Optimizing Electric Vehicle Trajectories: Overcoming Challenges and Improving Performance through PIDControl

Autonomous Electric Vehicles (AEVs) are pivotal in intelligent transportation systems, particularly in Electric Vehicles (EVs). The controller unit for automated EV navigation must optimize trajectory considering obstacles, ensuring a safe and comfortable ride. This paper emphasizes the challenges inherent in relinquishing control actions from drivers to control units in automated EVs, encompassing tasks like collision avoidance, adaptive cruise control, and lane-keeping. To address complexities, the Proportional-Integral-Derivative (PID) controller, known for its simplicity, is employed for steering angle control. The study recommends incorporating adaptive control techniques and robust control strategies to handle nonlinearities, time variations, and uncertainties. Additionally, the integration of obstacle detection and avoidance mechanisms enhances real-world applicability. The proposed approach involves iterative refinement of the PID controller through comprehensive evaluations, combining simulation and real-world experiments. The paper aims to enhance PID controller performance by adjusting parameters based on system characteristics, operating conditions, and user preferences. Evaluation metrics include lateral position error, yaw movement error, control effort, and system robustness, highlighting the effectiveness of the proposed PID control method.