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arXiv · 2408.08020

Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach

Philipp Schitz·Johann C. Dauer·Paolo Mercorelli
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Abstract

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.

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Philipp Schitz, Johann C. Dauer, Paolo Mercorelli. 2024-08-15. Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach. https://doi.org/10.1109/lcsys.2024.3414971

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