Adaptive PID and Fuzzy Logic Controller with Expert System for Longitudinal Movement in Adaptive Cruise Control

The purpose of this work is to create an expert system tailored to rectify inaccuracies in control input estimation that are frequently caused by improper gain values or rule-based designs that aren't compatible with the dynamics of the system. PID controllers and fuzzy logic controllers are a common practice in many different control systems. The main goal of this research is to make control inputs more adaptable by incorporating an expert system that can handle problems with PID and fuzzy logic controller modifications. The methodology involves creating an expert system capable of identifying internal error signals and dynamically adjusting gains to enhance the primary controller's control input estimation. To assess the system's effectiveness in controlling PID and fuzzy logic controllers, particularly in the context of longitudinal movement, rigorous experiments were carried out using input signals surpassing the saturation limit specified in the original design. Through a comprehensive assessment that encompassed diverse variable input values and saturation limits, the study consistently demonstrated the superiority of the adaptive PID controller, it outperformed in terms of %OS and settling time values, especially when it came to the regulation of longitudinal movement. This study emphasizes the critical part that expert systems play in improving control input estimates within the primary controller and so increasing system efficiency beyond that of the initial setup, in order to accelerates the development of intelligent control systems and boosting overall performance, with practical applications in longitudinal movement control, by combining expert systems with PID and fuzzy logic controllers.