A fast iterative learning control scheme for linear time-variant continuous systems

2007 ◽  
Author(s):  
Deyuan Meng ◽  
Yingmin Jia ◽  
Junping Du ◽  
Shiying Yuan
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Learning Control ◽  
Iterative Learning ◽  
Continuous Systems ◽  
Control Scheme

A New Iterative Learning Control Scheme for Linear Time-varying Discrete Systems

2007 ◽  
Vol 40 (14) ◽  
pp. 279-282 ◽  
Author(s):  
Lukasz Hladowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers ◽  
Paul L. Lewin ◽  
Christopher T. Freeman
Keyword(s):  
Iterative Learning Control ◽  
Linear Time ◽  
Discrete Systems ◽  
Learning Control ◽  
Iterative Learning ◽  
Time Varying ◽  
Control Scheme

Adaptive iterative learning control for unknown linear time-varying continuous systems

2019 ◽  
Vol 42 (5) ◽  
pp. 981-996
Author(s):  
Fateme Afsharnia ◽  
Ali Madady ◽  
Mohammad Bagher Menhaj
Keyword(s):  
Iterative Learning Control ◽  
Reference Model ◽  
Linear Time ◽  
Learning Control ◽  
Iterative Learning ◽  
Gronwall Lemma ◽  
Continuous Systems ◽  
Time Varying ◽  
Adaptive Iterative Learning Control ◽  
Time Varying Systems

This paper presents a novel model reference adaptive iterative learning control (ILC) for unknown continuous-time linear time-varying systems. The unknown time-varying parameters of the system are neither required to vary slowly nor to have known bounds. The system is not required to be minimum-phase, stable, controllable or observable. The input of the system is determined by a differentiator-free control law. The used reference model is time-invariant and first order and thus choosing its parameters is easily possible, even though, the system under control is high order and time variant. Almost all of the components of the system initial condition can be iteration variant. By introducing a novel kind of Lyapunov function the convergence of the proposed adaptive ILC (AILC) and achieving asymptotic tracking are proved. Also, by rigorous mathematical analysis and with the help of some mathematical key techniques such as Bellman-Gronwall lemma, it is shown that all signals and quantities in the closed-loop system are bounded in the sense of at least one norm. Finally, the effectiveness of the proposed method is verified by two simulation examples.

scholarly journals Open-closed Iterative Learning Control Algorithm for Exoskeleton Rehabilitation Purposes

2019 ◽  
Vol 292 ◽  
pp. 01010
Author(s):  
Mihailo Lazarević ◽  
Nikola Živković ◽  
Darko Radojević
Keyword(s):  
Iterative Learning Control ◽  
Control Algorithm ◽  
Closed Loop ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Control Law ◽  
Exact Linearization ◽  
Outer Loop ◽  
Control Scheme

The paper designs an appropriate iterative learning control (ILC) algorithm based on the trajectory characteristics of upper exosk el eton robotic system. The procedure of mathematical modelling of an exoskeleton system for rehabilitation is given and synthesis of a control law with two loops. First (inner) loop represents exact linearization of a given system, and the second (outer) loop is synthesis of a iterative learning control law which consists of two loops, open and closed loop. In open loop ILC sgnPDD2 is applied, while in feedback classical PD control law is used. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed advanced open-closed iterative learning control scheme.

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