More Works from Our Lab

Recursively Feasible Chance-Constrained Model Predictive Control Under Gaussian Mixture Model Uncertainty

Our previous work

IEEE Transactions on Control Systems Technology

PRF-MPC: Probabilistic Recursively Feasible Model Predictive Control
Under Uncertain Environments

Hyeontae Sung, Hyeongchan Ham, Junyoung Park, Kai Ren, and Heejin Ahn
Submitted to IFAC 2026
arXiv
PRF-MPC teaser animation
Lane-changing scenario - label

The nominal MPC (green) fails to retain feasibility during closed-loop execution, while PRF-MPC (blue) guarantees recursive feasibility until the goal is reached

Abstract

Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic Recursively Feasible Model Predictive Control (PRF-MPC) framework that guarantees recursive feasibility with a specified probability. We introduce properties that an ideal predictor should satisfy to ensure distributional consistency, and use these properties to derive closed-form expressions for the means and covariances of trajectories predicted at future time steps. Building on this analysis, we construct safety constraints that ensure, with high probability, that the current safe set is contained within the safe sets at future time steps, thereby probabilistically guaranteeing recursive feasibility. Simulation results on a lane-change scenario demonstrate that the proposed method significantly improves recursive feasibility.

Results

Probabilistic Recursively Feasible Model Predictive Control (PRF-MPC) significantly improves recursive feasibility over nominal MPC approaches with low computational costs.

Table 1: Simulation results.

Planning RF Rate (%) Cost dmin Comp. Time (s)
Nominal MPC 88.2 25.15 4.74 0.034
PRF-MPC 99.2 69.38 4.95 0.034

RF Rate denotes the fraction of closed-loop simulations that remain recursively feasible. Cost denotes the 2-norm of the deviation of closed-loop trajectories from the reference trajectory. dmin indicates the minimum distance to obstacles averaged over all runs. Comp. Time denotes the average worst-case solve time.

BibTeX

@article{YourPaperKey2024,
  title={Your Paper Title Here},
  author={First Author and Second Author and Third Author},
  journal={Conference/Journal Name},
  year={2024},
  url={https://your-domain.com/your-project-page}
}