Active control of earthquake excited structures using fuzzy supervisory technique

Park, Kwan-Soon; Koh, Hyun‐Moo; Ok, Seung-Yong

doi:10.1016/s0965-9978(02)00044-3

Cited by 42 publications

(18 citation statements)

References 9 publications

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“…In the dynamic model of the MR damper, the input voltage varies in the range of 0-10 V. Thus, the output membership function has also a maximum value of 10 V. The defuzzification module then combines a set of fuzzified outputs for all the rules in order Table 3 Fuzzy rule tables to arrive at a single conclusion. Here, we adopt the center of gravity (COG) defuzzification method [39]. For the jth rule of the ith MR damper, the COG method computes the command voltage v i output to be…”

Section: Semi-active Fuzzy Control Strategymentioning

confidence: 99%

Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers

Kim

Park

et al. 2007

Engineering Structures

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Section: Semi-active Fuzzy Control Strategymentioning

confidence: 99%

Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers

Kim

Park

et al. 2007

Engineering Structures

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“…Fuzzy gain scheduling is also an effective approach to control systems with variable dynamics . It has been successfully employed for online control of active structural control systems . However, performance of the fuzzy gain‐scheduling controller is very sensitive to the parameters of the fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Online control of an active seismic system via reinforcement learning

Khalatbarisoltani

Soleymani

Khodadadi

2018

Struct Control Health Monit

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Summary Tuning the seismic control systems in order to achieve optimal performance is a challenging area due to the system and disturbance uncertainties. Although, model uncertainties, process time delay, and actuator dynamics can be considered as typical uncertainties, the main source of uncertainty in a seismic control system comes from the aleatory nature of earthquake disturbances. In this case, tuning of the control system based on a given seismic record may not necessarily result in optimal performance for other earthquakes. In this paper, a methodological approach is proposed for online control of active structural control systems considering seismic uncertainties. For this purpose, the concept of reinforcement learning is utilized for online tuning of an active mass drive system. The controller comprises a gain‐scheduling fuzzy proportional derivative controller whose gains are tuned via an online reinforcement learning algorithm. Moreover, in order to tackle the time delays, a dynamic state predictor is utilized in conjunction with the proposed controller. To evaluate the performance of proposed controller, according to an assumed site hazard, thousand ground motion records are generated and clustered based on their spectral features using a fuzzy clustering approach. Finally, the controller is implemented in a laboratory‐scale structure, and its performance is examined in the presence of the cluster centers and some real seismic records simulated on a shake table. The test results reveal successful performance of the proposed controller in tackling a wide range of seismic disturbances in the presence of time delay.

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“…Several control approaches have been employed for control design of ATMD (Park et al ., ; Samali and Al‐Dawod, ; Choi et al ., ; Guclu and Yazici, ; Mohan Rao and Sivasubramanian, ; Tani et al ., ; Li et al ., ; Bitaraf et al ., ). Among the control methods, fuzzy logic control (FLC) has proven to be quite effective for a number of reasons.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy controller for active tuned mass damper of a benchmark tall building subjected to seismic and wind loads

Soleymani

Khodadadi

2013

Structural Design Tall Build

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SUMMARYActive tuned mass dampers (ATMDs) are one of the most effective solutions for mitigation of destructive effects of earthquakes and strong winds in tall buildings. In order to achieve optimal performance, these systems are designed and tuned to mitigate effect of either wind or earthquake excitation. However, due to different frequency contents and intensities of wind and earthquake excitations, which will cause contrasting structural modes stimulation, the ATMD designed for one of these disturbances may not work optimally for the other one. This paper addresses a methodological simulation approach for adaptive control design of ATMDs in tall buildings located in regions with high level of seismic activity and recurrent strong winds. For this purpose, a multi‐objective adaptive genetic‐fuzzy controller is proposed for the control of an ATMD of a benchmark 76‐story building subjected to wind load and earthquake disturbances. Simulation results reveal that the optimal ATMD designed for earthquake disturbance does not work adequately for wind load disturbance and vice versa. Furthermore, the proposed adaptive controller has superior performance in suppressing base shear and inter‐story drifts induced by wind load and earthquake excitations. Copyright © 2013 John Wiley & Sons, Ltd.

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Active control of earthquake excited structures using fuzzy supervisory technique

Cited by 42 publications

References 9 publications

Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers

Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers

Online control of an active seismic system via reinforcement learning

Adaptive fuzzy controller for active tuned mass damper of a benchmark tall building subjected to seismic and wind loads

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