A Critical Review and Comparative Analysis of Advanced Adaptive Fuzzy Logic Controllers: From 2000 To 2026

Bharathi

doi:10.55524/ijirem.2026.14.4.6

Abstract

Adaptive fuzzy logic controllers (AFLCs) have been one of the most successful control strategies for controlling complex uncertain systems. Unlike conventional controllers an AFLC can automatically tune its rule base, membership functions scaling factors, or gains to satisfy specific control objectives. As a result, it has been demonstrated to be superior to traditional approaches in terms of tracking and disturbance-rejection capabilities and robustness. Because of advances in computers, optimization, machine learning, and microprocessors, adaptive fuzzy logic controllers have become one of the most important control strategies used today. This capability renders AFLCs particularly attractive for controlling nonlinear, uncertain, time-varying, and only partially modeled plants. The literature from 2000 until 2026 demonstrates a progression from rather simplistic adaptation laws to neuro-fuzzy learning, evolutionary adaptation, type-2 fuzzy logic, event-triggered control, fault-tolerant designs, reinforcement-learning-assisted adaptation, and self-evolving fuzzy systems. This review attempts to follow this development and classifies the related works according to the adapted quantities and adaptation mechanisms. Sixty publications have been analyzed and synthesized to highlight the trends, with a particular focus on the works published in 2021–2026. The application areas include robotics, drives, power electronics, renewable energy, transportation, aerospace, and industrial processes. The comparison of the surveyed solutions reveals the tendency to transition from relatively simple fuzzy structures with offline-tuned parameters to more sophisticated control laws with online-adjusted parameters and rules. At the same time, the adaptability gain often comes at the price of complexity, computational burden, rule explosion, interpretability, cyber security concerns, data reliance, and the need for hardware-in-the-loop testing.

Keywords

Adaptive Fuzzy Control; Intelligent Control; Neuro-Fuzzy Systems; Self-Tuning Fuzzy Controller; Anfis; Type-2 Fuzzy Logic; Event-Triggered Control; Reinforcement Learning; Evolving Fuzzy Systems; Artificial Intelligence

