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SUMMARYActive tuned mass dampers (ATMDs) are one of the most effective solutions for mitigation of destructive effects of earthquakes and strong winds in tall buildings. In order to achieve optimal performance, these systems are designed and tuned to mitigate effect of either wind or earthquake excitation. However, due to different frequency contents and intensities of wind and earthquake excitations, which will cause contrasting structural modes stimulation, the ATMD designed for one of these disturbances may not work optimally for the other one. This paper addresses a methodological simulation approach for adaptive control design of ATMDs in tall buildings located in regions with high level of seismic activity and recurrent strong winds. For this purpose, a multi‐objective adaptive genetic‐fuzzy controller is proposed for the control of an ATMD of a benchmark 76‐story building subjected to wind load and earthquake disturbances. Simulation results reveal that the optimal ATMD designed for earthquake disturbance does not work adequately for wind load disturbance and vice versa. Furthermore, the proposed adaptive controller has superior performance in suppressing base shear and inter‐story drifts induced by wind load and earthquake excitations. Copyright © 2013 John Wiley & Sons, Ltd.

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Summary Tuning the seismic control systems in order to achieve optimal performance is a challenging area due to the system and disturbance uncertainties. Although, model uncertainties, process time delay, and actuator dynamics can be considered as typical uncertainties, the main source of uncertainty in a seismic control system comes from the aleatory nature of earthquake disturbances. In this case, tuning of the control system based on a given seismic record may not necessarily result in optimal performance for other earthquakes. In this paper, a methodological approach is proposed for online control of active structural control systems considering seismic uncertainties. For this purpose, the concept of reinforcement learning is utilized for online tuning of an active mass drive system. The controller comprises a gain‐scheduling fuzzy proportional derivative controller whose gains are tuned via an online reinforcement learning algorithm. Moreover, in order to tackle the time delays, a dynamic state predictor is utilized in conjunction with the proposed controller. To evaluate the performance of proposed controller, according to an assumed site hazard, thousand ground motion records are generated and clustered based on their spectral features using a fuzzy clustering approach. Finally, the controller is implemented in a laboratory‐scale structure, and its performance is examined in the presence of the cluster centers and some real seismic records simulated on a shake table. The test results reveal successful performance of the proposed controller in tackling a wide range of seismic disturbances in the presence of time delay.

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