Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators with Prescribed Performance and Input Constraints

Singh, R and Keshavan, J (2024) Approximation-Free Robust Tracking Control of Unknown Redundant Manipulators with Prescribed Performance and Input Constraints. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54 (11). pp. 6743-6755.

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Abstract

This article proposes a novel neural control architecture that employs input-output information to compensate for the lack of knowledge about the robot model to achieve prescribed tracking performance in the presence of joint constraints. To this end, an observer-controller zeroing neural network framework is formulated that combines online estimation of the unknown model's Jacobian with a trajectory tracking controller that implements joint angle and velocity constraints via a nonlinear map. Further, prescribed performance constraints are embedded within this architecture to achieve desired transient and steady-state performance along with added robustness to chattering. Hence, in comparison to prior studies, the proposed scheme facilitates a more robust control architecture with the added benefits of more stringent application of the input constraints and superior transient and steady-state performance. Simulation and experimental studies of trajectory tracking, including comparisons with leading alternative designs, are used to verify the efficacy and superior performance of the proposed scheme. © 2024 IEEE.

Item Type:	Journal Article
Publication:	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to publisher.
Keywords:	Flexible manipulators; Neural networks; Robust control; Robustness (control systems); Target tracking, Input-output; Input-output information; Joint constraint; Neural-networks; Performance constraints; Prescribed performance; Prescribed performance constraint; Trajectory-tracking; Unknown model; Zeroing neural network, Redundant manipulators
Department/Centre:	Division of Mechanical Sciences > Mechanical Engineering
Date Deposited:	19 Nov 2024 20:42
Last Modified:	19 Nov 2024 20:42
URI:	http://eprints.iisc.ac.in/id/eprint/86775

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