Research Article
Model Predictive Control (MPC) and Proportional Integral Derivative Control (PID) for Autonomous Lane Keeping Maneuvers: A Comparative Study of Their Efficacy and Stability
@INPROCEEDINGS{10.1007/978-3-031-48891-7_9, author={Ahsan Kabir Nuhel and Muhammad Al Amin and Dipta Paul and Diva Bhatia and Rubel Paul and Mir Mohibullah Sazid}, title={Model Predictive Control (MPC) and Proportional Integral Derivative Control (PID) for Autonomous Lane Keeping Maneuvers: A Comparative Study of Their Efficacy and Stability}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part II}, proceedings_a={IC4S PART 2}, year={2024}, month={1}, keywords={Autonomous car Trajectory models MPC PID Model Prediction}, doi={10.1007/978-3-031-48891-7_9} }- Ahsan Kabir Nuhel
Muhammad Al Amin
Dipta Paul
Diva Bhatia
Rubel Paul
Mir Mohibullah Sazid
Year: 2024
Model Predictive Control (MPC) and Proportional Integral Derivative Control (PID) for Autonomous Lane Keeping Maneuvers: A Comparative Study of Their Efficacy and Stability
IC4S PART 2
Springer
DOI: 10.1007/978-3-031-48891-7_9
Abstract
The escalating frequency of fatal crashes has led to an enhanced focus on road safety, resulting in the creation of diverse driver assistance systems. Several instances of these systems encompass active braking, lane departure warning, cruise control, lane maintaining, and numerous additional examples. However, the primary objective of this research is to examine the effectiveness and reliability of a model predictive control (MPC) and a proportional integral derivative (PID) control in executing lane keeping maneuvers within an autonomous vehicle. In this paper, a custom controller for autonomous lane-changing maneuvers is developed by utilizing the Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) controllers. Different trajectory models are employed to assess the overall effectiveness of the designed model, showcasing its superiority over existing models.
