Model predictive control-based autonomous vehicle for monitoring path tracing process Available to Purchase

This paper presents the path-tracking issue of terrestrial Autonomous Vehicles (AV) using a linear model predictive controller (LMPC) structure. In a cascade structure, the controller architecture takes into account both the kinematic and dynamic control. In addition to ensuring tracking accuracy, the controller also takes vehicle dynamic stability into account during tracking. The aim of this research is for the AV to precisely track the route’s specified waypoints, ensure vehicle stability, and satisfy the control system’s reliable performance. The model of the autonomous vehicle used AV as a model for the MPC. This study includes a comparative study between two scenarios. The first scenario was a route that requires the car to travel along a normal path without any barriers, while the second scenario requires the car to pass over a barrier that has been placed in the road without physical contact. Using MATLAB/Simulink R2022b, a study on the performance of the MPC was carried out. After improving the parameters for LMPC, the results show that by changing the direction of the vehicle, the MPC delivers high-quality performance in both the precision of the path tracing and the smoothness of the steering angle. These results demonstrate that the suggested MPC structure is provides optimum solution in addressing the target requirements.