.png){kind=icon}
.png){kind=icon}
Return
Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3252904.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An initial-rectification adaptive iterative learning control scheme is proposed to solve the angle tracking problem of robot manipulators with input deadzone under nonzero initial errors. Lyapunov approach is utilized to design the controller. First, the initial-rectification auxiliary reference signal is constructed to overcome the obstacle caused by nonzero initial errors during ILC design. Second, adaptive ILC strategy and robust control strategy are adopted for dealing with deadzone nonlinearity. In addition, adaptive learning neural network is applied to approximate for uncertainties. The stability of closed-loop robotic system is rigorously proven by theoretical analysis. In the end, numerical simulation results are provided to verify the effectiveness of the proposed adaptive iterative learning control scheme.
Keywords:
Robots
Manipulators
Iterative learning control
Neural networks
Indexes
Uncertainty
Adaptive learning
initial position problem
robot manipulators
deadzone
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W