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Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints

Adinehvand, M; Lai, CY; Hoseinnezhad, R; (2021) Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints. In: Proceedings of the American Control Conference. (pp. pp. 2266-2271). IEEE: New Orleans, LA, USA. Green open access

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Abstract

In this paper, we present a backstepping adaptive hybrid force/position control based on Barrier Lyapunov Function for a robotic manipulator to prevent constraint violation of applied force and position simultaneously. First, the task space is partitioned according to the constrained and unconstrained directions, and a new representation of dynamics is introduced. Next, force/position control is applied using the strict-feedback backstepping technique, in which a time-varying Barrier Lyapunov Function is employed to ensure that the force and position do not violate their constraints. Finally, to deal with uncertainty, disturbance and non-linearity of the system, an adaptive radial basis function neural network (RBFNN) is also implemented in the control algorithm. Stability proof of the proposed control method is presented, and simulation studies on a 2-link manipulator show the effectiveness as well as the performance of the proposed controller in preventing constraint violation.

Type: Proceedings paper
Title: Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints
Event: 2021 American Control Conference (ACC)
Location: ELECTR NETWORK
Dates: 25 May 2021 - 28 May 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/ACC50511.2021.9483300
Publisher version: http://dx.doi.org/10.23919/ACC50511.2021.9483300
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Backstepping , Uncertainty, Force, Dynamics, Radial basis function networks, Stability analysis, Robustness
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10139863
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