Multi-rendezvous low-thrust trajectory optimization using costate transforming and homotopic approach

Chen, Shiyu; Li, Haiyang; Baoyin, Hexi

doi:10.1007/s10509-018-3334-x

Cited by 19 publications

(5 citation statements)

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“…approach [5]- [7], switching detection method [8], [9], analytic derivatives [10], [11], and expressing orbital states in modified equinoctial elements [12], [13]. Methods concerning the guess of initial costates include initial costates normalization [5], [14], costate transformation [15], providing initial guesses by solving simplified linear equations [16], using Particle Swarm Optimization [17], shape-based methods [18], pseudospectral methods [19], and k-nearest neighbor methods [20], just to mention few.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Baoyin

Topputo

2019

IEEE Access

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In this paper, neural networks are trained to learn the optimal time, the initial costates, and the optimal control law of time-optimal low-thrust interplanetary trajectories. The aim is to overcome the difficult selection of first guess costates in indirect optimization, which limits their implementation in global optimization and prevents on-board applications. After generating a dataset, three networks that predict the optimal time, the initial costate, and the optimal control law are trained. A performance assessment shows that neural networks are able to predict the optimal time and initial costate accurately, especially a 100% success rate is achieved when neural networks are used to initialize the shooting function of indirtect methods. Moreover, learning the state-control pairs shows that neural networks can be utilized in real-time, on-board optimal control. INDEX TERMS Indirect methods, low-thrust trajectory optimization, initial costates, neural networks.

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Section: Introductionmentioning

confidence: 99%

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Baoyin

Topputo

2019

IEEE Access

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“…Besides Edelbaum's approach, some control laws are designed to implement low-thrust orbit raising tasks [11,12], but their optimality cannot be ensured. Indirect methods are applied a lot in optimizing low-thrust trajectories [13][14][15][16] because of its optimality. In indirect methods, the original optimal control problem is transformed into a two-point boundary value problem (TPBVP) which can be solved by shooting methods.…”

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confidence: 99%

Autonomous Time-Optimal Many-Revolution Orbit Raising for Electric Propulsion GEO Satellites via Neural Networks

Li,

Topputo,

Baoyin

2019

Preprint

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I. Introduction Epropulsion (EP) Geostationary Earth orbit (GEO) satellites are more efficient because they cost a lower fuel consumption and thus increase the payload mass or reduce the launch mass, compared to conventional chemical propulsion GEO satellites. EP has been tested and applied a lot in many deep space missions, and its application to the Earth orbit satellites has been a growing interest [1,2]. The world-first all-electric GEO platform, Boeing 702SP, has already been launched in March 2015 by a Falcon 9 rocket. China also launched its first EP-GEO satellite in April 2017. The design of GEO satellites is rapidly changing from classic high-thrust propulsion more and more toward low-thrust propulsion. Low-thrust orbit raising brings in a many-revolution long-duration transfer that is a special characteristic of EP-GEO satellites.Autonomous orbit raising is necessary for future EP-GEO missions because the support from the ground is expensive when transfer time is very long, for instance, hundreds of days for low-thrust orbit raising from low Earth orbit (LEO) to GEO. Recently, neural networks (NNs) have attracted the interest of many researchers and have had applications in various fields in recent years, including astrodynamics [3]. For landing problems and short-duration orbit transfers, NNs are trained to learn the optimal state-control pairs [4-6] and image-control relations [7]. Their results show that NNs have excellent performance in learning the optimal control of short-duration problems. In this Note, NNs are trained to learn the optimal control of many-revolution long-duration problems, that is, time-optimal low-thrust orbit raising problems.Optimization of time-optimal low-thrust orbit raising problems is necessary before training neural networks.The many-revolution long-duration characteristic makes it extremely difficult for numerical optimization methods to converge. Some methods based on Edelbaum's approach [8] have been proposed under the assumption of quasi-

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“…In two-body dynamics, numerical results show that the fuel consumption of an impulsive trajectory with a Venus-Earth-Venus swingby sequence is 41% with flight time of 1694 days (Lei et al 2017). Moreover, electric propulsion (EP) is also an efficient technique to save the fuel consumption (Chen et al 2018). EP was applied in the last leg of an impulsive transfer trajectory, i.e., from Venus to 2010 TK 7 , transforming the impulsive leg into a low-thrust leg (Lei et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…However, direct methods require a large amount of computation and may not converge to the optimal solution. As for an indirect method, the optimization problem is usually turned into a two-point boundary-value problem (TPBVP) or multi-point boundary-value problem (MPBVP) (Haberkorn et al 2004;Jiang et al 2012;Chen et al 2018). Indirect methods provide assurances that the firstorder necessary conditions are satisfied (Morante et al 2021).…”

Section: Introductionmentioning

confidence: 99%

A Rendezvous Mission to the Second Earth Trojan Asteroid 2020 XL₅ with Low-Thrust Multi-Gravity Assist Techniques

Yang,

Xu,

2024

Res. Astron. Astrophys.

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As the second Earth's Trojan asteroid, 2020 XL5 is worthy of rendezvous and even sample return missions in many aspects. In this paper, a rendezvous mission to Earth's second Trojan asteroid 2020 XL5 is proposed. However, due to its high inclination and large eccentricity, direct impulsive transfer requires large amounts of fuel consumption. To address this challenge, we explore the benefits of electric propulsion and multi-gravity assists techniques for interplanetary missions. These two techniques are integrated in this mission design. The design of low-thrust gravity-assists (LTGA) trajectory in multi-body dynamics is thoroughly investigated, which is a complex process. A comprehensive framework including three steps is presented here for optimization of LTGA trajectories in multi-body dynamics. The rendezvous mission to 2020 XL5 is designed with this three-step approach. The most effective transfer sequence among the outcomes involves Earth-Venus-Earth-Venus-2020 XL5. Numerical results indicates that the combination of electric propulsion and multi-gravity assists can greatly reduce the fuel consumption, with fuel consumption of 9.03\%, making it a highly favorable choice for this rendezvous mission.

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Multi-rendezvous low-thrust trajectory optimization using costate transforming and homotopic approach

Cited by 19 publications

References 19 publications

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Autonomous Time-Optimal Many-Revolution Orbit Raising for Electric Propulsion GEO Satellites via Neural Networks

A Rendezvous Mission to the Second Earth Trojan Asteroid 2020 XL₅ with Low-Thrust Multi-Gravity Assist Techniques

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Multi-rendezvous low-thrust trajectory optimization using costate transforming and homotopic approach

Cited by 19 publications

References 19 publications

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Neural Networks in Time-Optimal Low-Thrust Interplanetary Transfers

Autonomous Time-Optimal Many-Revolution Orbit Raising for Electric Propulsion GEO Satellites via Neural Networks

A Rendezvous Mission to the Second Earth Trojan Asteroid 2020 XL5 with Low-Thrust Multi-Gravity Assist Techniques

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A Rendezvous Mission to the Second Earth Trojan Asteroid 2020 XL₅ with Low-Thrust Multi-Gravity Assist Techniques