Domain knowledge-integrated reinforcement learning control of nonlinear tunable vibration absorber under nonstationary excitation

This paper proposes a novel model-free reinforcement learning (RL) control algorithm for a semi-active tunable vibration absorber (TVA) with nonlinear properties that are operated under nonstationary and multi-frequency excitations. The research addresses two critical challenges in vibration absorber control that are often treated separately: (i) the time-varying and nonlinear stiffness-damping characteristics, and (ii) the complex and nonstationary nature of real-world excitations. To address these challenges, a modified Q-learning algorithm is proposed by integrating vibration-domain knowledge derived from Parseval's theorem and frequency response functions. This integration not only enables the controller to effectively minimize vibration energy without requiring an explicit model of the plant but also significantly reduces the computational complexity of the learning process. The proposed controller is experimentally validated under nonstationary multi-frequency excitation using a semi-active TVA with highly time-variant stiffness and damping properties. Experimental results demonstrated accurate real-time control performance, achieving an R-squared value of 0.994 compared to an optimal control baseline, and up to 58% reduction in vibration energy. These results provide strong evidence that reinforcement learning control strategies, when guided by vibration-domain knowledge, can offer generalizable, efficient, and adaptive solutions to complex mechanical vibration control problems.