TY - JOUR
T1 - Disturbance Observer-Based Fault-Tolerant Control for Robotic Systems with Guaranteed Prescribed Performance
AU - Huang, Haifeng
AU - He, Wei
AU - Li, Jiashu
AU - Xu, Bin
AU - Yang, Chenguang
AU - Zhang, Weicun
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.
AB - The actuator failure compensation control problem of robotic systems possessing dynamic uncertainties has been investigated in this paper. Control design against partial loss of effectiveness (PLOE) and total loss of effectiveness (TLOE) of the actuator are considered and described, respectively, and a disturbance observer (DO) using neural networks is constructed to attenuate the influence of the unknown disturbance. Regarding the prescribed error bounds as time-varying constraints, the control design method based on barrier Lyapunov function (BLF) is used to strictly guarantee both the steady-state performance and the transient performance. A simulation study on a two-link planar manipulator verifies the effectiveness of the proposed controllers in dealing with the prescribed performance, the system uncertainties, and the unknown actuator failure simultaneously. Implementation on a Baxter robot gives an experimental verification of our controller.
KW - Actuator failure compensation
KW - barrier Lyapunov function (BLF)
KW - Baxter
KW - disturbance observer (DO)
KW - neural networks
KW - prescribed performance
UR - https://www.scopus.com/pages/publications/85124800622
U2 - 10.1109/TCYB.2019.2921254
DO - 10.1109/TCYB.2019.2921254
M3 - 文章
C2 - 32356765
AN - SCOPUS:85124800622
SN - 2168-2267
VL - 52
SP - 772
EP - 783
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 2
ER -