A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models

Abstract

Deep reinforcement learning (DRL) has emerged as a promising approach for optimizing control policies in various fields. In this article, we explore the use of DRL for controlling vibrations in building structures. Specifically, we focus on the problem of reducing vibrations induced by external sources such as wind or earthquakes. We propose a DRL-based control framework that learns to adjust the control signal of a classical adaptive linear quadratic regulator (LQR)-based model to mitigate the vibration of building structures in real-time. The framework combines the proximal policy optimization method and a deep neural network that is trained using a simulation environment. The network takes input sensor readings from the building and outputs signals that work as a corrector to the signals from the LQR model. It demonstrates the approach’s effectiveness by simulating a 3-story building structure. The results show that our DRL-based control approach outperforms the classical LQR model in reducing building vibrations. Moreover, we show that the approach is robust for learning the system’s dynamics. Overall, the work highlights the potential of DRL for improving the performance of building structures in the face of external disturbances. The framework can be easily integrated into existing building control systems and extended to other control problems in structural engineering.

Keywords

• vibration control
• deep reinforcement learning
• proximal policy optimization
• lqr model
• deep neural network