Data-Driven μ-Robust Control with CBF-Based Safety for Robotic Manipulators

This paper presents a unified data-driven robust control framework for robotic manipulators that integrates 𝜇-synthesis with control barrier functions (CBFs) to guarantee both performance and safety under structured uncertainty. The proposed approach employs data-driven system identification to construct uncertainty models suitable for 𝜇-analysis and synthesizes a robust controller via D–K iteration. Safety constraints, including joint limits and workspace restrictions, are enforced through CBF-based conditions that are embedded into the control synthesis problem. A novel feasibility theorem is proposed to ensure that the resulting 𝜇-CBF control law admits a solution while maintaining robust stability and constraint satisfaction. The effectiveness of the framework is demonstrated through simulations on a representative ۲-DOF robotic manipulator. The results indicate that the proposed method can be extended to general robotic systems operating in uncertain and safety-critical environments.

کلیدواژه ها:

Data-Driven Control ، 𝜇-synthesis ، Control Barrier Functions ، Robotic Manipulators