TY - GEN
T1 - An Overview of X-TFC Applications for Aerospace Optimal Control Problems
AU - Schiassi, Enrico
AU - D’Ambrosio, Andrea
AU - Furfaro, Roberto
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This paper is an overview of Optimal Control Problems (OCPs) for aerospace applications tackled via the indirect method and a particular Physics-Informed Neural Networks (PINNs) framework, developed by the authors, named Extreme Theory of Functional Connections (X-TFC). X-TFC approximates the unknown OCP solutions via the Constrained Expressions, which are functionals made up of the sum of a free-function and a functional that analytically satisfies the boundary conditions. Thanks to this property, the framework is fast and accurate in learning the solution to the Two-Point Boundary Value Problem (TPBVP) arising after applying the Pontryagin Minimum Principle. The applications presented in this paper regard intercept problems, interplanetary planar orbit transfers, transfer trajectories within the Circular Restricted Three-Body Problem, and safe trajectories around asteroids with collision avoidance. The main results are presented and discussed, proving the efficiency of the proposed framework in solving OCPs and its low computational times, which can potentially enable a higher level of autonomy in decision-making for practical applications.
AB - This paper is an overview of Optimal Control Problems (OCPs) for aerospace applications tackled via the indirect method and a particular Physics-Informed Neural Networks (PINNs) framework, developed by the authors, named Extreme Theory of Functional Connections (X-TFC). X-TFC approximates the unknown OCP solutions via the Constrained Expressions, which are functionals made up of the sum of a free-function and a functional that analytically satisfies the boundary conditions. Thanks to this property, the framework is fast and accurate in learning the solution to the Two-Point Boundary Value Problem (TPBVP) arising after applying the Pontryagin Minimum Principle. The applications presented in this paper regard intercept problems, interplanetary planar orbit transfers, transfer trajectories within the Circular Restricted Three-Body Problem, and safe trajectories around asteroids with collision avoidance. The main results are presented and discussed, proving the efficiency of the proposed framework in solving OCPs and its low computational times, which can potentially enable a higher level of autonomy in decision-making for practical applications.
KW - Aerospace optimal control problems
KW - Extreme learning machines
KW - Machine learning
KW - Physics-informed neural networks
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U2 - 10.1007/978-3-031-25755-1_13
DO - 10.1007/978-3-031-25755-1_13
M3 - Conference contribution
SN - 9783031257544
T3 - Studies in Computational Intelligence
SP - 199
EP - 212
BT - The Use of Artificial Intelligence for Space Applications - Workshop at the 2022 International Conference on Applied Intelligence and Informatics
A2 - Ieracitano, Cosimo
A2 - Mammone, Nadia
A2 - Di Clemente, Marco
A2 - Mahmud, Mufti
A2 - Furfaro, Roberto
A2 - Morabito, Francesco Carlo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Applied Intelligence and Informatics , AII 2022
Y2 - 1 September 2022 through 3 September 2022
ER -