation, taking into account uncertainties in the process and measurement noise, as well as sensor limitations. By detecting the failure of angle sensors timely, the trajectory tracking performance can be guaranteed by the robustness of AEKF. In cases where a joint experiences free-swinging, the failure can be identified based on the estimated control effectiveness coefficients from AEKF. Then, once the failure is identified, the joint angle can be regulated by exploiting the motion coupling between the joints. Furthermore, SMC is employed to mitigate the effects of unknown disturbances in the joints. The effectiveness of the proposed method is demonstrated through numerical simulation, which illustrates its ability to diagnose failures and maintain control performance under various failure scenarios. In addition, the robustness of the method is verified through Monte Carlo simulation, demonstrating its reliability in dealing with various uncertainties.
AB - Space manipulators play a critical role in the operation of space robots. However, the failure of these manipulators can affect the control performance and even lead to instability. To improve the safety and robustness of space manipulators, this paper presents an efficient fault diagnosis and fault tolerant control method by integrating adaptive extended Kalman filter (AEKF) and sliding mode control (SMC). The proposed method uses AEKF for system estimation, taking into account uncertainties in the process and measurement noise, as well as sensor limitations. By detecting the failure of angle sensors timely, the trajectory tracking performance can be guaranteed by the robustness of AEKF. In cases where a joint experiences free-swinging, the failure can be identified based on the estimated control effectiveness coefficients from AEKF. Then, once the failure is identified, the joint angle can be regulated by exploiting the motion coupling between the joints. Furthermore, SMC is employed to mitigate the effects of unknown disturbances in the joints. The effectiveness of the proposed method is demonstrated through numerical simulation, which illustrates its ability to diagnose failures and maintain control performance under various failure scenarios. In addition, the robustness of the method is verified through Monte Carlo simulation, demonstrating its reliability in dealing with various uncertainties.
KW - Adaptive extended Kalman filter
KW - Fault diagnosis
KW - Fault tolerant control
KW - Sliding mode control
KW - Space manipulator
UR - http://www.scopus.com/inward/record.url?scp=85190726400&partnerID=8YFLogxK
U2 - 10.1016/j.jfranklin.2024.106824
DO - 10.1016/j.jfranklin.2024.106824
M3 - 文章
AN - SCOPUS:85190726400
SN - 0016-0032
VL - 361
JO - Journal of the Franklin Institute
JF - Journal of the Franklin Institute
IS - 8
M1 - 106824
ER -