Active Control of Structures Using Energy-Based LQR Method

Alavinasab, Ali; Moharrami, Hamid; Khajepour, Amir

doi:10.1111/j.1467-8667.2006.00460.x

Cited by 73 publications

(35 citation statements)

References 21 publications

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“…가중행렬의 결정은 다양한 방 법으로 이루어지며 11) , 본 연구에서는 제어목표 응답을 구 조물의 변위로 선정하고 이에 상응하는 성능지수를 구조 물의 변형에너지로서 정의하였다. 따라서 가중행렬 Q는 구 조물의 강성 k가 되며, 제어력에 대한 가중치 R=r·I의 값 에 따라 전체 제어성능 및 제어력의 크기가 결정된다.…”

Section: 이 중 능동제어시스템은 제진효과의 우수성으로 인하여unclassified

FORM-based Structural Reliability Analysis of Dynamical Active Control System

2013

Journal of the Korean Society of Safety

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: This study describes structural reliability analysis of actively-controlled structure for which random vibration analysis is incorporated into the first-order reliability method (FORM) framework. The existing approaches perform the reliability analysis based on the RMS response, whereas the proposed study uses the peak response for the reliability analysis. Therefore, the proposed approach provides us a meaningful performance measure of the active control system, i.e., realistic failure probability. In addition, it can deal with the uncertainties in the system parameters as well as the excitations in single-loop reliability analysis, whereas the conventional random vibration analysis requires double-loop reliability analysis; one is for the system parameters and the other is for stochastic excitations. The effectiveness of the proposed approach is demonstrated through a numerical example where the proposed approach shows fast and accurate reliability (or inversely failure probability) assessment results of the dynamical active control system against random seismic excitations in the presence of parametric uncertainties of the dynamical structural system.

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Section: 이 중 능동제어시스템은 제진효과의 우수성으로 인하여unclassified

FORM-based Structural Reliability Analysis of Dynamical Active Control System

2013

Journal of the Korean Society of Safety

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“…Some optimal controller design schemes such as the Linear Quadratic Regulator (LQR) do explicitly account for this by minimising a function both of the system states and the control input (Alavinasab et al 2006;Sethi & Song 2005;Nyawako 2009). However this multi-object optimisation is typically resolved into a single number through scalar addition of the two components where the relative importance of each term is arbitrarily chosen.…”

Section: Introductionmentioning

confidence: 99%

Active Vibration Control of a Multi-Panel Floor Area

Hudson¹,

Reynolds²,

Nyawako³

2014

Structures Congress 2014

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Modern structures are increasingly being constructed ever more slender and with longer spans between columns. These aesthetically pleasing designs are however more susceptible to high levels of vibration caused by their human occupants. This has led to research into technology such as active vibration control (AVC) that can significantly reduce vibrations whilst maintaining the slender structural form. Previous research in the area of AVC for human-induced vibrations in floors has focussed on single points where the control is located. This paper investigates the effect over the entire area in a multi-panel floor. Simulations are performed on a finite element model of a real office floor with a realistic walking force applied, simulating the in-service conditions of an office environment. The power demand of the active control system is then investigated, in particular considering the trade-off between performance increase and power demand reduction. The benefits of using high feedback gains to control the entire area of this multi-panel floor are shown to be limited. Subsequently, power and cost savings could be realised through the use of reduced gain and/or a switching-off rule.

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“…The physical properties of a structure need to be known to control the building structure by general control methods such as linear-quadratic Gaussian (LQG), linear quadratic regulation (LQR), and proportional-integral-derivative (PID) control [Song et al, 2006;Alavinasab and Khajepour, 2006]. If they are undefined, an artificial neural network (ANN) method can be useful.…”

Section: Introductionmentioning

confidence: 99%

Earthquake Response Reduction of Building Structures Using Learning-Based Lattice Pattern Active Controller

Chang

Kim

et al. 2012

Journal of Earthquake Engineering

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An active vibration control technique for building structures using a learning-based lattice pattern controller (LBLPC) is proposed in this article. The training pattern of the LBLPC is composed of a lattice form for the control force and a state vector. The training pattern was trained by a learning rule using the gradient descent method (GDM) in earthquakes. The LBLPC calculates the control force using only the adjacent input information, thus making the corresponding calculation process much faster. A three-story building in the El Centro earthquake was used to train the LBLPC. And the California and Northridge earthquakes were used to verify the performance of the proposed method. In order to prove the control capability of the LBLPC, the control results of the LBLPC were compared with those of a lattice type probabilistic neural network (LPNN) in a numerical example. The results demonstrated that the proposed LBLPC algorithm reduces the response of the building structure during earthquakes more effectively than the LPNN.

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Active Control of Structures Using Energy-Based LQR Method

Cited by 73 publications

References 21 publications

FORM-based Structural Reliability Analysis of Dynamical Active Control System

FORM-based Structural Reliability Analysis of Dynamical Active Control System

Active Vibration Control of a Multi-Panel Floor Area

Earthquake Response Reduction of Building Structures Using Learning-Based Lattice Pattern Active Controller

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