A Comprehensive Review of Adaptive Control for Nonlinear Systems with Nonlinearities and Faults Using Fuzzy Logic and Neural Network Techniques

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems.