Resource-efficient FPGA-based machine learning control for active structural damping in shock environments

Maintaining structural integrity of electronic components in dynamic environments requires effective vibration control strategies, particularly in aerospace, automotive, and industrial applications where transient impacts and sustained oscillations can degrade performance and reliability. Traditional passive damping techniques provide limited flexibility to varying external forces, necessitating active control approaches for enhanced vibration suppression. This study evaluates the computational feasibility and performance trade-offs of Proportional- Derivative (PD), Linear Quadratic Gaussian (LQG), and Multi-Layer Perceptron (MLP) controllers for active vibration mitigation in a simulated cantilever beam system. A finite element model is used to simulate structural response under impact loading, while real-time implementation is conducted on an Field Deployable Gate Array (FPGA)-based control system. The PD and LQG controllers follow structured control frameworks, with the LQG approach incorporating optimal control and state estimation for improved stabilization. The MLP controller, trained on LQG-derived synthetic data, learns a control strategy without explicit system modeling. Performance metrics, including peak displacement, settling time, RMS displacement, control effort, and damping efficiency, provide insight into each controller’s trade-offs. Additionally, FPGA resource utilization is analyzed to assess the computational overhead of deploying structured and learning-based controllers in embedded systems. Results highlight that while structured controllers maintain efficiency and predictable performance, the MLP controller demonstrates the potential for adaptive vibration suppression while using 22.5% fewer FPGA resources than the LQG controller in terms of total slices. This is achieved while maintaining performance comparable to the LQG controller, with a settling time of 1.36 s compared to 1.01 s for LQG, and significantly outperforming the PD controller, which has a settling time of 1.82 s. These findings emphasize that the choice of control strategy depends on application constraints, balancing computational efficiency with flexibility.