A guaranteed monotonically convergent iterative learning control

Madady, Ali; Reza-Alikhani, Hamid-Reza

doi:10.1002/asjc.397

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…Remark 1. Assumptions 1 and 2 are common in [1,[6][7][8][9][10][11][12][13]. Assumption 3 basically defines a new task of TILC, where the desired terminal point is varying with iterations.…”

Section: Problem Formulationmentioning

confidence: 99%

Adaptive Terminal ILC for Iteration‐varying Target Points

Chi¹,

Wang²,

Lewis³

et al. 2014

Asian Journal of Control

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Terminal iterative learning control (TILC) has been developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations. In this work, the desired terminal point is not fixed but allowed to change run‐to‐run among a set of fixed points and a new adaptive terminal iterative learning control scheme is developed to achieve learning objective over iterations. The control signal is updated from the measured terminal value at the end of a run, instead of the whole output trajectory. Although the reference terminal point is iteration‐varying, the new adaptive TILC guarantees that the tracking error converges to zero iteratively. Both rigorous mathematical analysis and simulation results confirm the applicability and effectiveness of the proposed approach.

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Section: Problem Formulationmentioning

confidence: 99%

Adaptive Terminal ILC for Iteration‐varying Target Points

Chi¹,

Wang²,

Lewis³

et al. 2014

Asian Journal of Control

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“…There are several types of ILC . It can be categorized into continuous time or discrete time ILC , depending on the representation of the system model. It can also be categorized into time domain or frequency domain , depending on the analysis and implementation of the ILC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement for Rigid Tapping by Iterative Learning Control

Chen

Wen

Zhao

2018

Asian Journal of Control

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In this study, an iterative learning control (ILC) algorithm is proposed to improve synchronous errors in rigid tapping. In rigid tapping, the displacements of the z-axis and spindle must be kept synchronous to prevent damage. Using learning control provides better commands for both the z-axis and spindle dynamics, improving the synchronicity of the output responses of the z-axis and spindle. The proposed ILC makes use of synchronous errors in the previous cycle of tapping to modify the current position commands of both the z-axis and spindle. A systematic algorithm is proposed for the computation of learning gains that guarantee the monotonic convergence of synchronous errors. A systematic procedure of applying ILC to rigid tapping is also proposed, where the ideas of effective learning gains and stop learning criteria are discussed. Experimental results on a tapping machine verify the effectiveness of the proposed ILC algorithm.

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“…A gradientbased optimal ILC scheme is proposed for ensuring robust monotonic convergence [29]. A new semisliding window ILC algorithm is developed for discrete-time LTI systems [30]. Recently, by integrating the technique of linear matrix inequality (LMI), the well-established ∞ norm has been used for deriving monotonical convergence conditions that can be described as LMIs and formulas for the control law design, and the tracking error can be ensured to converge monotonically in the sense of L 2 norm [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

Ding

Yang

2014

Abstract and Applied Analysis

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This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

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A guaranteed monotonically convergent iterative learning control

Cited by 8 publications

References 29 publications

Adaptive Terminal ILC for Iteration‐varying Target Points

Adaptive Terminal ILC for Iteration‐varying Target Points

Performance Enhancement for Rigid Tapping by Iterative Learning Control

Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

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