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Adaptive filter-based prescribed-time control for a tracking-dependent constrained nonlinear system
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DOI：10.1080/00207721.2025.2603560.png)
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Abstract
En 中文
This paper proposes an adaptive filter-based prescribed-time control strategy for a tracking-dependent constrained nonlinear system. By correlating with the reference command and time, a novel state transformation approach dynamically adjusts constraint boundaries to match the reference command while handling diverse state constraints. Then, a prescribed-time adjusting function is designed to meet the practical prescribed-time bounded stability (PPTBS) criterion. Subsequently, the designed practical prescribed-time bounded filter (PPTBF) by integrating the criterion effectively solves the 'explosion of complexity' issue. Radial basis function neural networks (RBFNNs) provide approximations for uncertain nonlinear dynamics and partial computation variables. The developed controller ensures the PPTBS of the tracking-dependent constrained system. Moreover, under strict full-state constraints enforcement, the reference command is tracked by the output within a bounded range. Finally, the scheme's efficacy is confirmed through simulations, particularly its convergence rate.
Keywords:
Practically prescribed-time bounded stability (PPTBS)
practically prescribed-time bounded filter (PPTBF)
state transformation approach
RBFNNs
Journal
I
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4.6
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1.1K
Citations:
7.3K