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Related Experiment Video

Updated: Sep 1, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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LTV-MPC Approach for Automated Vehicle Path Following at the Limit of Handling.

Ádám Domina1, Viktor Tihanyi1

  • 1Department of Automotive Technologies, Budapest University of Technology and Economics, 1111 Budapest, Hungary.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary

A new linear time-varying model predictive controller (LTV-MPC) offers efficient automated vehicle path following. This method reduces computational costs associated with nonlinear controllers by linearizing vehicle dynamics.

Keywords:
automated vehiclemodel predictive controllerpath followingsuccessive linearizationvehicle dynamics

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Area of Science:

  • Robotics and Control Systems
  • Automotive Engineering
  • Artificial Intelligence

Background:

  • Nonlinear Model Predictive Controllers (MPC) are increasingly used for vehicle path following.
  • High computational cost remains a significant drawback of nonlinear MPC algorithms.
  • Efficient path-following control is crucial for the advancement of autonomous driving systems.

Purpose of the Study:

  • To propose a computationally efficient linear time-varying model predictive controller (LTV-MPC) for automated vehicle path following.
  • To reduce the computational burden of nonlinear MPC by introducing novel linearization techniques.
  • To investigate the impact of steering system dynamics on path-following performance.

Main Methods:

  • Developed a novel path transformation method based on the vehicle's current state.
  • Applied successive linearization to obtain a state-space representation for vehicle prediction.
  • Modeled steering dynamics using a first-order lag and integrated it into the control framework.
  • Separately controlled longitudinal dynamics with a PI cruise controller.

Main Results:

  • The proposed LTV-MPC effectively reduces computational complexity compared to nonlinear approaches.
  • The controller successfully generates optimal steering control vectors at each time step.
  • Evaluation demonstrates the controller's performance and the influence of the steering model.

Conclusions:

  • The developed LTV-MPC provides an efficient solution for automated vehicle path following.
  • The linearization techniques successfully mitigate the computational cost of MPC.
  • Accurate modeling of steering dynamics is important for robust path-following control.