Vehicle Longitudinal Control for Adaptive Cruise Systems: Enhancing Performance with Adaptive PID and Fuzzy Logic Controllers with Expert Systems

Abstract

The aim of this study is to develop a specialised expert system designed to rectify inaccuracies in control input estimation that are often attributed to inappropriate gain values or rule-based designs that do not align with the system's dynamics. The primary objective of this research is to enhance the vehicle's longitudinal movement control by integrating an expert system that can dynamically adjust controller gains based on internal error signals. This expert system is specifically developed to improve control input adaptability in the context of vehicle control systems, addressing issues in the adjustment of PID and fuzzy logic controllers. In order to evaluate the system's performance in controlling PID and fuzzy logic controllers, particularly in the context of longitudinal movement, a series of rigorous experiments have been conducted, focusing on longitudinal movement control for a vehicle. These experiments have involved input signals that have exceeded the saturation limits specified in the original controller design. Through a comprehensive assessment encompassing a range of variable input values and saturation limits, this study has consistently demonstrated the superior performance of the adaptive PID controller over traditional methods, particularly in terms of metrics such as %OS and settling time, for in the regulation of longitudinal movement. This study underscores the pivotal role that expert systems play in enhancing control input estimates within the primary controller, ultimately improving the efficiency of vehicle control systems. By synergizing expert systems with PID and fuzzy logic controllers, this research advances intelligent control systems for vehicles, contributing to the development of more adaptive and efficient control strategies for longitudinal movement in practical vehicle applications such as adaptive cruise control.