An Advanced Type-2 Fuzzy Inference for Rapid Convergence in Adaptive Communication Control

Recent research in smart factory networks has shown that advanced adaptive control are essential for managing the multidimensional uncertainties inherent in communication systems. In dynamic environments where traditional fuzzy controllers suffer from slow convergence and reduced robustness, rapid error decay is critical to ensure system stability. In this article, we propose the adaptive reinforcement fuzzy control algorithm (ARFCA), a novel scheme that integrates advanced Type-2 fuzzy inference, reinforcement learning-based control updates, and temporal memory defuzzification to maximize the convergence rate (CR). Experimental results demonstrate that the proposed ARFCA achieves a CR approximately three times higher and reduces control error by nearly 80% compared to conventional methods, thereby significantly enhancing system reliability and scalability.