TY - GEN
T1 - A model predictive control approach for in-flight acoustic constraint compliance
AU - Ackerman, Kasey A.
AU - Gregory, Irene M.
AU - Theodorou, Evangelos A.
AU - Hovakimyan, Naira
N1 - Funding Information:
The authors would like to thank Dr. Kyle Pascioni of the Aeroacoustics Branch at NASA Langley Research Center for providing the acoustic model used in this work and Javier Puig Navarro of the Autonomous Integrated Systems Research Branch at NASA Langley Research Center for providing a distance query algorithm used to compute the minimum distance to polytopic acoustic observers. This research is supported by the NASA Revolutionary Vertical Lift Technology Project.
Publisher Copyright:
© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.
AB - Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.
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M3 - Conference contribution
AN - SCOPUS:85099853563
SN - 9781624106095
T3 - AIAA Scitech 2021 Forum
SP - 1
EP - 12
BT - AIAA Scitech 2021 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
Y2 - 11 January 2021 through 15 January 2021
ER -