2024/11/12 by Mingkun Wu, Wu, Mingkun, Alisa Rupenyan +3
Engineering · Mathematics · #Actuator #Adaptive Control of Nonlinear Systems #Aerospace Engineering and Control Systems #Artificial intelligence #Computer science #Control (management) #Control and Dynamics of Mobile Robots #Control engineering #Control theory (sociology) #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Mathematical analysis #Mathematics #Robotics (cs.RO) #Singularity #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2411.07830
openalex publication_date 2024/11/12 · openalex created_date 2024/11/16 · openalex updated_date 2026/07/28
Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a learning-based control strategy to prevent robots entering singularity regions. More precisely, we leverage Gaussian process (GP) regression to learn the unknown model mismatch, where the prediction error is restricted by a deterministic bound. Moreover, we offer the criteria for parameter selection to ensure the feasibility of CBFs subject to actuator constraints. The proposed approach is validated by high-fidelity simulations on a 2 degrees-of-freedom (DoFs) planar robot.