Modal-space reference-model-tracking fuzzy control of earthquake excited structures

Park, Kwan-Soon; Ok, Seung-Yong

doi:10.1016/j.jsv.2014.09.009

Cited by 19 publications

(17 citation statements)

References 18 publications

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“…Earthquake is an example of a natural phenomenon that influences the dynamics of a structures and building. Dynamic behavior of structures in the presence of earthquakes has been extensively studies in [21][22][23][24][25][26][27]. In the mentioned studies, the dynamic behavior of the structures has been investigated under the influence of a particular earthquake.…”

Section: Introductionmentioning

confidence: 99%

A Case Study on Designing a Sliding Mode Controller to Stabilize the Stochastic Effect of Noise on Mechanical Structures: Residential Buildings Equipped with ATMD

Azizi

2020

Complexity

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This study aims to stabilize the unwanted fluctuation of buildings as mechanical structures subjected to earth excitation as the noise. In this study, the ground motion is considered as a Wiener process, in which the governing stochastic differential equations have been presented in the form of Ito equation. To stabilize the vibration of the system, the ATMD system is considered and located on the upmost story of the building. A sliding mode controller has been utilized to control the ATMD system, which is a robust controller in the presence of uncertainty. For this purpose, the design of a sliding mode controller for the general dynamic system with Lipschitz nonlinearity and considering the Ito relations has been accomplished. The mentioned design has been implemented considering the presence of the Weiner process and existence of uncertainty in the structure and actuator. Then, the obtained general control law has been generalized to control the ATMD system. The results show that the designed controller is effective to reduce the effect of the unwanted impused vibrations on the building.

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Section: Introductionmentioning

confidence: 99%

A Case Study on Designing a Sliding Mode Controller to Stabilize the Stochastic Effect of Noise on Mechanical Structures: Residential Buildings Equipped with ATMD

Azizi

2020

Complexity

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“…e seismic response control problem of two neighboring buildings has been successfully addressed by introducing a method of interconnecting two adjacent buildings with control devices such as linear passive [1][2][3], nonlinear passive [4][5][6], active [7][8][9], semiactive [10][11][12], and hybrid [13,14] systems. is is due to the fact that there exists optimal control capacity in reducing the responses of the neighboring parallel structures interconnected with the control device [1,4].…”

Section: Introductionmentioning

confidence: 99%

“…In [8], we proposed a preference-based design approach to balance several design objectives such as interstory drift, instantaneous control force, instantaneous control power, and total control energy when these design objectives are mutually conflicting. In [9], we proposed a reference-model-tracking fuzzy control technique where the reference model is an ideal desirable model constructed by assigning the target damping ratios to the first few dominant modes in modal space, and the fuzzy control is able to make the structural behavior track the desirable behavior of the reference model. In [13], we dealt with the hybrid control system consisting of passive damper and tendon-type active device where the active devices are installed inside the buildings and the passive dampers are used as the link members.…”

Section: Introductionmentioning

confidence: 99%

Coupling ATMD System for Seismic Response Control of Two Adjacent Buildings

Park

2019

Shock and Vibration

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This study deals with optimal control of the coupling active tuned mass damper (ATMD) system for two neighboring structures under earthquake excitations. It also investigates the seismic performances of the proposed system including the issues on actuator failure problems. The conventional control approach is to use two ATMDs independently where each ATMD is individually installed at each structure without any connection between two structures. Since this uncoupling configuration does not allow transferring its control force to the other structure, it cannot adapt to emergency situation in which one of the two actuators becomes inoperable due to the power cuts or outages. On the other hand, the proposed control approach is to use a coupling ATMD system where two ATMDs with one mass being shared are installed to interconnect two structures. Hence, the proposed coupling ATMD system can transmit the control force of the remaining actuator to another structure through the interconnection configuration so that it can adapt to the one-side actuator failure. In a numerical example, the conventional independent and newly proposed coupling systems have been optimally designed to have similar control performance in normal operational conditions, and their performances have been compared to each other under the malfunction conditions of one-side actuator. Their comparative results verified the outstanding adaptive performance of the proposed coupling ATMD system over the independent ATMD system. The independent ATMD system showed considerably degraded control performance compared to its own normal control performance, whereas the proposed system exhibits much improved control performance over the independent ATMD system by fully utilizing the other normally operating actuators through the coupling configuration.

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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%

“…25,26 It has been successfully employed for online control of active structural control systems. [27][28][29][30] However, performance of the fuzzy gain-scheduling controller is very sensitive to the parameters of the fuzzy controller. In this case, a fuzzy gainscheduling controller tuned for a given disturbance may not work optimally in confrontation with the other disturbances.…”

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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Modal-space reference-model-tracking fuzzy control of earthquake excited structures

Cited by 19 publications

References 18 publications

A Case Study on Designing a Sliding Mode Controller to Stabilize the Stochastic Effect of Noise on Mechanical Structures: Residential Buildings Equipped with ATMD

A Case Study on Designing a Sliding Mode Controller to Stabilize the Stochastic Effect of Noise on Mechanical Structures: Residential Buildings Equipped with ATMD

Coupling ATMD System for Seismic Response Control of Two Adjacent Buildings

Online control of an active seismic system via reinforcement learning

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