Fuzzy logic and genetic algorithms for intelligent control of structures using MR dampers

Yan, Gang; Zhou, Lily L.

doi:10.1117/12.540382

Cited by 2 publications

(2 citation statements)

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“…Most well‐known and frequently adopted control algorithms are based on optimal control forces calculated by minimizing some cost functions such as linear quadratic regulator and LQG: Linear Quadratic Gaussian (H 2 /LQG) . Another approach to produce a required optimal control force is adaptive control based on the machine‐learning method such as neural network (NN) and genetic algorithm (GA) ). The NN‐based method uses inverse dynamics model of a magnetorheological (MR) damper and training of NN models based on the input–output relationships.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized output feedback polynomial control of seismically excited structures using genetic algorithm

Cha

Agrawal

2011

Struct. Control Health Monit.

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SUMMARY This paper presents novel decentralized output feedback control strategy for the active and semi‐active controls of the highway bridge benchmark phases I and II problems subjected to earthquake ground motions. The control force is calculated using two third‐order polynomial equations expressing a direct relationship between displacement and velocity of the control device and the control force. An advanced implicit redundant representation genetic algorithm is utilized to determine optimal coefficients of the two polynomial equations by minimizing the sum of three evaluation criteria for six prescribed earthquakes. The performance of the proposed controller is evaluated in terms of a set of prescribed 21 evaluation criteria and is compared with the other several control strategies published in the special issue of the benchmark problem. The results show that the proposed decentralized output feedback polynomial control strategy can achieve significant response reductions in the bridge system, and it is evidently superior to sample control strategies and other suggested active and semi‐active controls for the phase I problem and is quite competitive with respect to the sample and other semi‐active control approaches for the fully isolated phase II problem. Copyright © 2011 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

“…Hence, additional feedback algorithms may be needed to produce required actuator/damper forces. Similar to NN models, fuzzy logic control (FLC) is also applied to structural control . However, FLC has some disadvantages in the determination of parameters such as membership functions, control rules, and inadequate stability analysis.…”

Section: Introductionmentioning

confidence: 99%