Iterative Learning Control: Practical Implementation and Automation

Saab, Samer S.; Shen, Dong; Orabi, Mohamad; Kors, David; Jaafar, Rayana H.

doi:10.1109/tie.2021.3063866

Cited by 59 publications

(27 citation statements)

References 42 publications

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“…In this paper, we focus our attention primarily on the initial condition problem of AILC. However, the published results highlighted difficulties associated with AILC designs in terms of uncertainties 5,7 . There are theoretical and practical issues should be taken into account, and the crucial issues include unmodeled dynamics, measurement noise, etc.…”

Section: Discussionmentioning

confidence: 99%

“…During the past four decades, the contraction-mapping approach that is well understood has been successfully applied in learning system synthesis. 3,5 One of the most fundamental hypotheses for the conventional learning control systems is the repetivity, which pose requirements on initial conditions and desired trajectories. ILC has been verified to effectively address the nonrepetitive uncertainties that arise from practical implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Iterative learning control (ILC) is suitable for systems performing tasks repetitively, and through learning, the tracking performance can be improved trial by trial 1 . A wide range of successful applications of ILC were reported (see, e.g., robot manipulators, 2‐5 mechatronic systems, 6‐8 and autonomous vehicles 9,10 ). During the past four decades, the contraction‐mapping approach that is well understood has been successfully applied in learning system synthesis 3,5 .…”

Section: Introductionmentioning

confidence: 99%

“…A wide range of successful applications of ILC were reported (see, e.g., robot manipulators, 2‐5 mechatronic systems, 6‐8 and autonomous vehicles 9,10 ). During the past four decades, the contraction‐mapping approach that is well understood has been successfully applied in learning system synthesis 3,5 . One of the most fundamental hypotheses for the conventional learning control systems is the repetivity, which pose requirements on initial conditions and desired trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…5,1]. The reference trajectory to be tracked is given asy * k (tT s ) = tT s ∈ [0, Δ 1 ] ( p k israndomly iteration-variant on [0, 0.5] and Δ 1 = 10s.…”

mentioning

confidence: 99%

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Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Sun

Zhan

et al. 2022

Adaptive Control & Signal

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Summary Initial condition problem is crucial to a conventional iterative learning control (ILC) scheme, which steers the tracking error from arbitrary initial value to zero in the time steps equaling to the relative degree of the system undertaken. The implementation may be difficult, due to the practical limitation for the control amplitude. This paper presents an error‐tracking approach to discrete‐time adaptive ILC designs for tracking nonidentical tasks in the presence of initial repositioning errors, which may be quite large. A prototype iterative learning algorithm is derived for estimating the time‐varying unknowns, and the saturation is introduced for assuring the estimates to keep away from zero. A key technical lemma, tailored for the analysis purpose in the iteration domain, is presented and applied to analysis of the ILC scheme. The tracking performance of the closed‐loop system is evaluated and explored in detail. It is shown that the perfect tracking for the error between the tracking error and the desired error is achieved at every time instant, while the input and output signals remain bounded. By the simulation example, the proposed scheme is verified to be applicable to tracking tasks without restriction on initial repositioning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…5,1]. The reference trajectory to be tracked is given asy * k (tT s ) = tT s ∈ [0, Δ 1 ] ( p k israndomly iteration-variant on [0, 0.5] and Δ 1 = 10s.…”

mentioning

confidence: 99%

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Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Sun

Zhan

et al. 2022

Adaptive Control & Signal

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Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Lan

Zheng

2022

Adaptive Control & Signal

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This article presents a PI-type iterative learning control (ILC) law with initial state learning for a class of 𝛼 (0 < 𝛼 ≤ 1) fractional order two-dimensional (2D) linear systems. First, by using backward difference method, the finite difference approximation of the fractional order derivative is obtained, which leads to globally 2 − 𝛼 order accuracy. Then, a PI-ILC law is constructed at the nodes and the convergence analysis of the iterative scheme is proved. A linear matrix inequality-based sufficient condition is derived to guarantee that the tracking error is asymptotically convergent. The obtained convergence condition is fractional order dependent. Most of the classical ILC conditions for fractional order one-dimensional linear systems fall into the special cases of this article. Finally, the simulation results show the effectiveness of the proposed control method.

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Variable‐gain PD‐type iterative learning control for a class of nonlinear time‐varying systems

Zhang,

Ding,

et al. 2023

Asian Journal of Control

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This paper investigates variable‐gain PD‐type iterative learning control (ILC) for a class of nonlinear time‐varying systems to well balance high‐gain convergence rate and low‐gain noise transmission. Different from the classic PD‐type ILC, the control gains of the proposed method are variable. Each variable‐gain consists of an amplitude‐dependent term and an iteration‐varying term. The amplitude‐dependent terms vary with the amplitudes of tracking error and derivative of tracking error, and the iteration‐varying terms are increasing along the iteration axis. The proposed ILC achieves a faster convergence rate than low‐gain ILC and higher tracking accuracy with limited noise amplification than high‐gain ILC. Moreover, the convergence condition of the proposed method in the presence of external noise is provided. Simulation and experimental results demonstrate the effectiveness of the proposed method.

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Iterative Learning Control: Practical Implementation and Automation

Cited by 59 publications

References 42 publications

Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Error tracking approach to discrete‐time adaptive iterative learning control against arbitrary initial perturbations

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Variable‐gain PD‐type iterative learning control for a class of nonlinear time‐varying systems

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