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Optimal Maneuvers Around Binary Asteroids Using Particle Swarm Optimization and Machine Learning

University of Rome “La Sapienza”, 00138 Rome, Italy

*Ph.D. Candidate, Automation, Robotics and Control for Aerospace Laboratory, School of Aerospace Engineering, Via Salaria 851; .

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Andrea Carbone

University of Rome “La Sapienza”, 00138 Rome, Italy

†Graduate Student, Automation, Robotics and Control for Aerospace Laboratory, School of Aerospace Engineering, Via Salaria 851; .

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and

Fabio Curti

https://orcid.org/0000-0002-7282-7221

University of Rome “La Sapienza”, 00138 Rome, Italy

‡Associate Professor, Automation, Robotics and Control for Aerospace Laboratory, School of Aerospace Engineering, Via Salaria 851; (Corresponding Author).

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Published Online:5 Jun 2023https://doi.org/10.2514/1.A35317

Abstract

Designing optimal transfer trajectories and reference orbit tracking in binary asteroid systems is both challenging and computationally expensive. This paper proposes a method of bypassing the high computational overhead by leveraging a collection of known techniques. Indeed, the proposed framework is based on the combination of artificial intelligence techniques, such as the particle swarm optimization and neural networks, along with the inverse dynamics and the B-splines approximation of the trajectory. The real irregular shapes of the asteroids are considered in the free dynamics of the system, which are obtained via the mutual polyhedral model. The gravitational accelerations of the single asteroids acting on the spacecraft are approximated by using two single-layer neural networks trained via an extreme learning machine. By using a combination of these techniques, the computational time of the whole optimization is decreased from hours to minutes. The proposed approach is applied to the optimal trajectory design around the binary asteroid system, 1999 KW4, showing the feasibility of the proposed optimization approach, reducing the computational effort and time, and increasing the reliability of the obtained results. It is shown through a Monte Carlo analysis that our optimization strategy yields more accurate solutions than other optimization algorithms, such as the interior point and sequential quadratic programming methods, when a random initial guess is provided. Finally, the proposed optimization approach can be used in combination with other techniques to provide a feasible and reliable initial guess for a better solution refinement.t

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Volume 60, Number 5September 2023

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This work was presented as American Astronautical Society (AAS) Paper 21-260 at the 31st AAS/AIAA Space Flight Mechanics Meeting, which took place virtually 1–4 February 2021.

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Received28 November 2021
Accepted30 March 2023
Published online5 June 2023

Optimal Maneuvers Around Binary Asteroids Using Particle Swarm Optimization and Machine Learning

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