Journals / EMO Bilimsel Dergi / 2026 / Cilt: 16 Sayı: 3

Task-Space Control of Robot Manipulators Subject to an Input Delay: A Self-Tuning Fuzzy-Logic-Based Approach

Giriş Gecikmeli Robot Manipülatörlerinin Görev Uzayında Denetimi: Kendi Kendini Uyarlayan Bulanık Mantık Tabanlı Bir Yaklaşım

Serhat Obuz(Tarsus Üniversitesi)
Pages
71–80
Publication Date
2026-09-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The delay-compensation term and the fuzzy feedforward are each shown to be necessary for stability beyond 50 ms and 100 ms, respectively.

📌 3 Key Takeaways

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Objective & Hypothesis:A self-tuning (self-organized) fuzzy-logic controller is developed for an n-link robot manipulator that operates directly in the task space and whose control channel is affected by a known constant input delay.
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Key Findings & Data:Analytical development is emphasized and supported by a numerical study on a two-link revolute manipulator in task space under known input delays of 10--175 ms.
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Conclusion & Impact:The delay-compensation term and the fuzzy feedforward are each shown to be necessary for stability beyond 50 ms and 100 ms, respectively.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:uyarlamalı bulanık denetim · Doğrusal Olmayan Denetim · Robot Manipülatörler

Abstract

A self-tuning (self-organized) fuzzy-logic controller is developed for an n-link robot manipulator that operates directly in the task space and whose control channel is affected by a known constant input delay. The lumped dynamic uncertainty is reconstructed on-line by a fuzzy-logic network whose output weights and whose membership-function centers and widths are adapted through e-modification update laws equipped with a projection operator. Because the delay is known, a delay-free auxiliary signal is injected into the open-loop error system through an exponentially weighted integral of the true past control values. The delayed input is therefore removed from the error dynamics without a delay-mismatch term. A composite Lyapunov functional augmented with Lyapunov–Krasovskii (LK) terms is used to establish that the task-space tracking error is semi-globally uniformly ultimately bounded (SGUUB). A design procedure and a constructive nonlinear-damping alternative are also provided. Analytical development is emphasized and supported by a numerical study on a two-link revolute manipulator in task space under known input delays of 10--175 ms. Starting from zero weights and without any dynamic model knowledge, the fuzzy system reconstructs the lumped uncertainty online with a few percent relative error and drives a 10 mm initial error to a 0.31--2.43 mm steady state error. The delay-compensation term and the fuzzy feedforward are each shown to be necessary for stability beyond 50 ms and 100 ms, respectively.

Özet

Bu çalışmada, denetim girişi bilinen ve sabit bir gecikmeye uğrayan n-bağlantılı robot manipülatörler için, doğrudan görev uzayında çalışan kendi kendini uyarlayan bulanık mantık tabanlı bir denetleyici geliştirilmektedir. Manipülatör dinamiklerinin bilinmediği varsayılmaktadır. Tüm belirsizlik, çıkış ağırlıkları ile üyelik fonksiyonlarının merkez ve genişlikleri projeksiyon operatörlü e-modifikasyon yasalarıyla çevrimiçi güncellenen bir bulanık ağ tarafından yaklaştırılmaktadır. Gecikme bilindiğinden, telafi terimi gerçek geçmiş denetim değerlerinin üstel ağırlıklı integraliyle kurulmaktadır. Böylece gecikmeli giriş, hata dinamiklerinden gecikme uyumsuzluğu terimi doğurmadan çıkarılmaktadır. İzleme hatasının yarı-global düzgün nihai sınırlı (SGUUB) olduğu, Lyapunov--Krasovskii terimleriyle genişletilmiş bileşik bir fonksiyonel ile kanıtlanmaktadır. Ayrıca kazanç seçimi için açık bir rehber, yapıcı bir doğrusal olmayan sönümleme alternatifi ve 10--175 ms gecikme aralığını kapsayan sayısal bir çalışma sunulmaktadır. Sıfır ağırlıkla ve hiçbir dinamik model bilgisi kullanılmadan başlayan bulanık sistem, ideal ileri beslemeyi çevrimiçi kurmakta ve 10 mm'lik başlangıç hatası 0.31--2.43 mm bandına inmektedir. Gecikme telafi teriminin 50 ms, bulanık mantık tabanlı ileri beslemenin ise 100 ms ve üzerindeki gecikmelerde kararlılık için gerekli olduğu gösterilmektedir.

