Genetic approach to optimal topology/controller design of adaptive structures

Xiaojian Liu; D. W. Begg; R. J. Fishwick

doi:10.1002/(SICI)1097-0207(19980315)41:5<815::AID-NME310>3.0.CO;2-O

Genetic approach to optimal topology/controller design of adaptive structures

Xiaojian Liu^*, D. W. Begg, R. J. Fishwick

^*Corresponding author for this work

School of Civil Engineering & Surveying

Research output: Contribution to journal › Article › peer-review

Abstract

Integrated optimization of structural topology and control system is considered. The problem is formulated as mixed discrete-continuous multi-objective programming with a linear quadratic regulator cost index, and measures of robustness and controllability as objectives. The Genetic Algorithm (GA), a guided random search technique, is adopted for the problem-solving. A member elimination strategy that allows deleted members to be recovered is suggested in the search procedure. As verification for the proposed method, optimum layout and actuator placement for a 45-bar truss is illustrated. Numerical results indicate that the genetic algorithm can converge to optimum solutions by searching only a minor fraction of the solution space. Discussions on the algorithm are presented.

Original language	English
Pages (from-to)	815-830
Number of pages	16
Journal	International Journal for Numerical Methods in Engineering
Volume	41
Issue number	5
DOIs	https://doi.org/10.1002/(SICI)1097-0207(19980315)41:5<815::AID-NME310>3.0.CO;2-O
Publication status	Published - 15 Mar 1998

Keywords

Adaptive structures
Riccati equation and genetic algorithm (GA)
Sensor/actuator placement
Structural topology optimization
Structural-control interaction

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10.1002/(SICI)1097-0207(19980315)41:5<815::AID-NME310>3.0.CO;2-O

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title = "Genetic approach to optimal topology/controller design of adaptive structures",

abstract = "Integrated optimization of structural topology and control system is considered. The problem is formulated as mixed discrete-continuous multi-objective programming with a linear quadratic regulator cost index, and measures of robustness and controllability as objectives. The Genetic Algorithm (GA), a guided random search technique, is adopted for the problem-solving. A member elimination strategy that allows deleted members to be recovered is suggested in the search procedure. As verification for the proposed method, optimum layout and actuator placement for a 45-bar truss is illustrated. Numerical results indicate that the genetic algorithm can converge to optimum solutions by searching only a minor fraction of the solution space. Discussions on the algorithm are presented.",

keywords = "Adaptive structures, Riccati equation and genetic algorithm (GA), Sensor/actuator placement, Structural topology optimization, Structural-control interaction",

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year = "1998",

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AU - Liu, Xiaojian

AU - Begg, D. W.

AU - Fishwick, R. J.

PY - 1998/3/15

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N2 - Integrated optimization of structural topology and control system is considered. The problem is formulated as mixed discrete-continuous multi-objective programming with a linear quadratic regulator cost index, and measures of robustness and controllability as objectives. The Genetic Algorithm (GA), a guided random search technique, is adopted for the problem-solving. A member elimination strategy that allows deleted members to be recovered is suggested in the search procedure. As verification for the proposed method, optimum layout and actuator placement for a 45-bar truss is illustrated. Numerical results indicate that the genetic algorithm can converge to optimum solutions by searching only a minor fraction of the solution space. Discussions on the algorithm are presented.

AB - Integrated optimization of structural topology and control system is considered. The problem is formulated as mixed discrete-continuous multi-objective programming with a linear quadratic regulator cost index, and measures of robustness and controllability as objectives. The Genetic Algorithm (GA), a guided random search technique, is adopted for the problem-solving. A member elimination strategy that allows deleted members to be recovered is suggested in the search procedure. As verification for the proposed method, optimum layout and actuator placement for a 45-bar truss is illustrated. Numerical results indicate that the genetic algorithm can converge to optimum solutions by searching only a minor fraction of the solution space. Discussions on the algorithm are presented.

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KW - Riccati equation and genetic algorithm (GA)

KW - Sensor/actuator placement

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KW - Structural-control interaction

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Genetic approach to optimal topology/controller design of adaptive structures

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