Optimal Sensors and Actuators Placement for Large-Scale Unstable Systems via Restricted Genetic Algorithm

Masoud Seyyed Sakha, Hamid Reza Shaker

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

One of the fundamental problems in control systems engineering is the problem of sensors and actuators placement.
Decisions in this context, play a key role in the success of control process. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive. The computational burden is significant in particular for large-scale systems. In this paper, we develop a new technique for placing sensor and actuator in large-scale systems by using Restricted Genetic Algorithm (RGA). The RGA is a kind of genetic algorithm which is developed specifically for sensors and actuator placement. Unlike the other counterparts, the method not only supports unstable systems but also reduces the computational complexity significantly. The method is illustrated by numerical examples.
Original languageEnglish
JournalEngineering Computations
Volume34
Issue number8
Pages (from-to)2582-2597
ISSN0264-4401
DOIs
Publication statusPublished - 2017

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Large scale systems
Actuators
Genetic algorithms
Sensors
Systems engineering
Computational complexity
Control systems

Cite this

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title = "Optimal Sensors and Actuators Placement for Large-Scale Unstable Systems via Restricted Genetic Algorithm",
abstract = "One of the fundamental problems in control systems engineering is the problem of sensors and actuators placement.Decisions in this context, play a key role in the success of control process. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive. The computational burden is significant in particular for large-scale systems. In this paper, we develop a new technique for placing sensor and actuator in large-scale systems by using Restricted Genetic Algorithm (RGA). The RGA is a kind of genetic algorithm which is developed specifically for sensors and actuator placement. Unlike the other counterparts, the method not only supports unstable systems but also reduces the computational complexity significantly. The method is illustrated by numerical examples.",
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Optimal Sensors and Actuators Placement for Large-Scale Unstable Systems via Restricted Genetic Algorithm. / Seyyed Sakha, Masoud; Shaker, Hamid Reza.

In: Engineering Computations, Vol. 34, No. 8, 2017, p. 2582-2597.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - Optimal Sensors and Actuators Placement for Large-Scale Unstable Systems via Restricted Genetic Algorithm

AU - Seyyed Sakha, Masoud

AU - Shaker, Hamid Reza

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AB - One of the fundamental problems in control systems engineering is the problem of sensors and actuators placement.Decisions in this context, play a key role in the success of control process. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive. The computational burden is significant in particular for large-scale systems. In this paper, we develop a new technique for placing sensor and actuator in large-scale systems by using Restricted Genetic Algorithm (RGA). The RGA is a kind of genetic algorithm which is developed specifically for sensors and actuator placement. Unlike the other counterparts, the method not only supports unstable systems but also reduces the computational complexity significantly. The method is illustrated by numerical examples.

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