Adaptive Iterative Learning Control for Robot Manipulators with Initial Resetting Errors

2011 ◽  
Vol 130-134 ◽  
pp. 265-269 ◽  
Author(s):  
Jian Ming Wei ◽  
Yun An Hu
Keyword(s):  
Iterative Learning Control ◽  
Robot Manipulators ◽  
Error Function ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Unknown Parameters ◽  
Adaptive Iterative Learning Control ◽  
Suitable Parameter ◽  
Parameter Values

In this paper, an adaptive iterative learning control is presented for robot manipulators with unknown parameters, performing repetitive tasks. In order to overcome the initial resetting errors, an auxiliary tracking error function is introduced. The adaptive algorithm is derived along the iteration axis to search for suitable parameter values. The technical analysis shows convergence of the tracking errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed controller.

Backstepping Adaptive Iterative Learning Control for Robotic Systems

2013 ◽  
Vol 284-287 ◽  
pp. 1759-1763
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Neural Network ◽  
Iterative Learning Control ◽  
Fuzzy Neural Network ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks ◽  
Fuzzy Neural

A backstepping adaptive iterative learning control for robotic systems with repetitive tasks is proposed in this paper. The backstepping-like procedure is introduced to design the AILC. A fuzzy neural network is applied for compensation of the unknown certainty equivalent controller. Using a Lyapunov like analysis, we show that the adjustable parameters and internal signals remain bounded, the tracking error will asymptotically converge to zero as iteration goes to infinity.

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