Genetic Algorithm Based Optimum Semi-Active Control of Building Frames Using Limited Number of Magneto-Rheological Dampers and Sensors

Abstract

Optimum semi-active control with a limited number of magneto-rheological (MR) dampers and measurement sensors has certain requirements. Most important of them is the accurate estimation of control forces developed in the MR dampers from the observations made in the structure. Therefore, the observation strategy should form an integral part of the optimization problem. The existing literature on the subject does not address this issue properly. The paper presents a computationally efficient optimization scheme for semi-active control of partially observed building frames using a limited number of MR dampers and sensors for earthquakes. The control scheme duly incorporates the locations of measurement sensors as variables into the genetic algorithm (GA) based optimization problem. A ten-storied building frame is taken as an illustrative example. The optimum control strategy utilizes two well-known control laws, namely, the linear quadratic Gaussian (LQG) with clipped optimal control and the bang-bang control to find the time histories of voltage to be applied to the MR dampers. The results of the numerical study show that the proposed scheme of sensor placement provides the optimum reduction of response with more computational efficiency. Second, optimal locations of sensors vary with the response quantities to be controlled, the nature of earthquake, and the control algorithm. Third, optimal locations of MR dampers are invariant of the response quantities to be controlled and the nature of earthquake.

Review of Vibration Control Strategies of High-Rise Buildings

“…A semi-active control device has qualities that can be modified to best reduce the system responses but cannot inject mechanical energy into the controlled structural system (containing the structure and the control device) [ 169 ]. According to preliminary research, semi-active systems outperform passive devices when implemented properly, opening the door to the possibility of successful response reduction under a variety of dynamic loading scenarios [ 169 , 170 , 171 , 172 ]. There are many semi-active systems available, including electro-mechanical devices, smart tuned mass dampers, tuned liquid dampers, controllable friction devices, controllable fluid dampers, and controllable stiffness devices.…”

Study on integrated response-based adaptive strategies for control and placement optimization of multiple magneto-rheological dampers-controlled structure under seismic excitations

“…The results of the adopted GA algorithm comply with the sequential placement of MR dampers starting from the first floor, independent of the number of MR dampers. Also, H2/LQG with clipped optimal control (COC) strategy proposed by Dyke et al (1996) and Bhaiya et al (2018) is implemented for the control of each MR damper.…”

“…Abdullah et al (2001) combined gradient-based optimization and GA to obtain the optimal spatial position of actuators in a structure. Bhaiya et al (2018) used the GA to numerically determine the optimal number and configuration of MR dampers and sensors, to use in a 10-story building frame.…”

Research on Vibration Reduction Control Based on Reinforcement Learning

“…MR damper vibration control methods mainly include active control, passive control, and semiactive control. Research [5,[10][11][12][13] shows that active control and semiactive control have better damping effects than passive control. However, compared to active control, semiactive control methods can change the stiffness and damping of the structure with a small amount of energy to reduce the vibration response of the structure [14,15].…”