Robust Maneuver Planning With Scalable Prediction Horizons: A Move Blocking Approach
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in: IEEE Control Systems Letters, Jahrgang 8, 2024, S. 1907-1912.
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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TY - JOUR
T1 - Robust Maneuver Planning With Scalable Prediction Horizons
T2 - A Move Blocking Approach
AU - Schitz, Philipp
AU - Dauer, Johann C.
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2024
Y1 - 2024
N2 - 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 letter, we propose a computationally efficient tube-based 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.
AB - 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 letter, we propose a computationally efficient tube-based 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.
KW - autonomous systems
KW - computational methods
KW - Predictive control for linear systems
KW - robotics
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=85196106007&partnerID=8YFLogxK
U2 - 10.1109/LCSYS.2024.3414971
DO - 10.1109/LCSYS.2024.3414971
M3 - Journal articles
AN - SCOPUS:85196106007
VL - 8
SP - 1907
EP - 1912
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
SN - 2475-1456
ER -