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Cite This Article

Obuz, S. (2026). Task-Space Control of Robot Manipulators Subject to an Input Delay: A Self-Tuning Fuzzy-Logic-Based Approach. EMO Bilimsel Dergi, Cilt: 16 Sayı: 3, 71–80. https://ajindex.com/articles/1390245

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References (30)

  1. M. W. Spong, “An historical perspective on the control of robotic manipulators”, Annual Review of Control, Robotics, and Autonomous Systems, Cilt 5, Sayı 1, ss. 1–31, 2022.
  2. A. Izadbakhsh, ve S. Khorashadizadeh, “Robust adaptive control of robot manipulators using bernstein polynomials as universal approximator”, International Journal of Robust and Nonlinear Control, Cilt 30, Sayı 7, ss. 2719–2735, 2020.
  3. L. Sciavicco, ve B. Siciliano, Modelling and control of robot manipulators, Springer Science & Business Media, 2012.
  4. B. Xian, M. de Queiroz, D. Dawson, ve I. Walker, “Task-space tracking control of redundant robot manipulators via quaternion feedback”, Proceedings of the 2001 IEEE International Conference on Control Applications, IEEE, 2001, ss. 363–368.
  5. B. Siciliano, “Kinematic control of redundant robot manipulators: A tutorial”, Journal of intelligent and robotic systems, Cilt 3, Sayı 3, ss. 201–212, 1990.
  6. U. Özbay, H. T. Sahin, ve E. Zergeroglu, “Robust tracking control of kinematically redundant robot manipulators subject to multiple self-motion criteria”, Robotica, Cilt 26, Sayı 6, s. 711, 2008.
  7. K. Cetin, E. Tatlicioglu, ve E. Zergeroglu, “An extended jacobian-based formulation for operational space control of kinematically redundant robot manipulators with multiple subtask objectives: An adaptive control approach”, Journal of Dynamic Systems, Measurement, and Control, Cilt 141, Sayı 5, 2019.
  8. S. Obuz, E. Tatlicioglu, ve E. Zergeroglu, “Adaptive cartesian space control of robotic manipulators: A concurrent learning based approach”, Journal of the Franklin Institute, Cilt 361, Sayı 6, s. 106701, 2024.
  9. N. Sharma, S. Bhasin, Q. Wang, ve W. E. Dixon, “Predictor-based control for an uncertain Euler–Lagrange system with input delay”, Automatica, Cilt 47, Sayı 11, ss. 2332–2342, 2011.
  10. M. Krstic, “Lyapunov tools for predictor feedbacks for delay systems: Inverse optimality and robustness to delay mismatch”, Automatica, Cilt 44, Sayı 11, ss. 2930–2935, Kasım 2008.
  11. R. Kamalapurkar, N. Fischer, S. Obuz, ve W. E. Dixon, “Time-varying input and state delay compensation for uncertain nonlinear systems”, IEEE Transactions on Automatic Control, Cilt 61, Sayı 3, ss. 834–839, 2016.
  12. S. Obuz, A. Parikh, I. Chakraborty, ve W. E. Dixon, “Lyapunov-based control of an uncertain Euler–Lagrange system with uncertain time-varying input delays without delay rate constraints”, IFAC-PapersOnLine (Proc. of the IFAC Workshop), Elsevier, 2016.
  13. I. Chakraborty, S. Obuz, ve W. E. Dixon, “Control of an uncertain nonlinear system with known time-varying input delays with arbitrary delay rates”, IFAC-PapersOnLine (Proc. of the 10th IFAC Symp. on Nonlinear Control Systems, NOLCOS), Elsevier, 2016.
