Safe Reinforcement Learning-Based Vibration Control: Overcoming Training Risks with LQR Guidance

• URL: http://arxiv.org/abs/2510.01269v1
• Date: Mon, 29 Sep 2025 10:10:20 GMT
• Title: Safe Reinforcement Learning-Based Vibration Control: Overcoming Training Risks with LQR Guidance
• Authors: Rohan Vitthal Thorat, Juhi Singh, Rajdip Nayek,
• Abstract summary: Structural vibrations pose significant risks, including safety hazards for occupants, structural damage, and increased maintenance costs.<n>Traditional model-based control strategies, such as Linear Quadratic Regulator (LQR) effectively mitigate vibrations.<n>We introduce a hybrid control framework that integrates both LQR and RL controllers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structural vibrations induced by external excitations pose significant risks, including safety hazards for occupants, structural damage, and increased maintenance costs. While conventional model-based control strategies, such as Linear Quadratic Regulator (LQR), effectively mitigate vibrations, their reliance on accurate system models necessitates tedious system identification. This tedious system identification process can be avoided by using a model-free Reinforcement learning (RL) method. RL controllers derive their policies solely from observed structural behaviour, eliminating the requirement for an explicit structural model. For an RL controller to be truly model-free, its training must occur on the actual physical system rather than in simulation. However, during this training phase, the RL controller lacks prior knowledge and it exerts control force on the structure randomly, which can potentially harm the structure. To mitigate this risk, we propose guiding the RL controller using a Linear Quadratic Regulator (LQR) controller. While LQR control typically relies on an accurate structural model for optimal performance, our observations indicate that even an LQR controller based on an entirely incorrect model outperforms the uncontrolled scenario. Motivated by this finding, we introduce a hybrid control framework that integrates both LQR and RL controllers. In this approach, the LQR policy is derived from a randomly selected model and its parameters. As this LQR policy does not require knowledge of the true or an approximate structural model the overall framework remains model-free. This hybrid approach eliminates dependency on explicit system models while minimizing exploration risks inherent in naive RL implementations. As per our knowledge, this is the first study to address the critical training safety challenge of RL-based vibration control and provide a validated solution.

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