Enhancing Stability in Autonomous Control Systems Through Fuzzy Gain Scheduling (FGS) and Lyapunov Function Analysis

Traditional control methods may struggle to adapt to the nonlinear and uncertain characteristics in Autonomous Vehicle Control. In recent years, fuzzy control techniques, such as the Takagi–Sugeno fuzzy controller, have emerged as promising approaches for handling such complexities. Fuzzy controllers utilize linguistic variables and fuzzy logic to model system behavior, offering flexibility and robustness in dealing with uncertainties. Furthermore, Lyapunov function analysis provides a powerful tool for assessing the stability of dynamical systems. By employing Lyapunov functions, researchers can mathematically prove the stability of a system and derive stability criteria, contributing to a deeper understanding of system behavior. This paper investigates the enhancement of stability in control systems by employing Fuzzy Gain Scheduling combined with Lyapunov function analysis. Stability is a crucial aspect of control systems, ensuring their reliable and efficient operation in various dynamic environments. Traditional control techniques often struggle to handle the nonlinear and uncertain nature of modern systems. FGS offers a flexible and adaptive approach to control by adjusting controller gains based on system operating conditions. Additionally, Lyapunov function analysis provides a rigorous mathematical framework for stability assessment, enabling the verification of system stability properties. By integrating FGS and Lyapunov function analysis, this research aims to develop a robust control strategy capable of ensuring stability across a range of operating conditions. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed approach in enhancing stability and performance in control systems. Specifically, the settling time was reduced by 20