Iterative learning control for discrete time systems using optimal feedback and feedforward actions

Amann, N.; Owens, D.H.; Rogers, Eric

doi:10.1109/cdc.1995.480384

Cited by 26 publications

(17 citation statements)

References 12 publications

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“…Finally, the convergence of the output tracking error can be established as, using equation (2) Amann et al 1995, Xu 1997, Longman 1998, Wang 1998b, Cheah and Wang 1998a for di erent systems and applications.…”

Section: Proof Of Theoremmentioning

confidence: 99%

On D-type and P-type ILC designs and anticipatory approach

Wang

2000

International Journal of Control

114

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Many schemes of iterative learning control (ILC) have been developed for continuous-time, non-linear dynamic systems to improve tracking performance. Two schemes, D-type and P-type, have been the bases for many ILC designs. Recently, the anticipatory ILC scheme has been introduced, on the basis of a di erent approach (Wang 1998a(Wang , 1999. In this paper, these basic schemes are compared from both analysis and implementation view points. The anticipatory ILC scheme is designed on the basis of a causal pair of the action taken and its resulting state variables. This approach has the anticipatory characteristics of the D-type ILC and the simplicity for implementation of P-type ILCs. A sampled-data ILC scheme is presented as another form of this anticipatory ILC scheme. Furthermore, control device saturation is taken into account and tracking error convergence results are established, with proofs. The convergence results are also provided in the presence of uncertainties, disturbances and measurement noises. Experimental results are presented to show the e ectiveness of this scheme.

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Section: Proof Of Theoremmentioning

confidence: 99%

On D-type and P-type ILC designs and anticipatory approach

Wang

2000

International Journal of Control

114

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“…In order to guarantee this convergence, many ILC methods use the time derivative of the error signal to adapt the input [1,2,14,15]. The dual system optimization approach is used in [1,17]. In [9], an approach is presented that does not use the error derivative to update the control signal and is based on the use of a set of basis functions derived from a target trajectory.…”

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confidence: 99%

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confidence: 99%

A Robust Iterative Learning Control Algorithm for Uncertain Power Systems

Vassilaki

2016

Electricity Distribution

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Learning control is an iterative approach to the problem of output tracking for processes that are repetitive in nature and operate over fixed time intervals. The adaptability of the control law to changes of model parameters, to unmodelled dynamics and to nonlinearities of real systems has been proven valuable for applications in management and control of components of production and distribution networks. The ILC problem is converted to the design problem of the linear approximation parameters of the input function in a subspace spanned by a set of basis functions. Results on positive invariant sets of linear discrete-time systems allow for the development of a robust ILC algorithm that deals with the uncertainties in the systems model and the restriction of the input and output spaces. The method is presented in view of applications in the management and control of components of production and distribution networks such as Active Power Filters, Inverters for interfacing with renewable energies microgrids and wind turbines.

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“…Using an observation made in [3], this cost functional can be converted into a lifted domain cost functional:…”

Section: A General Formulationmentioning

confidence: 99%

“…For a norm optimal ILC controller (see, e.g., [3], [10], [14]), it is recognised that it has some robustness against model uncertainty. To quantify the allowable uncertainty, tools have been developed in [9], [11], [13].…”

Section: Introductionmentioning

confidence: 99%

A design approach for noncausal robust Iterative Learning Control using worst case disturbance optimisation

Donkers

Wijdeven

Bosgra

2008

2008 American Control Conference

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, O. H. (2008). A design approach for noncausal robust iterative learning control using worst case disturbance optimisation. In Proceedings of the 2008 American Control Conference (ACC2008), Seattle, Washington, USA, June 11-13, 2008 (pp. 4567-4572 Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nl providing details and we will investigate your claim. {m.c.f.donkers,j.j.m.v.d.wijdeven,o.h.bosgra}@tue.nl Abstract-In this paper, we present a novel Iterative Learning Control (ILC) strategy that is robust against model uncertainty, as given by a system model and an additive uncertainty bound. The design methodology hinges on H∞ optimisation, however, the procedure is modified such that the ILC controller is noncausal and inherently acts on a finite time interval. The resulting controller has the structure of a norm optimal ILC controller, so that robustness can be easily assessed. Furthermore, in an example, we show that the presented robust ILC controller can outperform linear quadratic ILC controllers.

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Iterative learning control for discrete time systems using optimal feedback and feedforward actions

Cited by 26 publications

References 12 publications

On D-type and P-type ILC designs and anticipatory approach

On D-type and P-type ILC designs and anticipatory approach

A Robust Iterative Learning Control Algorithm for Uncertain Power Systems

A design approach for noncausal robust Iterative Learning Control using worst case disturbance optimisation

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