Current status and future prospects of fuzzy control

Fuzzy control, rooted in fuzzy set theory, has emerged as a robust methodology for managing complex systems that defy precise modeling via traditional control approaches. This paper systematically reviews its theoretical foundations, recent advancements, and hybridization with cutting-edge technologies. The architecture of fuzzy control—comprising fuzzification, knowledge base, inference engine, and defuzzification—enables effective handling of uncertainty and nonlinearity. Despite its strengths in adaptability and robustness, challenges persist, including rule optimization, stability analysis, and precision limitations in high-dimensional models. To address these gaps, hybrid fuzzy control systems have been developed, integrating fuzzy logic with adaptive algorithms, PID control, neural networks, genetic algorithms, and deep learning. Notable innovations include Adaptive Fuzzy Control for dynamic parameter adjustment, Fuzzy-PID Control for nonlinear systems, and Genetic Fuzzy Control for optimizing rule bases. Historical milestones, such as Mamdani’s foundational work (1975) and Takagi-Sugeno (T-S) fuzzy models (1985), highlight its evolution from theory to industrial applications in manufacturing, transportation, and healthcare. Recent advancements synergize fuzzy control with IoT and big data analytics, enabling real-time optimization in smart homes, intelligent transportation, and medical diagnostics. However, theoretical research lags behind practical implementations, particularly in systematic rule derivation and mathematical rigor. Future directions emphasize strengthening fuzzy theory’s axiomatic basis, enhancing computational efficiency, and expanding applications in emerging domains like autonomous systems and sustainable energy. By bridging theory and practice, fuzzy control is poised to play a pivotal role in next-generation intelligent automation.