Data-Driven Control Optimization on Frequency Response for Fast and Precise Motion of Flexible Joint Robots

This letter presents a data-driven control optimization framework for flexible joint robots (FJR) based on frequency response function (FRF) data, enabling automated controller synthesis without explicit model identification. Unlike conventional model-based approaches that rely on accurate parameter estimation, the proposed method directly utilizes measured FRF data and formulates the controller design as a convex optimization problem. The controller maximizes control bandwidth while ensuring stability across a wide range of configurations. Experimental validation on a FJR demonstrates superior tracking accuracy, vibration suppression, and robustness compared to model-based methods. Furthermore, a high-speed drumming task demonstrates the ability of the controller to handle repeated impacts and inertia variations, highlighting the potential of FRF-based control for the fast and precise operation of flexible robotic systems.