Evolutionary approach for path following optimal control of multibody systems
Evolutionary approach for path following optimal control of multibody systems
An approach to the determination of approximate solutions of path following optimal control problems by exploiting evolutionary optimization techniques is proposed. Such an approach enables modeling and solution of a wide class of path following optimal control problems, arising in engineering practice, within a unified framework of constrained optimization techniques, including: implementation of genetic algorithms for global optimization and multiobjective control, and utilization of parallel processing to alleviate the computational burden in high dimensional optimal control problems. Computer realization of the proposed method is mainly based on MATLAB simulation programs for constrained optimization and genetic algorithms.
512-516
Dakev, Nikolay V.
f91c4332-e605-43b6-964a-14efb5ec4248
Chipperfield, Andrew J.
524269cd-5f30-4356-92d4-891c14c09340
Fleming, Peter J.
6e1fa975-88d3-477a-8e95-53dec4a041e3
22 May 1996
Dakev, Nikolay V.
f91c4332-e605-43b6-964a-14efb5ec4248
Chipperfield, Andrew J.
524269cd-5f30-4356-92d4-891c14c09340
Fleming, Peter J.
6e1fa975-88d3-477a-8e95-53dec4a041e3
Dakev, Nikolay V., Chipperfield, Andrew J. and Fleming, Peter J.
(1996)
Evolutionary approach for path following optimal control of multibody systems.
In Proceedings of the IEEE Conference on Evolutionary Computation.
.
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Conference or Workshop Item
(Paper)
Abstract
An approach to the determination of approximate solutions of path following optimal control problems by exploiting evolutionary optimization techniques is proposed. Such an approach enables modeling and solution of a wide class of path following optimal control problems, arising in engineering practice, within a unified framework of constrained optimization techniques, including: implementation of genetic algorithms for global optimization and multiobjective control, and utilization of parallel processing to alleviate the computational burden in high dimensional optimal control problems. Computer realization of the proposed method is mainly based on MATLAB simulation programs for constrained optimization and genetic algorithms.
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Published date: 22 May 1996
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Local EPrints ID: 470251
URI: http://eprints.soton.ac.uk/id/eprint/470251
PURE UUID: 68bfb2e8-a9d8-434a-abaa-16d95d06c4a1
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Date deposited: 05 Oct 2022 16:34
Last modified: 23 Feb 2023 02:45
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Contributors
Author:
Nikolay V. Dakev
Author:
Peter J. Fleming
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