  14. S. Obuz, J. R. Klotz, R. Kamalapurkar, ve W. Dixon, “Unknown time-varying input delay compensation for uncertain nonlinear systems”, Automatica, Cilt 76, ss. 222–229, 2017.
  15. N. Sadegh, ve R. Horowitz, “An exponentially stable adaptive control law for robot manipulators”, IEEE Trans. Robot. Autom., Cilt 6, Sayı 4, ss. 491–496, Ağustos 1990.
  16. J. J. Slotine, ve W. Li, “Composite adaptive control of robot manipulators”, Automatica, Cilt 25, Sayı 4, ss. 509–519, Temmuz 1989.
  17. E. Zergeroglu, W. E. Dixon, D. Haste, ve D. M. Dawson, “Composite adaptive output feedback tracking control of robotic manipulators”, Robotica, Cilt 17, ss. 591–600, 1999.
  18. P. Patre, W. Mackunis, M. Johnson, ve W. E. Dixon, “Composite adaptive control for Euler-Lagrange systems with additive disturbances”, Automatica, Cilt 46, Sayı 1, ss. 140–147, 2010.
  19. G. Chowdhary, ve E. Johnson, “Concurrent learning for convergence in adaptive control without persistency of excitation”, Proc. IEEE Conf. Decis. Control, IEEE, 2010, ss. 3674–3679.
  20. A. Parikh, R. Kamalapurkar, ve W. E. Dixon, “Integral concurrent learning: Adaptive control with parameter convergence using finite excitation”, International Journal of Adaptive Control and Signal Processing, Cilt 33, Sayı 12, ss. 1775–1787, 2019.
  21. S. Basu Roy, S. Bhasin, ve I. N. Kar, “Composite adaptive control of uncertain euler-lagrange systems with parameter convergence without pe condition”, Asian Journal of Control, Cilt 22, Sayı 1, ss. 1–10, 2020.
  22. K. Guo, Y. Pan, D. Zheng, ve H. Yu, “Composite learning control of robotic systems: A least squares modulated approach”, Automatica, Cilt 111, s. 108612, 2020.
  23. S. Obuz, E. Zergeroglu, ve E. Tatlicioglu, “Adaptive control of a class of nonlinear systems with guaranteed parameter estimation: A concurrent learning based approach”, IET Control Theory & Applications, 2024.
  24. S. Commuri, ve F. L. Lewis, “Adaptive-fuzzy logic control of robot manipulators”, Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), Minneapolis, MN, USA, IEEE, 1996, ss. 2604–2609.
  25. B. M. Yilmaz, E. Tatlicioglu, A. Savran, ve M. Alci, “Self-adjusting fuzzy logic based control of robot manipulators in task space”, IEEE Transactions on Industrial Electronics, Cilt 69, Sayı 2, ss. 1620–1629, 2022.
  26. V. Kumar, K. Rana, ve D. Kler, “Efficient control of a 3-link planar rigid manipulator using self-regulated fractional-order fuzzy pid controller”, Applied Soft Computing, Cilt 82, s. 105531, 2019.
  27. F. L. Lewis, D. M. Dawson, ve C. Abdallah, Robot Manipulator Control Theory and Practice, CRC, 2003.
  28. W. E. Dixon, A. Behal, D. M. Dawson, ve S. Nagarkatti, Nonlinear Control of Engineering Systems: A Lyapunov-Based Approach, Birkhauser, Boston, 2003.
  29. M. Spong, ve M. Vidyasagar, Robot Dynamics and Control, John Wiley & Sons Inc., New York, 1989.
  30. Z. Cai, M. S. de Queiroz, ve D. M. Dawson, “A sufficiently smooth projection operator”, IEEE Trans. Autom. Control, Cilt 51, Sayı 1, ss. 135–139, Ocak 2006.