Indirect Optimization of Fuel-Optimal Many-Revolution Low-Thrust Transfers With Eclipses

Wang, Y.; Topputo, Francesco

doi:10.1109/taes.2022.3189330

Cited by 16 publications

(6 citation statements)

References 39 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 18) the detection of the switching time 𝑡 𝑠 is essential because discontinuities cause accumulation of the integration error around those instants, and because it is necessary to introduce the compensation matrices 𝚿(𝑡 𝑠 ) through Eq. (20). However, no prior information on the structure of the thrusting profile is known.…”

Section: B Switching Detection Techniquementioning

confidence: 99%

“…This is particularly true as the number of coasting arcs to be imposed increases and their duration decreases. For this reason, a triple continuation scheme based on the eclipses imposition in [20] is implemented here. The approach called 𝑁 𝑚𝑎𝑥 continuation, gradually turns inactive coasting arcs into active ones, i.e., the maximum number of forced coasting arcs 𝑁 𝑚𝑎𝑥 increases at each iteration of the continuation scheme.…”

Section: Triple Continuation Schemementioning

confidence: 99%

“…They allow the solution of the original, difficult, and discontinuous problem, starting from an easier one [16]. It is usually employed in indirect methods to overcome discontinuity problems as bang-bang control in fuel optimal solutions [17,18], to impose varying maximum thrust models [19] and shutdown of the thruster due to eclipses [20]. Recently, the problem of forced coasting arcs imposition has become more and more relevant, and homotopic techniques have been employed to solve it [21,22] also in direct formulations [23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Indirect Formulation of Operational Compliant Low-thrust Trajectories

Mannocchi,

Giordano,

Topputo

2024

AIAA SCITECH 2024 Forum

Self Cite

View full text Add to dashboard Cite

Deep-space missions will be performed in the future by several stand-alone CubeSats. For limited budget reasons, these spacecraft need to follow operational compliant trajectories: transfers with an alternation of coasting and thrusting periods imposed at pre-defined time instants, the duty cycles. Traditional trajectory optimization algorithms exhibit convergence problems when handling this kind of discontinuous constraints. In this work, an efficient and robust indirect method to compute operational compliant trajectories is formulated through the imposition of coasting arcs as interior-point constraints. Results show that the computed trajectories are similar to the optimal ones with no operational constraint, both in terms of thrusting profile and propellant mass.

show abstract

Section: B Switching Detection Techniquementioning

confidence: 99%

Section: Triple Continuation Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Indirect Formulation of Operational Compliant Low-thrust Trajectories

Mannocchi,

Giordano,

Topputo

2024

AIAA SCITECH 2024 Forum

Self Cite

View full text Add to dashboard Cite

show abstract

“…Regarding the quadratic perturbing function, it serves as a bridge connecting an energy-optimal problem to a fuel-optimal problem and has been utilized in numerous studies. To cite some examples, it was employed in [17] to derive low−thrust fuel−optimal trajectories, in [18] to calculate fuel−optimal low−thrust Earth−orbit transfers, accounting for shadow eclipses, and in [19] to study fuel optimal soft landing trajectories on asteroids. As an example of the logarithmic perturbing function, it is exploited by Izzo and Öztürk [20] to obtain the fuel−optimal trajectories required to build a dataset and train a Deep Neural Network (DNN) in a supervised fashion.…”

Section: Introductionmentioning

confidence: 99%

Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks

D’Ambrosio,

Furfaro

2024

Aerospace

View full text Add to dashboard Cite

This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control problems through indirect methods. Specifically, PoNNs learn to solve the Two-Point Boundary Value Problem derived from the application of the Pontryagin Minimum Principle to the problem’s Hamiltonian. Within PoNNs, the Extreme Theory of Functional Connections (X-TFC) is leveraged to approximate states and costates using constrained expressions (CEs). These CEs comprise a free function, modeled by a shallow neural network trained via Extreme Learning Machine, and a functional component that consistently satisfies boundary conditions analytically. Addressing discontinuous control, a smoothing technique is employed, substituting the sign function with a hyperbolic tangent function and implementing a continuation procedure on the smoothing parameter. The proposed methodology is applied to scenarios involving fuel-optimal Earth−Mars interplanetary transfers and Mars landing trajectories. Remarkably, PoNNs exhibit convergence to solutions even with randomly initialized parameters, determining the number and timing of control switches without prior information. Additionally, an analytical approximation of the solution allows for optimal control computation at unencountered points during training. Comparative analysis reveals the efficacy of the proposed approach, which rivals state-of-the-art methods such as the shooting technique and the adaptive Gaussian quadrature collocation method.

show abstract

“…Homotopy, or continuation, is a suitable method to deal with discontinuous structures. The homotopic approach allows solving the original, difficult and discontinuous problem, starting from an easier and affordable one [ 19 , 34 ]. In particular, it has been applied to indirect methods to overcome discontinuity problems as bang-bang control in fuel optimal solutions [ 3 , 11 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

A Homotopic Direct Collocation Approach for Operational-Compliant Trajectory Design

2022

Self Cite

View full text Add to dashboard Cite

Stand-alone deep-space CubeSats are the future of the space sector. For limited budget reasons, these spacecraft need to follow operational-compliant (OC) trajectories: transfers with thrusting and coasting periods imposed at pre-defined time instants. Traditional trajectory optimisation algorithms exhibit convergence problems when handling discontinuous constraints. In this work, a homotopic direct collocation approach is presented. It employs a continuation algorithm that maps the classical bang-bang trajectory of a fuel-optimal low-thrust problem into an OC solution. M-ARGO CubeSat mission is considered as case study for validation, including a realistic thruster model with variable specific impulse and maximum thrust. The trajectories computed with the developed algorithm are compared with non-operational-compliant solutions. Our algorithm produces transfers similar to the optimal solutions with no operational constraint, both in terms of thrusting profile and propellant mass.

show abstract

Indirect Optimization of Fuel-Optimal Many-Revolution Low-Thrust Transfers With Eclipses

Cited by 16 publications

References 39 publications

An Indirect Formulation of Operational Compliant Low-thrust Trajectories

An Indirect Formulation of Operational Compliant Low-thrust Trajectories

Learning Fuel-Optimal Trajectories for Space Applications via Pontryagin Neural Networks

A Homotopic Direct Collocation Approach for Operational-Compliant Trajectory Design

Contact Info

Product

Resources

About