For nonlinear systems subjected to external disturbances, an adaptive terminal sliding mode control (TSM) approach with fixed-time convergence is presented in this paper. The introduction of the fixed-time TSM with the sliding surface and the new Lemma of fixed-time stability are the main topics of discussion. The suggested approach demonstrates quick convergence, smooth and non-singular control input, and stability within a fixed time. Existing fixed-time TSM schemes are often impacted by unknown dynamics such as uncertainty and disturbances. Therefore, the proposed strategy is developed by combining the fixed-time TSM with an adaptive scheme. This adaptive method deals with an uncertain dynamic system when there are external disturbances. The stability of a closed-loop structure in a fixed-time will be shown by the findings of the Lyapunov analysis. Finally, the outcomes of the simulations are shown to evaluate and demonstrate the efficacy of the suggested method. As a result, examples with different cases are provided for a better comparison of suggested and existing control strategies.
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PLoS One
August 2024
School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
For nonlinear systems subjected to external disturbances, an adaptive terminal sliding mode control (TSM) approach with fixed-time convergence is presented in this paper. The introduction of the fixed-time TSM with the sliding surface and the new Lemma of fixed-time stability are the main topics of discussion. The suggested approach demonstrates quick convergence, smooth and non-singular control input, and stability within a fixed time.
View Article and Find Full Text PDFISA Trans
January 2024
School of Electrical Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610, Republic of Korea. Electronic address:
This paper introduces a new control strategy for robot manipulators, specifically designed to tackle the challenges associated with traditional model-based sliding mode (SM) controller design. These challenges include the need for accurately computed system models, knowledge of disturbance upper bounds, fixed-time convergence, prescribed performance, and the generation of chattering. To overcome these obstacles, we propose the incorporation of a neural network (NN) that effectively addresses these issues by removing the constraint of a precise system model.
View Article and Find Full Text PDFJ Magn Reson Imaging
May 2016
Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA.
Purpose: To determine whether triple-arterial phase acquisition with fluoroscopic triggering can provide both well-timed early and late hepatic arterial phase (HAP) images more frequently than when using a fixed-time delay during gadoxetic acid-enhanced magnetic resonance imaging (MRI).
Materials And Methods: Written informed consent was obtained for this Institutional Review Board (IRB)-approved prospective, Health Insurance Portability and Accountability Act (HIPAA)-compliant study. Ninety patients underwent gadoxetic acid-enhanced MRI at 3T with a single-breath-hold triple-arterial phase acquisition using either a fixed-time delay (n = 45) or fluoroscopic triggering injection protocol (n = 45).
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