Fuzzy Logic Controller for Vehicle Longitudinal Control

This document presents a fuzzy logic controller for vehicle longitudinal control, which serves as a critical component in automotive control systems. The controller employs a generic architecture that allows for flexible modifications to meet various application needs. Fuzzy logic controllers utilize linguistic variables and rule-based inference mechanisms to handle the nonlinearities and uncertainties inherent in vehicle dynamics. The implementation typically involves defining input variables (such as velocity error and acceleration), output variables (like throttle and brake commands), and a rule base containing conditional statements like "IF velocity_error is positive_large THEN increase_throttle." The design flexibility enables adjustments to membership functions (triangular, trapezoidal, or Gaussian) and rule optimization through MATLAB's Fuzzy Logic Toolbox or Python's scikit-fuzzy library. By applying fuzzy control principles, precise regulation of longitudinal vehicle motion is achieved, enhancing both stability and safety through smooth acceleration/deceleration profiles. Therefore, fuzzy logic control represents an effective methodology for longitudinal vehicle control applications.