scholarly journals Design and Implementation of an Accelerated Error Convergence Criterion for Norm Optimal Iterative Learning Controller

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1766
Author(s):  
Saleem Riaz ◽  
Hui Lin ◽  
Muhammad Pervez Akhter
Keyword(s):  
Dynamic Systems ◽  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Nonlinear Dynamic Systems ◽  
Convergence Criterion ◽  
Design And Implementation ◽  
Linear And Nonlinear ◽  
Huge Challenge

Designing an optimal iterative learning control is a huge challenge for linear and nonlinear dynamic systems. For such complex systems, standard Norm optimal iterative learning control (NOILC) is an important consideration. This paper presents a novel NOILC error convergence technique for a discrete-time method. The primary effort of the controller is to converge the error efficiently and quickly in an optimally successful way. A new iterative learning algorithm based on feedback based on reliability against input disruption was proposed in this paper. The illustration of the simulations authenticates the process suggested. The numerical example simulated on MATLAB@2019 and the mollified results affirm the validation of the designed algorithm.

Cross-coupled iterative learning control of systems with dissimilar dynamics: design and implementation

2011 ◽  
Vol 84 (7) ◽  
pp. 1223-1233 ◽  
Author(s):  
Kira L. Barton ◽  
David J. Hoelzle ◽  
Andrew G. Alleyne ◽  
Amy J. Wagoner Johnson
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Design And Implementation

Iterative Learning Control for a Class of Modeling Undesirable Single Degree of Freedom System

2014 ◽  
Vol 538 ◽  
pp. 379-382
Author(s):  
Wei Zhou ◽  
Bao Bin Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Degree Of Freedom ◽  
Control Input ◽  
Convergence Properties ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Simulation Results

A class of modeling undesirable single degree of freedom system is studied by using iterative learning control. The proposed iterative learning algorithm constantly updates the control input according to output error until the desired output occurred. So the system with designed controller can achieve perfect accuracy. We have proved convergence properties in iteration domain and simulation results demonstrate the effectiveness of the presented method.

Enhanced Policy Adaptation Through Directed Explorative Learning

2015 ◽  
Vol 12 (03) ◽  
pp. 1550028 ◽  
Author(s):  
Rok Vuga ◽  
Bojan Nemec ◽  
Aleš Ude
Keyword(s):  
Reinforcement Learning ◽  
Iterative Learning Control ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Framework ◽  
Policy Adaptation ◽  
Explorative Learning ◽  
Integrated Policy

In this paper, we propose an integrated policy learning framework that fuses iterative learning control (ILC) and reinforcement learning. Integration is accomplished at the exploration level of the reinforcement learning algorithm. The proposed algorithm combines fast convergence properties of iterative learning control and robustness of reinforcement learning. This way, the advantages of both approaches are retained while overcoming their respective limitations. The proposed approach was verified in simulation and in real robot experiments on three challenging motion optimization problems.

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