Analysis and synthesis of robust iterative learning control for linear continuous systems based on 2D theory

2015 ◽  
Author(s):  
Olfa Kouki ◽  
Chaouki Mnasri ◽  
Moncef Gasmi
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Continuous Systems ◽  
Analysis And Synthesis

scholarly journals Iterative Learning Control Design and Application for Linear Continuous Systems with Variable Initial States Based on 2-D System Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Wei Guan ◽  
Qiao Zhu ◽  
Xu-Dong Wang ◽  
Xu-Hui Liu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Linear Matrix ◽  
Design Criteria ◽  
Continuous Systems ◽  
Matrix Inequalities ◽  
Roesser Model ◽  
Initial State ◽  
Initial States

This paper is concerned with the variable initial states problem in iterative learning control (ILC) for linear continuous systems. Firstly, the properties of the trajectory of 2-D continuous-discrete Roesser model are analyzed by using Lyapunov's method. Then, for any variable initial states which absolutely converge to the desired initial state, some ILC design criteria in the form of linear matrix inequalities (LMI) are given to ensure the convergence of the PD-type ILC rules. The convergence for variable initial states implies that the ILC rules can be used to achieve the perfect tacking for variable initial states, even if the system dynamic is unknown. Finally, the micropropulsion system is considered to illustrate efficiency of the proposed ILC design criteria.

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.

PD-type iterative learning control for linear continuous systems with arbitrary relative degree

2018 ◽  
Vol 41 (9) ◽  
pp. 2555-2562 ◽  
Author(s):  
Qin Fu ◽  
Lili Du ◽  
Guangzhao Xu ◽  
Jianrong Wu ◽  
Pengfei Yu
Keyword(s):  
Iterative Learning Control ◽  
Contraction Mapping ◽  
Learning Control ◽  
Mapping Method ◽  
Iterative Learning ◽  
Relative Degree ◽  
Time Interval ◽  
Continuous Systems ◽  
Iteration Index ◽  
Learning Schemes

This article investigates the iterative learning control problem for linear continuous systems with fixed initial shifts. The systems have arbitrary relative degree and PD-type learning schemes are proposed. Under the effect of the PD-type learning schemes, the output-limiting trajectory is constructed. Based on the contraction mapping method, we show that the schemes can guarantee that the output of the iterative system converges uniformly to the output-limiting trajectory on the finite-time interval as the iteration index tends to infinity. A simulation example is used to illustrate the effectiveness of the proposed method.

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