TY - GEN
T1 - Learning Model Predictive Control for Quadrotors
AU - Li, Guanrui
AU - Tunchez, Alex
AU - Loianno, Giuseppe
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding-horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)×R3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.
AB - Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding-horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)×R3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=85136319925&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136319925&partnerID=8YFLogxK
U2 - 10.1109/ICRA46639.2022.9812077
DO - 10.1109/ICRA46639.2022.9812077
M3 - Conference contribution
AN - SCOPUS:85136319925
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5872
EP - 5878
BT - 2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Robotics and Automation, ICRA 2022
Y2 - 23 May 2022 through 27 May 2022
ER -