Adaptive Learning-Based Fuzzy Parameter Adjustment for Real-Time Robust Control under Uncertain Operating Conditions

Control systems in modern engineering applications are increasingly required to operate reliably under dynamic, uncertain, and nonlinear environments. Conventional control strategies, such as proportional–integral–derivative (PID) controllers, offer satisfactory performance only within narrow operating ranges and often deteriorate under disturbances, parameter variations, and unmodeled dynamics. Fuzzy logic controllers (FLCs) have been widely adopted to address system uncertainties by mimicking human decision-making through rule-based reasoning. However, traditional FLCs rely on fixed membership functions and rule weights, which limit their adaptability and degrade performance in rapidly changing environments. To overcome these limitations, this research proposes an adaptive learning-based mechanism that automatically adjusts fuzzy parameters in real time. The mechanism integrates an online learning algorithm with fuzzy control, enabling continuous monitoring of system performance and dynamic modification of membership functions, scaling factors, and rule priorities. Unlike static fuzzy systems, the adaptive model self-tunes its parameters to maintain robustness, stability, and efficiency under diverse operating conditions. The proposed adaptive fuzzy controller is designed to handle nonlinearities, external disturbances, and time-varying uncertainties that are typically encountered in practical applications such as robotics, renewable energy systems, and industrial automation. Simulation studies are conducted on benchmark nonlinear systems to validate the effectiveness of the approach. The results indicate significant improvements in convergence speed, disturbance rejection, and steady-state error minimization compared to conventional PID and static fuzzy controllers. Moreover, the adaptive mechanism enhances system resilience by ensuring consistent control quality even when operating parameters deviate from nominal conditions. This study highlights the potential of integrating adaptive learning strategies with fuzzy logic control to create intelligent, self-tuning controllers suitable for complex and uncertain environments. The proposed methodology contributes toward advancing the field of intelligent control by offering a scalable, real-time, and robust solution that can be extended to a wide range of engineering systems.