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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints
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  • Yuzhu Sun,
  • Mien Van,
  • Stephen McIlvanna,
  • Nguyen Minh Nhat,
  • Sean McLoone ,
  • Dariusz Ceglarek,
  • Shuzhi Sam Ge
Yuzhu Sun
Queen's University Belfast School of Electronics Electrical Engineering and Computer Science
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Mien Van
Queen's University Belfast School of Electronics Electrical Engineering and Computer Science

Corresponding Author:[email protected]

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Stephen McIlvanna
Queen's University Belfast School of Electronics Electrical Engineering and Computer Science
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Nguyen Minh Nhat
Queen's University Belfast School of Electronics Electrical Engineering and Computer Science
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Sean McLoone
Queen's University Belfast School of Electronics Electrical Engineering and Computer Science
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Dariusz Ceglarek
University of Warwick International Manufacturing Centre
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Shuzhi Sam Ge
National University of Singapore Department of Electrical and Computer Engineering
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Abstract

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator and then extended to the simulated UR10 robot. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety.
28 Mar 2024Submitted to International Journal of Robust and Nonlinear Control
28 Mar 2024Assigned to Editor
28 Mar 2024Submission Checks Completed
01 Apr 2024Reviewer(s) Assigned