References

L. A. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. Available from: https://doi.org/10.1016/S0019-9958(65)90241-X
E. H. Mamdani, “Application of Fuzzy Algorithms for Control of Simple Dynamic Plant,” Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585–1588, 1974. Available from: https://digital-library.theiet.org/doi/abs/10.1049/piee.1974.0328
K. M. Passino and S. Yurkovich, Fuzzy Control. Reading, MA, USA: Addison-Wesley, 1998. Available from: https://www.a-lab.ee/edu/system/files/eduard.petlenkov/courses/ISS0023/2016_Autumn/materials/FCbook.pdf
J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ, USA: Prentice Hall, 1997. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1367578803000099
H. K. Khalil, Nonlinear Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2002. Available from: https://files.dooctly.com/samples/solution-manual-nonlinear-systems-3e-khalil.pdf
L.-X. Wang, A Course in Fuzzy Systems and Control. Englewood Cliffs, NJ, USA: Prentice Hall, 1997. Available from: https://dl.acm.org/doi/abs/10.5555/248374
H. Ying, Fuzzy Control and Modeling: Analytical Foundations and Applications. Piscataway, NJ, USA: IEEE Press, 2000. Available from: https://dl.acm.org/doi/abs/10.5555/557283
R. Babuska, Fuzzy Modeling for Control. Boston, MA, USA: Kluwer Academic Publishers, 1998. Available from: https://tinyurl.com/3rejezej
R.-E. Precup and H. Hellendoorn, “A survey on industrial applications of fuzzy control,” Computers in Industry, vol. 62, no. 3, pp. 213–226, 2011. Available from: https://doi.org/10.1016/j.compind.2010.10.001
J. M. Mendel, Uncertain Rule-Based Fuzzy Systems, 2nd ed. Cham, Switzerland: Springer, 2017. Available from: https://link.springer.com/book/10.1007/978-3-319-51370-6
F. Jia, J. Lu, and Y. Li, “Adaptive fuzzy control for nonlinear uncertain pure-feedback systems with full-state and input constraints,” International Journal of Robust and Nonlinear Control, vol. 31, no. 10, pp. 4917–4932, 2021. Available from: https://doi.org/10.1002/rnc.5511
S. Li, C. K. Ahn, and Z. Xiang, “Command-filter-based adaptive fuzzy finite-time control for switched nonlinear systems using state-dependent switching method,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 4, pp. 833–845, 2021. Available from: https://doi.org/10.1109/TFUZZ.2020.2965917
T. Wang et al., “An adaptive fuzzy PID controller for speed control of brushless direct current motor,” Discover Applied Sciences, vol. 4, Art. no. 71, 2022. Available from: https://doi.org/10.1007/s42452-022-04957-6
Z. Yang, K. Wang, and Q. Wu, “Adaptive fuzzy event-triggered control for non-strict feedback nonlinear systems with input delay and full state constraints,” IEEE Access, vol. 11, pp. 113148–113159, 2023. Available from: https://doi.org/10.1109/ACCESS.2023.3323395
M. Van et al., “Adaptive fuzzy fault tolerant control for robot manipulators with fixed-time convergence,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 9, pp. 3210–3219, 2023. Available from: https://doi.org/10.1109/TFUZZ.2023.3247693
H. Zhao et al., “Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method,” Fuzzy Sets and Systems, vol. 474, Art. no. 108735, 2024. Available from: https://doi.org/10.1016/j.fss.2023.108735
A. A. Khater et al., “Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system,” Neural Computing and Applications, vol. 37, pp. 24607–24624, 2025. Available from: https://doi.org/10.1007/s00521-024-10741-x
Y. Bai et al., “Adaptive fuzzy fixed-time control for uncertain time-delay nonlinear systems with output constraints,” IEEE Transactions on Fuzzy Systems, vol. 33, no. 7, pp. 2261–2271, 2025. Available from: https://doi.org/10.1109/TFUZZ.2025.3560644
E. Çelik, “A new adaptive fuzzy logic controller configuration for control systems design,” IET Control Theory & Applications, vol. 20, no. 1, Art. no. e70139, 2026. Available from: https://doi.org/10.1049/cth2.70139
Z. Zhao, H. Chang, and C. Wu, “Adaptive fuzzy command filtered tracking control for flexible robotic arm with input dead-zone,” Applied Sciences, vol. 13, no. 19, Art. no. 10812, 2023. Available from: https://doi.org/10.3390/app131910812
G. Andonovski et al., “Advancements in data-driven evolving fuzzy and neuro-fuzzy control: A comprehensive survey,” Applied Soft Computing, vol. 186, Art. no. 114058, 2026. Available from: https://doi.org/10.1016/j.asoc.2025.114058
M. Kharrat and P. Mercorelli, “A comprehensive review of adaptive control for nonlinear systems with nonlinearities and faults using fuzzy logic and neural network techniques,” Mathematics, vol. 14, no. 8, Art. no. 1256, 2026. Available from: https://doi.org/10.3390/math14081256
M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, Art. no. n71, 2021. Available from: https://doi.org/10.1136/bmj.n71
Y. Sun, X. Liang, and Y. Wan, “Tracking control of robot manipulator with friction compensation using time-delay control and an adaptive fuzzy logic system,” Actuators, vol. 12, no. 5, Art. no. 184, 2023. Available from: https://doi.org/10.3390/act12050184
M.-D. Duong et al., “Adaptive fuzzy sliding mode control of an actuator powered by two opposing pneumatic artificial muscles,” Scientific Reports, vol. 13, Art. no. 8242, 2023. Available from: https://doi.org/10.1038/s41598-023-34491-3
Y. Yang et al., “Multiple adaptive fuzzy Nussbaum-type functions design for stochastic nonlinear systems with fixed-time performance,” Fuzzy Sets and Systems, vol. 476, Art. no. 108767, 2024. Available from: https://doi.org/10.1016/j.fss.2023.108767
S. Blazic and I. Skrjanc, “Evolving fuzzy control of a hydraulic plant,” IFAC-PapersOnLine, vol. 59, no. 26, pp. 341–346, 2025. Available from: https://doi.org/10.1016/j.ifacol.2025.12.058

Cites this article as

Bharathi "A Critical Review and Comparative Analysis of Advanced Adaptive Fuzzy Logic Controllers: From 2000 To 2026", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-4, Page No-43-49, 2026. Available from: https://doi.org/10.55524/ijirem.2026.14.4.6