Comparative Analysis of Fuzzy based Controller for Lane Keeping Assistance

Lane-keeping assistance control is a critical aspect of autonomous vehicle technology, aimed at ensuring the safety and stability of vehicles on the road. This research focuses on the development and comparative analysis of three distinct fuzzy based logic designs for lane-keeping assistance. The proposed controllers utilize different combinations of input parameters, including lateral deviation, yaw error, and speed, to generate the optimal steering angle for maintaining the vehicle within the lane. This study evaluates and compares three fuzzy logic controllers (FLC): Fuzz-Con-I, Fuzz-Con-II, and Fuzz-Con-III. Fuzz-Con-I uses lateral deviation and velocity to control the steering angle. Fuzz-Con-II employs yaw Error and velocity for the same purpose, while Fuzz-Con-Iii integrates lateral deviation, yaw error, and velocity. The performance of these FLCs is systematically analyzed and compared against state feedback and fuzzy PID control strategies. The simulation experiments employed a vehicle model, specifically depicted as a single tracked bicycle model, created within the MATLAB/SIMULINK environment. This model was regarded as a dynamic system with vision, and various control logics were applied and tested during the experiments.