Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints

This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state constraints. A mathematical model precisely depicting the dynamic characteristics of flexible high-rise buildings, considering the time-varying nature of actuator faults, is initially established. Subsequently, a reinforcement learning-based controller is devised to counteract the negative impacts of faults on system performance. By introducing a time-varying asymmetric Lyapunov function, system state constraints are ensured, safeguarding system stability and security. The stability of the closed-loop system is rigorously proven using the Lyapunov stability theory, guaranteeing stable vibration suppression performance even in the presence of faults. The simulation results indicate that the proposed reinforcement learning vibration control method can effectively reduce the vibration response of flexible high-rise buildings when facing time-varying actuator faults. This demonstrates its remarkable robustness and adaptability, presenting a novel and effective solution for vibration control in real-world flexible high-rise buildings.