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Discrete Fourier Transform based Iterative Learning Control Design for Linear Plants with Experimental Verification

Discrete Fourier Transform based Iterative Learning Control Design for Linear Plants with Experimental Verification
Discrete Fourier Transform based Iterative Learning Control Design for Linear Plants with Experimental Verification
A general class of iterative learning control law is examined using the Discrete Fourier Transform and it is shown that if the nominal plant satisfies a given uncertainty condition, there exist algorithms that are capable of driving the tracking error monotonically to zero. The effect of the filters appearing in the algorithm on the convergence rate is then examined using a bi-linear mapping into the space of plant uncertainty. A weighting function for the convergence rate is subsequently specified as a design parameter, and it is shown that the filters can be chosen to maximise the weighted convergence rate over a given region of uncertainty space. This permits the designer to manipulate the convergence and robustness properties of the algorithm in a straightforward manner. It is then demonstrated how the change of input over successive trials and the residual error may also be incorporated into the cost function. Experimental results are presented using a non-minimum phase test facility to show the effectiveness of the design method.
0022-0434
031006-1-031006-10
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Owens, David
0d0eb9da-c362-457d-841f-3094a18120ee
Hatonen, Jari
e1456ed4-4f7e-4bf3-bd7f-692c3ccf6b33
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Owens, David
0d0eb9da-c362-457d-841f-3094a18120ee
Hatonen, Jari
e1456ed4-4f7e-4bf3-bd7f-692c3ccf6b33

Freeman, Christopher, Lewin, Paul, Rogers, Eric, Owens, David and Hatonen, Jari (2009) Discrete Fourier Transform based Iterative Learning Control Design for Linear Plants with Experimental Verification. Journal of Dynamic Systems, Measurement and Control, 131 (3), 031006-1-031006-10.

Record type: Article

Abstract

A general class of iterative learning control law is examined using the Discrete Fourier Transform and it is shown that if the nominal plant satisfies a given uncertainty condition, there exist algorithms that are capable of driving the tracking error monotonically to zero. The effect of the filters appearing in the algorithm on the convergence rate is then examined using a bi-linear mapping into the space of plant uncertainty. A weighting function for the convergence rate is subsequently specified as a design parameter, and it is shown that the filters can be chosen to maximise the weighted convergence rate over a given region of uncertainty space. This permits the designer to manipulate the convergence and robustness properties of the algorithm in a straightforward manner. It is then demonstrated how the change of input over successive trials and the residual error may also be incorporated into the cost function. Experimental results are presented using a non-minimum phase test facility to show the effectiveness of the design method.

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Published date: 1 May 2009
Organisations: EEE, Southampton Wireless Group

Identifiers

Local EPrints ID: 265317
URI: https://eprints.soton.ac.uk/id/eprint/265317
ISSN: 0022-0434
PURE UUID: 289ed437-5386-4254-bff4-217f7355ffec
ORCID for Eric Rogers: ORCID iD orcid.org/0000-0003-0179-9398

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Date deposited: 11 Mar 2008 18:22
Last modified: 20 Jul 2019 01:23

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