The application and optimization of control systems with multiple magneto-rheological dampers integrated into a civil engineering structure is a challenging task. The performance of the control system is strongly linked with the location and arrangement of control devices, and the optimal placement of control devices is inherently linked with the performance objective of the control algorithm. Therefore, for semi-active control devices, the placement algorithm should be well rooted within the control algorithm, for effective structural control. This article proposes response-based adaptive control strategies embedded with the device location optimization algorithm. The acceleration and inter-story drift responses of the structure are considered as the performance objective for two separate control strategies. The flexibility of this approach lies in the fact that the design algorithm for control and location of magneto-rheological dampers can be engineered based on the performance criteria of the system. This study involves numerical simulation of an actual five-story framed structure. The simulation results indicated that the seismic performance of the structure is strongly linked with the number, placement of the magneto-rheological damper, and the performance objective of the control strategy used. Also, the configuration and corresponding control provided by the response-based adaptive strategies performed better than the configuration predicted by the benchmark genetic algorithm using the H2/LQG controller.
The field of semiactive control dampers for structural control has seen a significant advancement in recent years. However, the controllers proposed in recent years have been computationally inefficient, time consuming, and did not allow the designer the flexibility in overall structural control. Consequently, this study presents a sequence of numerical and experimental studies conducted to determine the efficiency and performance of proposed multiple response optimization (MRO)-based control with iterative technique, using magnetorheological (MR) dampers as a control device in mitigating the structural response. The prime objective of the MRO control strategy is selecting an optimal control gain, obtained by a trade-off between the gains corresponding to the local minima of structural responses, selected as the performance objective of the controllers. The proposed strategy is numerically compared with H 2 /LQG with clipped optimal control (COC). Results indicated the efficiency and flexibility of the proposed strategy in mitigating the structural responses. Finally, multiple shake table tests were performed on a five-story steel frame with MR damper, employing the control strategies to be examined. The corresponding results substantiated the superiority of MRO control over passive control strategies, in mitigating the structural responses.
Most of the studies in the field of structural control are focused on maximizing the performance of control devices without taking into consideration the complex dynamic response of the structure, computational efficiency, performance flexibility, and feedback reliability. Also, the location algorithm of energy dissipation should be concatenated with the control strategy, for effective structural control of multi-story structures. Moreover, in recent years, non-contact measurements using digital image correlation techniques have been adopted and implemented for the monitoring of civil engineering structures. However, its application is restricted to reinforced cement concrete members, involving a lower frequency spectrum, small displacements, and a lower image capture rate. In this study two response-based-adaptive control strategies based on inter-story drift and acceleration response reduction objectives respectively have been proposed. The control strategies are then integrated with the device location algorithm to establish the optimum configuration/location of magnetorheological dampers in addition to the design parameters of the controls system. The performance of proposed strategies is numerically compared with the benchmark Genetic algorithm using the Clipped optimal controller. The corresponding results indicated that proposed control strategies performed better for high-intensity ground motion and satisfactorily for low-intensity records. Next, the shake table results validated the performance of response-based-adaptive control strategies with device allocation algorithm in alleviating the peak and RMS response of the structure. The response of the structure was also attenuated and distributed among the modes of the structure, as indicated by the fast Fourier transform response of the structure.
The present research work is a part of a project was a semi-active structural control technique using magneto-rheological damper has to be performed. Magneto-rheological dampers are an innovative class of semi-active devices that mesh well with the demands and constraints of seismic applications; this includes having very low power requirements and adaptability. A small stroke magneto-rheological damper was mathematically simulated and experimentally tested. The damper was subjected to periodic excitations of different amplitudes and frequencies at varying voltage. The damper was mathematically modeled using parametric Modified Bouc-Wen model of magneto-rheological damper in MATLAB/SIMULINK and the parameters of the model were set as per the prototype available. The variation of mechanical properties of magneto-rheological damper like damping coefficient and damping force with a change in amplitude, frequency and voltage were experimentally verified on INSTRON 8800 testing machine. It was observed that damping force produced by the damper depended on the frequency as well, in addition to the input voltage and amplitude of the excitation. While the damping coefficient (c) is independent of the frequency of excitation it varies with the amplitude of excitation and input voltage. The variation of the damping coefficient with amplitude and input voltage is linear and quadratic respectively. More ever the mathematical model simulated in MATLAB was in agreement with the experimental results obtained.
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