• Title/Summary/Keyword: Groundhook

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Application of Semi-active TMD for Vibration Control of Floor Slab (바닥판 구조물의 진동제어를 위한 준능동 TMD의 적용)

  • Kim, Gee-Cheol;Kang, Joo-Won
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2007.04a
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    • pp.607-612
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    • 2007
  • A conventional passive TMD is only effective when it is tuned properly. In many practical applications, inevitable off-tuning of a TMD occurs because the mass in a building floor could change by moving furnishings, people gathering, etc. When TMDs are off tuned, TMDs their effectiveness is sharply reduced. This paper discusses the application of MR-TMD, semi-active damper, for the reduction of floor vibrations due to machine and human movements. Here, the groundhook and skyhook algorithm are applied to a single degree of freedom system representative of building floors. And displacement and velocity base control method are applied to reduce t100r vibration. The performance of the STMD is compared to that of the equivalent passive TMD. Comparison of the results demonstrates the efficiency and robustness of STMD with respect to equivalent TMD.

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Development of Control Algorithm for Semi-active TMD using MOGA (MOGA를 이용한 준능동 TMD 제어알고리즘 개발)

  • Kim, Hyun-Su;Kang, Joo-Won;Kim, Gee-Cheol
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2010.04a
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    • pp.331-334
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    • 2010
  • 본 논문에서는 준능동 TMD가 설치된 고층건물의 풍응답을 효과적으로 저감시키기 위하여 다목적 유전자알고리즘(MOGA)을 이용한 퍼지관리제어기를 개발하였다. 퍼지관리제어기는 하위제어기인 그라운드훅(groundhook) 제어알고리즘과 스카이훅(skyhook) 제어알고리즘에 의해서 결정된 제어명령을 적절하게 하나로 합치는 역할을 한다. 다목적 유전자알고리즘의 최적화 과정에서 75층의 가속도 응답과 준능동 TMD의 변위응답을 목적함수로 사용하였다. 다목적 유전자알고리즘 최적화과정을 통하여 퍼지관리제어기의 파레토 최적해집합을 효과적으로 얻을 수 있었다. 다목적 유전자알고리즘에 의하여 개발된 퍼지관리제어기는 가중합방법의 제어기보다 매우 우수한 성능을 나타내었다.

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Vibration control of an SDOF structure using semi-active tuned mass damner (준능동 TMD를 이용한 단자유도 구조물의 진동제어)

  • Kim, Hyun-Su;Lee, Dong-Guen
    • Proceedings of the Earthquake Engineering Society of Korea Conference
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    • 2006.03a
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    • pp.424-431
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    • 2006
  • Many types of tuned mass dampers (TMDs), such as active TMDs, multiple TMDs, hybrid TMDs etc., have been studied to effectively reduce the dynamic responses of a structure subjected to various types of dynamic loads. In this study, we replace a passive damper by a semi-active tuned mass damper to improve the control performance of conventional TMDs (STMD). An idealized variable damping device is used as semi-active dampers. These semi-active dampers can change the properties of TMDs in real time based on the dynamic responses of a structure. The control performance of STMD is investigated with respect to various types of excitation by numerical simulation. Groundhook control algorithm is used to appropriately modulate the damping force of semi-active dampers. The control effectiveness between STMD and a conventional passive TMD, both under harmonic and random excitations, is evaluated and compared for a single-degree-of-freedom (SDOF) structure. Excitations are applied to the structure as a dynamic force and ground motion, respectively. The numerical studies showed that the control effectiveness of STMD is significantly superior to that of the passive TMD, regardless of the type of excitations.

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Performance Evaluation of Reinforcement Learning Algorithm for Control of Smart TMD (스마트 TMD 제어를 위한 강화학습 알고리즘 성능 검토)

  • Kang, Joo-Won;Kim, Hyun-Su
    • Journal of Korean Association for Spatial Structures
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    • v.21 no.2
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    • pp.41-48
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    • 2021
  • A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.

Experimental Evaluation of Seismic Response Control Performance of Smart TMD (스마트 TMD의 지진응답 제어성능 실험적 검토)

  • Kang, Joo-Won;Kim, Hyun-Su
    • Journal of Korean Association for Spatial Structures
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    • v.22 no.3
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    • pp.49-56
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    • 2022
  • Tuned mass damper (TMD) is widely used to reduce dynamic responses of structures subjected to earthquake loads. A smart tuned mass damper (STMD) was proposed to increase control performance of a traditional passive TMD. A lot of research was conducted to investigate the control performance of a STMD based on analytical method. Experimental study of evaluation of control performance of a STMD was not widely conducted to date. Therefore, seismic response reduction capacity of a STMD was experimentally investigated in this study. For this purpose, a STMD was manufactured using an MR (magnetorheological) damper. A simple structure presenting dynamic characteristics of spacial roof structure was made as a test structure. A STMD was made to control vertical responses of the test structure. Two artificial ground motions and a resonance harmonic load were selected as experimental seismic excitations. Shaking table test was conducted to evaluate control performance of a STMD. Control algorithms are one of main factors affect control performance of a STMD. In this study, a groundhook algorithm that is a traditional semi-active control algorithm was selected. And fuzzy logic controller (FLC) was used to control a STMD. The FLC was optimized by multi-objective genetic algorithm. The experimental results presented that the TMD can effectively reduce seismic responses of the example structures subjected to various excitations. It was also experimentally shown that the STMD can more effectively reduce seismic responses of the example structures conpared to the passive TMD.