Integration of Fuzzy Logic and Neural Networks in Advanced Control Systems

Abstract

This paper provides a comprehensive review of the integration of Fuzzy Logic (FL) and Neural Networks (NNs) in advanced control systems, focusing on the resulting Neuro-Fuzzy Systems (NFS). Traditional control methodologies often struggle with the high nonlinearity, dynamic behavior, and inherent uncertainties characteristic of modern engineering systems, demanding precise mathematical models that are often difficult to obtain. Hybrid intelligent systems, such as NFS (also known as Fuzzy Neural Networks or FNNs) address this gap by combining the complementary strengths of FL and NNs. Fuzzy logic provides a structured framework for incorporating human expertise and managing linguistic ambiguity through "if-then" rules, offering high structural interpretability. In contrast, neural networks provide robust, data-driven learning capabilities to acquire knowledge and perform complex function approximation. By integrating these two techniques, NFS leverage the learning and optimization power of NNs to automatically tune the parameters of the fuzzy inference system. The Adaptive-Network-based Fuzzy Inference System (ANFIS) stands as the most influential architecture, using a hybrid learning procedure of gradient descent and least squares estimation for rapid learning convergence and high accuracy. We analyze the application of NFS across critical domains, including enhancing adaptive trajectory tracking in robotics by mitigating the chattering effect in Sliding Mode Control (SMC) and improving stability and power-sharing in microgrid management through decentralized ANFIS-based virtual impedance control. Finally, we discuss systematic challenges, such as data dependence and the need for standardized online adaptability, and explore the emerging field of Deep Neuro-Fuzzy Systems (DNFSs), which fuse deep learning for automated feature extraction with neuro-fuzzy principles to scale effectively for Big Data challenges while maintaining interpretability.