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.

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.

An Output Based Adaptive Iterative Learning Control with Particle Swarm Optimization for Robotic Systems

2013 ◽  
Vol 479-480 ◽  
pp. 737-741
Author(s):  
Ying Chung Wang ◽  
Chiang Ju Chien ◽  
Chi Nan Chuang
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Particle Swarm ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Robotic Systems ◽  
Adaptive Iterative Learning Control ◽  
Swarm Optimization

We consider an output based adaptive iterative learning control (AILC) for robotic systems with repetitive tasks in this paper. Since the joint velocities are not measurable, a sliding window of measurements and an averaging filter approach are used to design the AILC. Besides, the particle swarm optimization (PSO) is used to adjust the learning gains in the learning process to improve the learning performance. Finally, a Lyapunov like analysis is applied to show that the norm of output tracking error will asymptotically converge to a tunable residual set as iteration goes to infinity.

Adaptive robust iterative learning control with application to a Delta robot

2020 ◽  
pp. 095965182093853
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Derivative Type ◽  
Delta Robot ◽  
Proportional Derivative Controller

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

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