A Robust Approach to Iterative Learning Control Design for Uncertain Systems

Moon, Jung-Ho; Doh, Tae-Yong; Chung, Myung Jin

doi:10.1016/s0005-1098(98)00028-4

Cited by 76 publications

(46 citation statements)

References 7 publications

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“…ILC was initially developed as a feedforward action applied directly to the open-loop system [2], [3], [6]. Several closed-loop ILC schemes were later developed in order to benefit from the feedback properties in the first iteration, e.g., [1], [7], [11], [15], and [22].…”

mentioning

confidence: 99%

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Robust Iterative Learning Control Design: Application to a Robot Manipulator

Tayebi

Abdul

Zaremba

et al. 2008

IEEE/ASME Trans. Mechatron.

102

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Abstract-This paper deals with robust iterative learning control design for uncertain single-input-single-output linear time-invariant systems. The design procedure is based upon solving the robust performance condition using the Youla parameterization and the µ-synthesis approachto obtain a feedback controller. Thereafter, a convergent iterative learning law is obtained by using the performance weighting function involved in the robust performance condition. Experimental results, on a CRS465 robot manipulator, are provided to illustrate the effectiveness of the proposed design method.

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mentioning

confidence: 99%

“…The H ∞ and µ-synthesis approaches were used in [7] and [15] to design the learning filters, under model uncertainties, assuming that the feedback controller is already available. A two-step procedure based on the H ∞ optimization was proposed in [1] to design the feedback and learning controllers.…”

mentioning

confidence: 99%

Robust Iterative Learning Control Design: Application to a Robot Manipulator

Tayebi

Abdul

Zaremba

et al. 2008

IEEE/ASME Trans. Mechatron.

102

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“…However, if the system is integrator or unstable to open loop, or well, it has wrong initial condition, the ILC scheme to open loop can be inappropriate. Thus, the feedback-based ILC has been suggested in the literature as a more adequate structure ( (Roover, 1996); (Moon et al, 1998); (Doh et al, 1999); (Tayebi & Zaremba, 2003)). The basic idea is shown in Fig.…”

Section: Ilc Overviewmentioning

confidence: 99%

Iterative Learning - MPC: an Alternative Strategy

Adam¹,

González²

2012

Frontiers in Advanced Control Systems

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“…The papers introduce a robust design procedure based on a multiplicative uncertainty model, where Q is chosen as a low-pass filter and L found using a µ-synthesis procedure. Moon et al (1998) and Tayebi and Zeremba (2003) examine ILC as a method to control a plant with an added non-iterative feedback controller. The plant and controller are implemented in a feedback loop and then an IL controller adjusts the plant's input to improve the trial-to-trial performance.…”

Section: Robustness For Ilcmentioning

confidence: 99%

“…Moon et al (1998) and Tayebi and Zeremba (2003) consider the robustness of ILC using this approach (see Section 3.7). Tayebi and Zeremba (2003) the iterative system.…”

Section: Furthering the Robustness For Ilcmentioning

confidence: 99%

Robust stability for iterative learning control

Bradley

French

2009

2009 American Control Conference

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This thesis examines the notion of the long term robust stability of iterative learning control (ILC) systems engaged in trajectory tracking, using a robust stability theorem based on a biased version of the nonlinear gap metric. This is achieved through two main results:The first concerns the establishment of a nonlinear robust stability theorem, where signals are measured relative to a given trajectory. Although primarily motivated by ILC, the theorem provided is applicable to a wider range of problems. This is due to its development being made independently of any particular signal space, provided the space is furnished with a definition of causality. The theorem's formulation therefore permits its implementation on single-or multi-dimensional problems in a variety of different settings. Necessitated by an ILC constraint concerning reference signals, the trajectory that stability is measured relative to must often lie outside the signal space that is chosen. The robust stability theorem is therefore devised to address signals that lie in extended signal spaces throughout. Additionally, the theorem is applicable to nonlinear systems, and it is shown that the biased gap measure collapses to the standard nonlinear gap measure when the bias is set to zero. It is also shown to collapse to the classical linear gap when restricting the analysis to certain linear systems.The second result applies the robust stability theorem to ILC using a 2D signal space.Initially the subject of ILC is reviewed and some of the problems associated with controllers are described; in particular the issues of long-term stability and the criteria for convergence. ILC algorithms expressed in the 'lifted system' or 'supervector' formulation are discussed and then analysed using the biased robust stability theorem. Results are presented regarding the use of filtering in ILC algorithms to aid robustness, and also the robustness of inverse model-based techniques. The robust stability tool applied to ILC in this thesis is not restricted in its analysis to algorithms which are 'causal' (in an ILC sense); and is based on a general unstructured uncertainty model in contrast to the existing literature, whereby uncertainties are typically constrained to additive, multiplicative or parametric models.

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A Robust Approach to Iterative Learning Control Design for Uncertain Systems

Cited by 76 publications

References 7 publications

Robust Iterative Learning Control Design: Application to a Robot Manipulator

Robust Iterative Learning Control Design: Application to a Robot Manipulator

Iterative Learning - MPC: an Alternative Strategy

Robust stability for iterative learning control

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