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Area of Science:

  • Robotics and Control Systems
  • Automotive Engineering
  • Computational Optimization

Background:

  • Model Predictive Control (MPC) is crucial for constrained systems like automated lane-keeping.
  • High computational complexity of traditional MPC limits its application to slower systems.
  • Explicit MPC addresses this by using multi-parametric quadratic programming (mp-QP).

Purpose of the Study:

  • To develop an explicit Model Predictive Control (MPC) strategy for automated lane-keeping systems.
  • To enable autonomous vehicles to accurately follow various paths (straight, circular, clothoid) at high speeds.
  • To reduce the computational burden associated with MPC for real-time applications.

Main Methods:

  • Employed explicit Model Predictive Control (MPC) utilizing multi-parametric quadratic programming (mp-QP).
  • Investigated methods for selecting weighting matrices and control horizons for path-following.
  • Utilized CarSim for realistic vehicle dynamics simulation and validation.

Main Results:

  • The proposed explicit MPC controller effectively determines optimal front steering angles for path tracking.
  • Simulations demonstrated the controller's capability to handle straight, circular, and clothoid paths at high entry speeds.
  • Performance was validated against traditional MPC, Linear-Quadratic Regulator (LQR), and driver models.

Conclusions:

  • Explicit MPC provides a computationally efficient solution for high-performance automated lane-keeping.
  • The controller enables precise path following for autonomous vehicles in diverse scenarios.
  • The study validates the effectiveness and reliability of the proposed explicit MPC approach.