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

کد مقاله : 1317-ISME2026

نویسندگان

فرناز صباحی * دانشگاه ارومیه

چکیده مقاله

Abstract: 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 developed 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 2-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.

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