Cited by 2Adaptive Control of Nonlinear SystemsRobotic Mechanisms and DynamicsAdvanced Control Systems Design

Fixed-Time Control with an Improved Sparrow Search Algorithm for Robotic Arm Performance Optimization

Open access
Zhang, Ruochen, Choi, Hyeung-Sik, Jung, Dongwook, Cho, Hyunjoon, Anh, Phan Huy Nam, Vu, Mai The·2024·Applied Sciences

Abstract

This paper presents an innovative approach that integrates a fixed-time control (FTC) algorithm with an improved sparrow search algorithm (ISSA) to enhance the trajectory tracking accuracy of a two-degree-of-freedom (two-DOF) robotic arm. The FTC algorithm, which incorporates barrier Lyapunov function (BLF) and adaptive neural network strategies, ensures rapid convergence, effective vibration suppression, and the robust handling of system uncertainties and input saturation. The ISSA, inspired by the foraging behavior of sparrows, improves search efficiency through dynamic weight adjustments and chaotic mapping, balancing global and local search capabilities. By optimizing control parameters, ISSA minimizes tracking errors. Simulation results demonstrate that the combined FTC and ISSA approach significantly reduces tracking errors and improves response speed compared to the use of FTC alone, underscoring its potential for achieving high-precision control in robotic arms and offering a promising direction for precise robotic control applications.

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Crossref · 10.3390/app142210096

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41 of 41 references verified0 citation errors · 0 not checkable

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  • Bioinspired techniques enhance adaptability in swarms and refine precision in manipulators, while supporting uptime in agriculture.

    “Bioinspired techniques, detailed in Section Key Technical Methods in Hybrid FTC, enhance adaptability in swarms [6] and refine precision in manipulators [18], while supporting uptime in agriculture [8].”
  • Hybrid approaches outperform in versatility, validated by a sub-0.017s recovery and 98% resilience.

    “Table 1 summarizes PFTC and AFTC trade-offs, with hybrid approaches outperforming in versatility, validated by a sub-0.017s recovery [18] and 98% resilience [17].”
  • Computational demands escalate with neural processing, posing deployment challenges, though optimization techniques like ISSA's adaptive mutation mitigate overhead.

    “Computational demands escalate with neural processing, posing deployment challenges [56], though optimization techniques like ISSA's adaptive mutation mitigate overhead [18].”

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Authors
Zhang, R., Choi, H.-S., Jung, D., Cho, H., Anh, P. H. N., & Vu, M. T.
Affiliations
  • Korea Maritime and Ocean University (KR)
  • Sejong University (KR)
Corresponding author
  • Choi, Hyeung-Sik
  • Vu, Mai The
Venue
Applied Sciences
Publisher
MDPI AG
Published
2024-11-05
Volume
14
Issue
22
Pages
10096
ISSN
2076-3417 (ISSN-L)
OpenAlex
W4404076123
Cited by
2
References
41
Citations by year
2025: 2
FWCI
0.31
Type
journal article
Language
en
Open access
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotic Mechanisms and Dynamics
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
Keywords
fixed-time control, improved sparrow search algorithm, robot manipulators, trajectory tracking, barrier Lyapunov function, adaptive neural networks, two-degree-of-freedom control, chaotic maps, metaheuristic optimization, vibration suppression, input saturation, system uncertainty

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