Benchmark Control Problems for Seismically Excited Nonlinear Buildings

Abstract"This paper presents a robust sampled-data controller design approach for vibration attenuation of civil structures considering parameter uncertainties and actuator saturation. The parameter uncertainties belong to polytopic form and are assumed to be the variations of the structural stiffness and damping. Regarding the uncertain sampling problem encountered in real world applications, the sampling period designed for the controller is allowed to be variable within a given bound. In order to obtain reduced peak response quantities, the energy-to-peak performance used to describe the peak values of the control output under all possible energy-bounded disturbances is optimised. The robust sampled-data state feedback controller is obtained in terms of the solvability of certain linear matrix inequalities (LMIs). The applicability of the proposed approach is demonstrated by a numerical example on vibration control of a building structure subject to seismic excitation. It is validated by the simulation results confirming that the designed controllers can effectively attenuate the structural vibration and keep the system stability while there are parameter uncertainties and actuator saturation constraints. (C) Abstract This paper presents a robust sampled-data controller design approach for vibration attenuation of civil structures considering parameter uncertainties and actuator saturation. The parameter uncertainties belong to polytopic form and are assumed to be the variations of the structural sti ness and damping.Regarding the uncertain sampling problem encountered in real world applications, the sampling period designed for the controller is allowed to be variable within a given bound. In order to obtain reduced peak response quantities, the energy-to-peak performance used to describe the peak values of the control output under all possible energy-bounded disturbances is optimised. The robust sampled-data state feedback controller is obtained in terms of the solvability of certain linear matrix inequalities (LMIs). The applicability of the proposed approach is demonstrated by a numerical example on vibration control of a building structure subject to seismic excitation. It is validated by the simulation results con rming that the designed controllers can e ectively attenuate the structural vibration and keep the system stability while there are parameter uncertainties and actuator saturation constraints.

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“…To evaluate the system performance, six evaluation criteria in relation to building responses are used [31].…”

Section: Application To Seismic-excited Buildingmentioning

confidence: 99%

Robust sampled-data control of structures subject to parameter uncertainties and actuator saturation

Zhang

Samali

et al. 2012

Engineering Structures

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“…[7][8][9][10][11][12][13][14][15][16] Furthermore, control strategies using adaptive neural networks and their application in various types of structural systems, including seismically isolated structures, have been extensively studied during the last two decades. [17][18][19][20][21][22][23][24] Although varying levels of response reduction have been demonstrated by means of active control strategies, there is still a need to develop efficient, consistent, and robust techniques to address the issues stated earlier.…”

Section: Introductionmentioning

confidence: 99%

Active neural predictive control of seismically isolated structures

Khodabandehlou

Pekcan

Fadali

et al. 2017

Struct Control Health Monit

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SummaryAn online identification and control scheme based on a wavelet neural network (WNN) and model predictive control (MPC) are presented. The WNN comprises a backpropagation neural network with wavelet activation functions and a parallel feedforward term. The WNN is used to identify the structural system, and the model is used to provide the predictions for MPC. The backpropagation network parameters and the controller are trained by the gradient descent algorithm to minimize performance indices. The feedforward component is trained using recursive least squares. The latter is found to drastically reduce the number of hidden layer neurons and significantly reduce the computational load of the neural network. Due to the general structure of the controller, its performance is satisfactory even under the strict condition imposed by a fixed learning rate. The efficacy of the control was demonstrated through a series of computational simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings. Significant reductions of all response amplitudes were achieved for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. In particular, the controller effectively regulated the apparent stiffness at the isolation level.

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“…However, the relative merits of these active and semiactive controllers, as applied to base-isolated structures, has not been investigated by careful comparison on a well-defined benchmark problem. Recently well-defined analytical benchmark problems [16][17][18][19][20][21] have been developed for studying response control strategies for building and bridge structures subjected to seismic and wind excitation, by broad consensus effort of the ASCE structural control committee. The goal of this effort was to develop benchmark models to provide systematic and standardized means by which competing control strategies, including devices, algorithms, sensors, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Carefully defined analytical benchmark problems are an excellent alternative to expensive experimental benchmark test structures. Due to the effectiveness of the fixed-base building benchmark effort [17][18][19][20] the ASCE structural control committee voted to develop a new smart baseisolated benchmark problem. Narasimhan et al [22][23][24][25] and Nagarajaiah et al [26,27] have developed the smart base-isolated benchmark problem, based on input from the ASCE structural control committee, with the capability to model different kinds of base isolation systems [39][40][41]: linear elastomeric systems with low damping or supplemental high damping; frictional systems; bilinear or nonlinear elastomeric systems or any combination thereof.…”

Section: Introductionmentioning

confidence: 99%

Smart base-isolated benchmark building. Part I: problem definition

Narasimhan

Nagarajaiah

Johnson

et al. 2006

Struct. Control Health Monit.

205

181

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SUMMARYSample controllers for a three-dimensional smart base-isolated building benchmark problem with linear and frictional isolation system are presented in this paper. A Kalman filter is used to estimate the states based on absolute acceleration measurements. Input filters are used to better inform the controller of the spectral content of the earthquake excitations. A reduced order control-oriented model of the benchmark structure with a linear isolation system is developed. A H 2 /linear quadratic Gaussian controller is presented for the active case; additionally, a clipped optimal controller is presented for the semiactive case. A preliminary 'skyhook' semiactive controller is also presented for the benchmark problem. Magnetorheological fluid dampers are used for control in the semiactive case and ideal actuators are used for control in the active case. The focus of this phase I study is on the linear isolation system only. Computed results for the passive, semiactive, and active cases are presented. Detailed comparisons of benchmark performance indices for base-isolated structures with a nominal linear isolation system, with and without control, for a set of strong near-field earthquakes are presented. The modeling and sample control designs demonstrated in this paper can be used to form the basis for studying a wide variety of active and semiactive control strategies}to be developed by the participants in the benchmark study}for linear base-isolated buildings.

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Benchmark Control Problems for Seismically Excited Nonlinear Buildings

Cited by 581 publications

References 27 publications

Robust sampled-data control of structures subject to parameter uncertainties and actuator saturation

Robust sampled-data control of structures subject to parameter uncertainties and actuator saturation

Active neural predictive control of seismically isolated structures

Smart base-isolated benchmark building. Part I: problem definition

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