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Research Papers

Genetic Algorithm Based Optimum Semi-Active Control of Building Frames Using Limited Number of Magneto-Rheological Dampers and Sensors

[+] Author and Article Information
Vishisht Bhaiya

Department of Civil Engineering,
MNIT,
Jaipur 302017, India
e-mail: vishishtbhaiya@gmail.com

S. D. Bharti

Professor
Department of Civil Engineering,
MNIT,
Jaipur 302017, India
e-mail: sdbharti@mnit.ac.in

M. K. Shrimali

Professor
Department of Civil Engineering,
MNIT,
Jaipur 302017, India
e-mail: shrimlaimk@gmail.com

T. K. Datta

Emeritus Professor
IIT Delhi,
Adjunct Faculty,
NCDMM, MNIT,
Jaipur 302017, India
e-mail: tushar_k_datta@yahoo.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received September 5, 2017; final manuscript received May 2, 2018; published online May 28, 2018. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 140(10), 101013 (May 28, 2018) (13 pages) Paper No: DS-17-1444; doi: 10.1115/1.4040213 History: Received September 05, 2017; Revised May 02, 2018

Optimum semi-active control with a limited number of magneto-rheological (MR) dampers and measurement sensors has certain requirements. Most important of them is the accurate estimation of control forces developed in the MR dampers from the observations made in the structure. Therefore, the observation strategy should form an integral part of the optimization problem. The existing literature on the subject does not address this issue properly. The paper presents a computationally efficient optimization scheme for semi-active control of partially observed building frames using a limited number of MR dampers and sensors for earthquakes. The control scheme duly incorporates the locations of measurement sensors as variables into the genetic algorithm (GA) based optimization problem. A ten-storied building frame is taken as an illustrative example. The optimum control strategy utilizes two well-known control laws, namely, the linear quadratic Gaussian (LQG) with clipped optimal control and the bang-bang control to find the time histories of voltage to be applied to the MR dampers. The results of the numerical study show that the proposed scheme of sensor placement provides the optimum reduction of response with more computational efficiency. Second, optimal locations of sensors vary with the response quantities to be controlled, the nature of earthquake, and the control algorithm. Third, optimal locations of MR dampers are invariant of the response quantities to be controlled and the nature of earthquake.

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Figures

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Fig. 1

Building equipped with MR dampers and sensors

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Fig. 2

Comparison of percentage reductions in response quantities obtained by bang-bang algorithm and clipped on optimal algorithm for optimally placed three MR dampers and five sensors for real earthquake records

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Fig. 3

Comparison between control forces: (a) developed in MR damper at first story with strategy 1, (b) obtained by LQG, and (c) obtained by LQR for El Centro and Spitak earthquake

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Fig. 4

Comparison between control forces obtained through optimally placed three MR dampers and five sensors: (a) full state observation, (b) strategy 1, and (c) strategy 2 for El Centro earthquake

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Fig. 5

Comparison between control forces obtained through optimally placed three MR dampers and five sensors: (a) full state observation, (b) strategy 1, and (c) strategy 2 for Spitak earthquake

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Fig. 6

Number of iterations (generations) required by GA for each objective function for strategies 1 and 2 for clipped optimal control and bang-bang control

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Fig. 7

Comparison between maximum percentage reductions obtained by using different numbers of MR dampers for broadband, narrowband excitation, and Spitak earthquake

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Fig. 8

Variation of optimum percentage reductions of responses (for optimal sensor locations) with number of sensors for the cases of 3 and 4 MR dampers (broadband excitation)

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Fig. 9

Variation of optimum percentage reductions of responses (for optimal sensor locations) with number of sensors for the cases of 3 and 4 MR dampers (narrowband excitation)

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Fig. 10

Variation of optimum percentage reductions of responses (for optimal sensor locations) with number of sensors for the cases of 3 and 4 MR dampers (Spitak earthquake)

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Fig. 11

Pictorial view of optimal locations for the case of four sensors (for El Centro earthquake using bang-bang control) for the control of (a) peak top floor displacement, (b) maximum inter-story drift, and (c) maximum base shear

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