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An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies

Cilt: 4 18 Eylül 2026
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An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies

Öz

Comparing robotic-manipulator control strategies is difficult because performance is multi-criteria and replicated runs yield distributions rather than single values. This study benchmarks six strategies: proportional-integral-derivative control, PI-D control, sliding-mode control, fuzzy gain-scheduled PID control, model predictive control implemented as a fixed-gain finite-horizon linear-quadratic law (MPC-LQ) and active disturbance rejection control. Thirty common-random-number replications of a two-degree-of-freedom manipulator simulation are conducted with randomised payload, friction, disturbance torque and sensor noise. Six cost metrics are converted to goodness values using pooled fifth- and ninety-fifth-percentile anchors and summarised as interval-valued q-rung orthopair fuzzy numbers. A fixed radial-projection rule enforces q-rung admissibility where required. The score is adopted from the published interval-valued q-rung literature, while the four-endpoint distance is an explicit adaptation of Du’s published Minkowski-type q-rung distance principle. Literature-informed, author-specified scenario weights are generated with the full consistency method (FUCOM) and the strategies are ranked with RAFSI (ranking of alternatives through functional mapping of criterion sub-intervals into a single interval) using fixed external anchors. Under the stated model, tunings and baseline weights, active disturbance rejection control ranks first (0.735), followed by sliding-mode control (0.635) and PI-D control (0.593). The leading strategy remains unchanged under equal weights, four emphasis profiles, 10,000 seeded FUCOM-ratio perturbations, q values from 2 to 5 and an alternative projection rule; however, some middle ranks change across emphasis profiles. A distance-based ideal-solution comparison reproduces the baseline order. The study provides a transparent worked benchmarking example and does not establish hardware performance.

Anahtar Kelimeler

Destekleyen Kurum

This research received no external funding.

Etik Beyan

The research presented in this manuscript does not involve human participants, animal subjects or clinical trials. The study used no personal, biometric or health-related data.

Teşekkür

The author sincerely thanks the anonymous reviewers for their careful reading and constructive comments, which have substantially improved the quality and clarity of this manuscript.

Kaynakça

  1. Zhai, J., & Xu, G. (2021). A novel non-singular terminal sliding mode trajectory tracking control for robotic manipulators. IEEE Transactions on Circuits and Systems II: Express Briefs, 68(1), 391–395. https://doi.org/10.1109/TCSII.2020.2999937
  2. Yin, X., Pan, L., & Cai, S. (2021). Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators. Robotics and Computer-Integrated Manufacturing, 72, 101884. https://doi.org/10.1016/j.rcim.2019.101884
  3. Zhong, G., Wang, C., & Dou, W. (2021). Fuzzy adaptive PID fast terminal sliding mode controller for a redundant manipulator. Mechanical Systems and Signal Processing, 159, 107577. https://doi.org/10.1016/j.ymssp.2020.107577
  4. Ashagrie, A., Salau, A. O., & Weldcherkos, T. (2021). Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller. Cogent Engineering, 8(1), 1950105. https://doi.org/10.1080/23311916.2021.1950105
  5. Raoufi, M., Habibi, H., Yazdani, A., & Wang, H. (2022). Robust prescribed trajectory tracking control of a robot manipulator using adaptive finite-time sliding mode and extreme learning machine method. Robotics, 11(5), 111. https://doi.org/10.3390/robotics11050111
  6. Rishabh, R., & Das, K. N. (2025). A critical review on metaheuristic algorithms based multi-criteria decision-making approaches and applications. Archives of Computational Methods in Engineering, 32(2), 963–993. https://doi.org/10.1007/s11831-024-10165-9
  7. Halim, A. H., Ismail, I., & Das, S. (2021). Performance assessment of the metaheuristic optimization algorithms: An exhaustive review. Artificial Intelligence Review, 54(3), 2323–2409. https://doi.org/10.1007/s10462-020-09906-6
  8. Weerasuriya, A. U., Zhang, X., Wang, J., Lu, B., Tse, K. T., & Liu, C. H. (2021). Performance evaluation of population-based metaheuristic algorithms and decision-making for multi-objective optimization of building design. Building and Environment, 198, 107855. https://doi.org/10.1016/j.buildenv.2021.107855

Ayrıntılar

Birincil Dil

İngilizce

Konular

Kontrol Mühendisliği, Çok Ölçütlü Karar Verme

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

18 Eylül 2026

Gönderilme Tarihi

22 Temmuz 2026

Kabul Tarihi

18 Eylül 2026

Yayımlandığı Sayı

Yıl 2026 Cilt: 4

Kaynak Göster

APA
Şengül, D. (2026). An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies. International Periodical of Recent Technologies in Applied Engineering, 4, 22-32. https://izlik.org/JA55FP28JX
AMA
1.Şengül D. An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies. PORTA. 2026;4:22-32. https://izlik.org/JA55FP28JX
Chicago
Şengül, Doğan. 2026. “An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies”. International Periodical of Recent Technologies in Applied Engineering 4 (Eylül): 22-32. https://izlik.org/JA55FP28JX.
EndNote
Şengül D (01 Eylül 2026) An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies. International Periodical of Recent Technologies in Applied Engineering 4 22–32.
IEEE
[1]D. Şengül, “An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies”, PORTA, c. 4, ss. 22–32, Eyl. 2026, [çevrimiçi]. Erişim adresi: https://izlik.org/JA55FP28JX
ISNAD
Şengül, Doğan. “An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies”. International Periodical of Recent Technologies in Applied Engineering 4 (01 Eylül 2026): 22-32. https://izlik.org/JA55FP28JX.
JAMA
1.Şengül D. An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies. PORTA. 2026;4:22–32.
MLA
Şengül, Doğan. “An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies”. International Periodical of Recent Technologies in Applied Engineering, c. 4, Eylül 2026, ss. 22-32, https://izlik.org/JA55FP28JX.
Vancouver
1.Doğan Şengül. An interval-valued q-rung orthopair fuzzy FUCOM-RAFSI framework for simulation-based benchmarking of robotic-manipulator control strategies. PORTA [Internet]. 01 Eylül 2026;4:22-3. Erişim adresi: https://izlik.org/JA55FP28JX

International Periodical of Recent Technologies in Applied Engineering
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