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Analysis of explicit model predictive control for path-following control.

PloS oneยท2018
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Related Experiment Video

Updated: Feb 1, 2026

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
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Explicit model predictive control for linear time-variant systems with application to double-lane-change maneuver.

Junho Lee1, Hyuk-Jun Chang2

  • 1Department of Secured Smart Electric Vehicle, Kookmin University, Seoul, Republic of Korea.

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Summary
This summary is machine-generated.

Explicit model predictive control (eMPC) offers a computationally efficient alternative to model predictive control (MPC) for autonomous driving. This study presents a robust eMPC for varying vehicle speeds, outperforming traditional methods in simulations.

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Area of Science:

  • Control Systems Engineering
  • Automotive Engineering
  • Robotics

Background:

  • Model Predictive Control (MPC) faces computational challenges in real-time applications.
  • Explicit Model Predictive Control (eMPC) offers a computationally efficient solution while maintaining performance.
  • Vehicle dynamics are often simplified to Linear Time-Invariant (LTI) systems, neglecting velocity variations.

Purpose of the Study:

  • Design and evaluate an eMPC for the Double Lane Change (DLC) maneuver in autonomous driving.
  • Develop a robust eMPC strategy for discrete-time Linear Time-Variant (LTV) systems.
  • Enhance eMPC performance by accommodating variations in vehicle longitudinal velocity.

Main Methods:

  • Designed an eMPC controller for autonomous vehicle trajectory tracking.
  • Implemented an add-on unit to handle parameter variations (longitudinal velocity) in the eMPC.
  • Utilized CarSim software for vehicle dynamics simulation and controller performance evaluation.

Main Results:

  • The proposed eMPC demonstrated superior performance compared to a standard driver model in CarSim at high velocities.
  • The eMPC designed for LTV systems showed enhanced performance over the LTI-based eMPC.
  • The add-on unit effectively managed parameter variations without altering the core eMPC solution.

Conclusions:

  • The developed eMPC is effective for autonomous driving maneuvers like DLC.
  • The eMPC approach for LTV systems provides robustness against parameter variations, particularly vehicle speed.
  • This method offers a promising direction for computationally efficient and robust control in autonomous vehicles.