Active nonlinear vibration control of a buckled beam based on deep reinforcement learning

Abstract

Vibration control in civil engineering is often challenging due to the nonlinear nature of structures. Traditional control strategies have limitations in terms of modeling accuracy and scalability, especially when analyzing complex nonlinear systems. To solve this problem, this study proposes a model-free active vibration control technique specifically for nonlinear systems, which employs deep reinforcement learning (DRL) to train a neural network controller. The effectiveness and practicality of the proposed method have been validated on a shallow, simply supported buckled beam. The results prove that DRL can significantly increase the safety margin and effectively mitigate buckling under high load levels without requiring extra energy. Compared with conventional model-based linear and polynomial controllers, the proposed control strategy demonstrates excellent adaptability and ease of implementation. This research aims to supplement and expand the existing understanding of DRL applications in structural control, pointing towards a promising direction for future technological advancements and real-world applications.