Iterative learning control for linear time-variant continuous systems with iteration-varying initial conditions and iteration-varying reference trajectories

2010 ◽  
Author(s):  
Mang-Mang Lv ◽  
Xiao-Dong Li ◽  
Teng-Fei Xiao
Keyword(s):  
Iterative Learning Control ◽  
Initial Conditions ◽  
Linear Time ◽  
Learning Control ◽  
Iterative Learning ◽  
Continuous Systems ◽  
Reference Trajectories

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 Robust Conditions for Iterative Learning Control in State Feedback and Output Injection Paradigm

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Muhammad A. Alsubaie ◽  
Mubarak KH. Alhajri ◽  
Tarek S. Altowaim ◽  
Salem H. Salamah
Keyword(s):  
Iterative Learning Control ◽  
State Feedback ◽  
Linear Time ◽  
Stability Margin ◽  
Learning Control ◽  
Iterative Learning ◽  
Delay Model ◽  
Linear Time Invariant ◽  
System Input ◽  
Linear Time Invariant Systems

A robust Iterative Learning Control (ILC) design that uses state feedback and output injection for linear time-invariant systems is reintroduced. ILC is a control tool that is used to overcome periodic disturbances in repetitive systems acting on the system input. The design basically depends on the small gain theorem, which suggests isolating a modeled disturbance system and finding the overall transfer function around the delay model. This assures disturbance accommodation if stability conditions are achieved. The reported design has a lack in terms of the uncertainty issue. This study considered the robustness issue by investigating and setting conditions to improve the system performance in the ILC design against a system’s unmodeled dynamics. The simulation results obtained for two different systems showed an improvement in the stability margin in the case of system perturbation.

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
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