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Stabilization of Differential Repetitive Processes

Stabilization of Differential Repetitive Processes
Stabilization of Differential Repetitive Processes
Differential repetitive processes are a subclass of 2D systems that arise in modeling
physical processes with identical repetitions of the same task and in the analysis of other
control problems such as the design of iterative learning control laws. These models have
proved to be efficient within the framework of linear dynamics, where control laws designed in
this setting have been verified experimentally, but there are few results for nonlinear dynamics.
This paper develops new results on the stability, stabilization and disturbance attenuation,
using an H? norm measure, for nonlinear differential repetitive processes. These results are
then applied to design iterative learning control algorithms under model uncertainty and sensor
failures described by a homogeneous Markov chain with a finite set of states. The resulting
design algorithms can be computed using linear matrix inequalities.
786-800
Emelianov, M A
e0b9b7c0-d81b-4610-8939-f85f6614e3a5
Pakshin, P V
b49d7402-75eb-4915-9106-870574fb7c60
Galkowski, K
65b638be-b5a5-4e25-b1b8-e152c08a1cbb
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72
Emelianov, M A
e0b9b7c0-d81b-4610-8939-f85f6614e3a5
Pakshin, P V
b49d7402-75eb-4915-9106-870574fb7c60
Galkowski, K
65b638be-b5a5-4e25-b1b8-e152c08a1cbb
Rogers, E
611b1de0-c505-472e-a03f-c5294c63bb72

Emelianov, M A, Pakshin, P V, Galkowski, K and Rogers, E (2015) Stabilization of Differential Repetitive Processes. Automation and Remote Control, 76 (5), 786-800.

Record type: Article

Abstract

Differential repetitive processes are a subclass of 2D systems that arise in modeling
physical processes with identical repetitions of the same task and in the analysis of other
control problems such as the design of iterative learning control laws. These models have
proved to be efficient within the framework of linear dynamics, where control laws designed in
this setting have been verified experimentally, but there are few results for nonlinear dynamics.
This paper develops new results on the stability, stabilization and disturbance attenuation,
using an H? norm measure, for nonlinear differential repetitive processes. These results are
then applied to design iterative learning control algorithms under model uncertainty and sensor
failures described by a homogeneous Markov chain with a finite set of states. The resulting
design algorithms can be computed using linear matrix inequalities.

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Published date: 2015
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 381089
URI: http://eprints.soton.ac.uk/id/eprint/381089
PURE UUID: 53532be4-7557-4a00-9dbf-183c59dd3240
ORCID for E Rogers: ORCID iD orcid.org/0000-0003-0179-9398

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Date deposited: 30 Aug 2015 08:32
Last modified: 03 Dec 2019 02:04

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