Online control of an active seismic system via reinforcement learning

Abstract

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.

Applications of Machine Learning to Wind Engineering

“…From Table 7i, it can be concluded that the structural performance evaluations in mitigation applications usually used ML as a regression model while RL was typically utilized as an effective optimization or control algorithm. It is noted that relatively few ML applications for structural optimization and control under winds have been generated compared to those in earthquake engineering community (e.g., Ghaboussi and Joghataie 1995;Adam and Smith 2008;Jiang and Adeli 2008;Yakut and Alli 2011;Subasri et al, 2014;Khodabandehlou et al, 2018;Khalatbarisoltani et al, 2019;Hayashi and Ohsaki 2020). From Table 7ii, it can be concluded that most social media-informed response applications used ML as a classification model for disaster rescue and relief information dissemination.…”

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

“…The existing literature focuses on developing ANN models in active and semi-active control, while more advanced learning algorithms, such as reinforcement learning (Khalatbarisoltani et al, 2019), should be explored to realize robust control in tackling various sources of uncertainties simultaneously, including seismic uncertainties and time delays. Meanwhile, the promise of implementing ML in structural control can be enhanced if other forms of control devices are investigated, such as active tuned mass damper, distributed actuators, and semi-active stiffness dampers (Fisco and Adeli, 2011).…”

Adaptive backstepping sliding mode control design for vibration suppression of earth-quaked building supported by magneto-rheological damper

“…A state predictor is combined with proposed controller to solve the time delay problem. 9 Chen and Lai have combined a transfer system with a modern control strategy for shaking tables in order to mimicking the responses of high-rise buildings subjected to earthquakes. Three nonlinear controllers and three linear controllers are designed and synthesized to verify the feasibility and applicability of the proposed strategy.…”