Robust Adaptive Iterative Learning Control for Trajectory Tracking of Uncertain Robotic Systems

2018 ◽  
Author(s):  
Meirong Qian ◽  
Jin Jiang
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Robust Adaptive

ADAPTIVE ITERATIVE LEARNING CONTROL OF ROBOTIC SYSTEMS USING BACKSTEPPING DESIGN

2013 ◽  
Vol 37 (3) ◽  
pp. 591-601 ◽  
Author(s):  
Ying-Chung Wang ◽  
Chiang-Ju Chien ◽  
Chi-Nan Chuang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Certainty Equivalent ◽  
Robotic Systems ◽  
Backstepping Design ◽  
Adaptive Iterative Learning Control ◽  
Repetitive Tasks

In this paper, a backstepping adaptive iterative learning control (AILC) is proposed for robotic systems with repetitive tasks. The AILC is designed to approximate unknown certainty equivalent controller. Finally, we apply a Lyapunov like analysis to show that all adjustable parameters and the internal signals remain bounded for all iterations.

scholarly journals Alignment Condition-Based Robust Adaptive Iterative Learning Control of Uncertain Robot System

2014 ◽  
Vol 6 ◽  
pp. 201317 ◽  
Author(s):  
Guofeng Tong ◽  
Mingxiu Lin
Keyword(s):  
Iterative Learning Control ◽  
Direct Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Robust Adaptive Control ◽  
Robot System ◽  
Adaptive Iterative Learning Control ◽  
Alignment Condition ◽  
Robust Adaptive ◽  
Uncertain Robot

This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.

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