Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach
<|reference_start|>Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach: Implementation of Model Predictive Control (MPC) on hardware with limited computational resources remains a challenge. Especially for long-distance maneuvers that require small sampling times, the necessary horizon lengths prevent its application on onboard computers. In this paper, we propose a computationally efficient tubebased shrinking horizon MPC that is scalable to long prediction horizons. Using move blocking, we ensure that a given number of decision inputs is efficiently used throughout the maneuver. Next, a method to substantially reduce the number of constraints is introduced. The approach is demonstrated with a helicopter landing on an inclined platform using a prediction horizon of 300 steps. The constraint reduction decreases the computation time by an order of magnitude with a slight increase in trajectory cost.<|reference_end|>
@article{schitz2024robust,
    title={Robust Maneuver Planning With Scalable Prediction Horizons: A Move
  Blocking Approach},
    author={Philipp Schitz, Johann C. Dauer and Paolo Mercorelli},
    journal={IEEE Control Systems Letters, vol. 8, pp. 1907-1912, 2024},
    year={2024},
    doi={10.1109/LCSYS.2024.3414971},
    archivePrefix={arXiv},
    eprint={2408.08020},
    primaryClass={cs.RO cs.SY eess.SY}
}