Modified Newton method based iterative learning control design for discrete nonlinear systems with constraints
Modified Newton method based iterative learning control design for discrete nonlinear systems with constraints
This paper considers the design of iterative learning control laws for classes of nonlinear dynamics. In particular, a new Newton method design is developed for nonlinear discrete systems in the presence of input constraints, where such constraints will arise in applications. The new design is based on the use of a penalty function and an iterative method for solving an unconstrained nonlinear optimization problem with an algorithm that has monotonic and super linear convergence characteristics. In this new algorithm the input inequality constraints are transformed into equality form by adding auxiliary variables. A cost function is then minimized to produce the new iterative learning control law design. Finally, a simulation based case study is given to illustrate the performance of the new design.
35-43
Tao, Hong-feng
ae828796-c320-48d1-b325-2ab19ff7708f
Paszke, Wojciech
cb0ed465-63b4-4165-8606-fe76dc7f4752
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef
Yang, Hui-zhong
78278031-3463-494f-907a-9b5de0602407
August 2018
Tao, Hong-feng
ae828796-c320-48d1-b325-2ab19ff7708f
Paszke, Wojciech
cb0ed465-63b4-4165-8606-fe76dc7f4752
Rogers, Eric
611b1de0-c505-472e-a03f-c5294c63bb72
Galkowski, Krzysztof
322994ac-7e24-4350-ab72-cc80ac8078ef
Yang, Hui-zhong
78278031-3463-494f-907a-9b5de0602407
Tao, Hong-feng, Paszke, Wojciech, Rogers, Eric, Galkowski, Krzysztof and Yang, Hui-zhong
(2018)
Modified Newton method based iterative learning control design for discrete nonlinear systems with constraints.
Systems & Control Letters, 118, .
(doi:10.1016/j.sysconle.2018.05.007).
Abstract
This paper considers the design of iterative learning control laws for classes of nonlinear dynamics. In particular, a new Newton method design is developed for nonlinear discrete systems in the presence of input constraints, where such constraints will arise in applications. The new design is based on the use of a penalty function and an iterative method for solving an unconstrained nonlinear optimization problem with an algorithm that has monotonic and super linear convergence characteristics. In this new algorithm the input inequality constraints are transformed into equality form by adding auxiliary variables. A cost function is then minimized to produce the new iterative learning control law design. Finally, a simulation based case study is given to illustrate the performance of the new design.
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Accepted/In Press date: 10 May 2018
e-pub ahead of print date: 14 June 2018
Published date: August 2018
Identifiers
Local EPrints ID: 421835
URI: http://eprints.soton.ac.uk/id/eprint/421835
ISSN: 0167-6911
PURE UUID: 4f0c1463-a732-4a89-95c4-8cc223bbca16
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Date deposited: 29 Jun 2018 16:30
Last modified: 16 Mar 2024 06:45
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Contributors
Author:
Hong-feng Tao
Author:
Wojciech Paszke
Author:
Eric Rogers
Author:
Krzysztof Galkowski
Author:
Hui-zhong Yang
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