Nonlinear Model Approximation Methods for Off-road Vehicle Path Tracking with MPC

Lucas  C. Sousa; Helon  H. V. Ayala

doi:10.48011/asba.v2i1.1364

Lucas C. Sousa Pontifícia Universidade Católica do Rio de Janeiro
Helon H. V. Ayala Pontifícia Universidade Católica do Rio de Janeiro

DOI: https://doi.org/10.48011/asba.v2i1.1364

Keywords: Path tracking, Linearization models, Model predictive control, Off-road autonomous vehicles

Abstract

This paper presents a comparative study between two different approaches for dynamic systems based on Taylor series and based on a double-integrator, and their influence in path tracking control using model predictive controller (MPC) with the view to solve the path tracking problem of an autonomous off-road vehicle. A physical-mathematical model, including the celebrated tire model known as “Magic Formula”, is modified and linearized to control the steering of the vehicle considering different reference trajectory inputs. Simulation results are considered satisfactory, showing that the model linearized considering Taylor series presented better results compared to the double-integrator which indicates that the prediction of the plant output is driven close to the reference. Moreover, the results show that the implemented control strategies for path tracking are adequate for applications in off-road autonomous vehicles.