TY - JOUR
T1 - Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators with Disturbance Observer
AU - Li, Gang
AU - Yu, Jinpeng
AU - Chen, Xinkai
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.
AB - This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.
KW - Command filter
KW - disturbance observer
KW - full-state constraints
KW - fuzzy neural network (NN)
KW - impedance control
KW - input saturation
UR - http://www.scopus.com/inward/record.url?scp=85118642034&partnerID=8YFLogxK
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U2 - 10.1109/TNNLS.2021.3113044
DO - 10.1109/TNNLS.2021.3113044
M3 - Article
C2 - 34587102
AN - SCOPUS:85118642034
SN - 2162-237X
VL - 34
SP - 5171
EP - 5180
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 8
ER -