Fixed-Time Control with an Improved Sparrow Search Algorithm for Robotic Arm Performance Optimization
Open accessAbstract
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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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].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [18]
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].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [18]
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].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [18]
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Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
Urrea, C. (2025) · Actuators · doi:10.3390/act14040177 · Cited by 19 · see citing works
“Centralized Drops > 100 robots [4] High [39] Moderate [10] Low Decentralized Up to 200 agents [44] Moderate [51] Rapid [18] Medium Swarm-Based Large fleets [27] High [44] Rapid [27] Medium–High Figure 8 illustrates these scalability trends, emphasizing advantages beyond small fleet sizes [44].”
InfluentialDjari, A., Djabri, R., Aouiche, A., Bouarroudj, N., Houam, Y., Bettayeb, M., Alawad, M. A., & Alkhrijah, Y. M. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9110687
- 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
- gold
- doi.org
- Licence
- 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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