Operational temperature effect on MR dampers in stay cable vibration control : an adaptive RL-based control strategy
This paper proposes a reinforcement-learning (RL)-based semi-active control strategy to mitigate stay-cable vibrations using magnetorheological (MR) dampers while explicitly addressing MR fluid temperature variations during damper operation. The controller is model-free and based on the Q-learning method. The controller updates its policy online using local measurements, including the displacement and velocity at the damper location. The control law avoids a fixed inverse-damper model by using a quadratic action–value approximation, which yields a closed-form current command, while a target matrix is used to stabilize the online learning. The controller performance is numerically evaluated on the A10 cable of the Dongting Lake Bridge using a temperature-dependent hyperbolic-tangent MR damper model implemented in MATLAB. A comparison with a classical LQR-based semi-active controller that relies on an inverse MR damper model is presented. The results indicate that the proposed RL controller remains effective under temperature-induced uncertainty, while still requiring lower control effort in terms of improved energy efficiency.