Model Predictive Control for Autonomous Vehicle Tracking

Authors

  • Vu Trieu Minh School of Engineering, Tallinn University of Technology, Tallinn, Estonia
  • Reza Moezzi Institute for Nanomaterials, Advanced Technologies and Innovation, Technical University of Liberec, Czech Republic
  • Klodian Dhoska Department of Production and Management, Faculty of Mechanical Engineering, Polytechnic University of Tirana, Albania
  • John Pumwa Department of Mechanical Engineering, Papua New Guinea University of Technology, Papua New Guinea

DOI:

https://doi.org/10.15157/IJITIS.2021.4.1.560-603

Keywords:

Model predictive control, autonomous vehicle, feasible path, optimal tracking.

Abstract

This study develops model predictive control (MPC) schemes for controlling autonomous vehicles tracking on feasible trajectories generated from flatness or polynomial equations. All of the vehicle online moving parameters including coordinate positions, body orientation angle, and steering angle are included into the MPC optimizer for calculating the real-time optimal inputs for the vehicle linear velocity and its steering velocity to minimize the errors between the desired and the actual course of travel. The use of MPC can simplify and eliminate the complexity of controller design since MPC can work itself as a system modelling controller. MPC can also handle on-line the constraints of any variables exceeding their limits. However the high computational demands are the main challenge for this method applying for the real applications.

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Published

2020-12-10

How to Cite

Minh, V. T., Moezzi, R., Dhoska, K., & Pumwa, J. (2020). Model Predictive Control for Autonomous Vehicle Tracking. International Journal of Innovative Technology and Interdisciplinary Sciences, 4(1), 560–603. https://doi.org/10.15157/IJITIS.2021.4.1.560-603

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