Effect of System Uncertainty on Control of Seismic‐Excited Buildings

Yang, Jann N.; Akbarpour, Abbas

doi:10.1061/(asce)0733-9399(1990)116:2(462)

Cited by 19 publications

(4 citation statements)

References 12 publications

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“…To show the effectiveness of the controller under such uncertainties, the stiffness matrix was multiplied to an uncertainty factor which varies between 5% and 40%, and the resultant system is then subjected to the El Centro earthquake. Note that the active control systems are not sensitive to the uncertainties in the damping matrix [49]. As presented in Table 4, the obtained performance varies between 37.2% to 60.3% with an average value of 51.8%, which is very close to the value of 52.1%, obtained without considering uncertainties under El Centro earthquake.…”

Section: Environmental Uncertaintiessupporting

confidence: 67%

A framework for brain learning-based control of smart structures

Rahmani

Chase

Wiering

et al. 2019

Advanced Engineering Informatics

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In this approach, a deep neural network learns how to improve structural responses using feedback control. The effectiveness of the framework is demonstrated in a case study for a moment frame subjected to earthquake excitations. The performance of the learning method was improved by proposing a state-selector function that prevented the neural network from forgetting key states. Results show that the controller significantly improves structural responses not only to earthquake records on which it was trained but also to earthquake records new to the controller. The controller also has stable performance under environmental uncertainties. This capability distinguishes the proposed approach and makes it more appropriate for the situations in which it is likely that the controller will be exposed to unpredictable external excitations and high degrees of uncertainties.

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Section: Environmental Uncertaintiessupporting

confidence: 67%

A framework for brain learning-based control of smart structures

Rahmani

Chase

Wiering

et al. 2019

Advanced Engineering Informatics

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show abstract

“…To show the robustness of the proposed decentralized algorithm, the uncertainty of building stiffness is considered herein because it has been demonstrated that active controllers are not sensitive to the uncertainty in damping . In addition to the building previously mentioned, referred to as the ‘nominal building’, two additional buildings are considered: one with a 15% higher stiffness matrix and another with a 15% lower stiffness.…”

Section: Numerical Examplementioning

confidence: 99%

A decentralized structural control algorithm with application to the benchmark control problem for seismically excited buildings

Lei

Liu

2012

Struct. Control Health Monit.

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In this paper, a new decentralized structural active control algorithm using the instantaneous optimal control scheme is proposed. On the basis of finite element models, large-size structures are divided into smaller-size substructures. Each substructure is controlled by its own local controller; however, interconnection effect between adjacent substructures is taken into account by treating the interconnections as 'additional unknown disturbances' at substructural interfaces to the substructure. By sequential Kalman estimator for the substructural state vector and the recursive estimation of the 'additional unknown disturbances', instantaneous optimal control forces of each local controller are estimated without substructure interface measurements. Then, the proposed decentralized control algorithm is applied to the American Society of Civil Engineers 20-story benchmark control building under earthquake excitation and compared with the control criteria by the conventional centralized control approach. It is shown that the proposed decentralized control provides satisfactory control performances when compared with the conventional centralized control, but it increases the reliability of the control performance in case of the failure of local controllers. Robustness of the proposed decentralized control is also studied. Thus, the proposed decentralized control algorithm is suitable for structural active control of large-size structures.

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“…The benchmark model assumes a computational time delay of 0.001 ms and measurement noises modelled as Gaussian rectangular pulse processes with a pulse width of 0.001 s and a two-sided spectral density of 10 À9 m 2 Hz/s 3 that provides noise level up to AE0:05 cm=s 2 : The sensitivity of controllers to computational time delay and measurement noises was examined by amplifying the time delay to 0.01 ms and adding measurement noises modelled as Gaussian rectangular pulse processes with a pulse width of 0.001 s and a two-sided spectral density of 10 À7 m 2 Hz/s 3 which generates noise level of AE0:5 cm=s 2 : Additionally, the controllers were experimented when some feedback sensors failed to report the signal and transmitted measurement noise instead. On the other hand, uncertainty in damping was disregarded; since it has been demonstrated in the literature [22] that active controllers are not sensitive to the uncertainty in damping.…”

Section: Robustness Analysismentioning

confidence: 99%

Vibration control of wind-induced response of tall buildings with an active tuned mass damper using neural networks

Bani-Hani

2007

Struct. Control Health Monit.

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This paper introduces a new robust neural network methodology for vibration mitigation of tall building under wind excitation. The building considered is a 76-storey 306 m concrete office tower controlled by an active tuned mass damper (ATMD). The control strategy proposes two neural network (NN) models functioning together online to control the building. The first NN predicts the building's future responses and the second mimics the inverse dynamics of the building control force model, and operates on the future response with set of if-then rules to issue the required control force. Next, both NN models operate jointly to train a separate feed-forward neural network model designated as the NN controller (NNC). The efficacy of the two neural networks as well as the NN controller are examined and compared with a linear quadratic Gaussian controller. The controllers' performance and robustness are verified and presented, with stiffness uncertainty, time-delay, measurements' noise and some sensor failure. The results reveal the robustness and the effectiveness of the proposed method in alleviating wind-induced vibration for tall buildings.NEURAL NETWORKS FOR VIBRATION CONTROL 87 * A computational time-delay of 1000 ms is considered. * Measurement noise is included and modelled as Gaussian rectangular pulse processes with a pulse width of 0.001 s and a two-sided spectral density of 10 À9 m 2 Hz/s 3 . * Not more than 12 states can be considered for the compensator, to limit the computational resources, and it is required to be stable. * The robustness of the controller needs to be considered.

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Effect of System Uncertainty on Control of Seismic‐Excited Buildings

Cited by 19 publications

References 12 publications

A framework for brain learning-based control of smart structures

A framework for brain learning-based control of smart structures

A decentralized structural control algorithm with application to the benchmark control problem for seismically excited buildings

Vibration control of wind-induced response of tall buildings with an active tuned mass damper using neural networks

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