Analytic Calculation and Application of the Q-Law Guidance Algorithm Partial Derivatives

The closed-loop Q-Law guidance algorithm has been shown to be a very capable and efficient method for producing low-thrust trajectories. This paper poses a Q-Law optimization problem for computing locally optimal gain values and for enforcing nonlinear constraints on the initial state using nonlinea...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:The Journal of the astronautical sciences Ročník 70; číslo 3; s. 14
Hlavní autoři: Shannon, Jackson L., Ellison, Donald H., Hartzell, Christine M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 17.05.2023
Springer Nature B.V
Témata:
ISSN:2195-0571, 0021-9142, 2195-0571
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The closed-loop Q-Law guidance algorithm has been shown to be a very capable and efficient method for producing low-thrust trajectories. This paper poses a Q-Law optimization problem for computing locally optimal gain values and for enforcing nonlinear constraints on the initial state using nonlinear programming (NLP). Gradient-based optimization methods have been shown to benefit greatly when analytical partial derivatives are supplied to the optimizer. Therefore, we present derivations of the Q-Law thrust vector partial derivatives with respect to the Q-law gains as well as with respect to the spacecraft’s state. These partials are leveraged to produce a state transition matrix, which contains exact partial derivatives of the terminal state with respect to the NLP problem decision vector. The Q-Law NLP problem can be coupled with the Sims–Flanagan interplanetary model in the same optimization problem. In this approach, the NLP solver uses a Q-Law model to design the planetocentric capture/escape spirals and a Sims–Flanagan model to design the interplanetary legs, resulting in end-to-end trajectory optimization.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2195-0571
0021-9142
2195-0571
DOI:10.1007/s40295-023-00371-1