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