Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
Volume 70, Issue 1, Pages 241-245Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSII.2022.3194917
Keywords
Fixed-time control; nonsingular terminal sliding mode control; neural networks; robotic systems
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This paper investigates the problem of fixed-time trajectory tracking control of uncertain robotic systems. An adaptive radial basis function neural network is designed to estimate the model uncertainties and viscous frictions in robotic systems. A segmented terminal sliding mode control variable is adopted to alleviate the singularity problem. A new second-order fixed-time reaching law is designed to improve the tracking performance. A novel fixed-time non-singular TSMC based on the adaptive neural network is proposed to make the tracking errors converge to a small neighborhood of the origin in a fixed-time independent of the initial state. The experimental results demonstrate the effectiveness and advantage of the proposed control method.
This brief investigates the problem of fixed-time trajectory tracking control of uncertain robotic systems. Firstly, an adaptive radial basis function neural network is designed to estimate the model uncertainties and viscous frictions in robotic systems. Secondly, a segmented terminal sliding mode control (TSMC) variable is adopted to alleviate the singularity problem. To improve the tracking performance, a new second-order fixed-time reaching law is designed. Then, in order to make the tracking errors converge to a small neighborhood of the origin in a fixed-time independent of the initial state, a novel fixed-time non-singular TSMC based on the adaptive neural network is proposed. Finally, the experimental results demonstrate the effectiveness and advantage of the proposed control method.
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