Q-Law Aided Direct Trajectory Optimization of Many-Revolution Low-Thrust Transfers

Shannon, Jackson; Ozimek, Martin T.; Atchison, Justin A.; Hartzell, C. M.

doi:10.2514/1.a34586

Cited by 32 publications

(29 citation statements)

References 16 publications

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“…Table 6 Comparison of state-dependent Q-law performance for GTO-GEO transfer, using a spacecraft with mass 1200kg, Isp 1800s and engine thrust 0.31N (given Power 5kW and efficiency 0.55). The time-of-flight results is within 0.85% of the orbit averaging result found in [2], and quoted in [7]. In addition, it differs from the 25% Q-law-75% NLP result presented in [7] by only 0.1%.…”

Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadosupporting

confidence: 76%

“…3c and 3d result in Fig 3e. Hence, we learn that the classical Q-law over emphasises the importance of the eccentricity, and instead, the contribution of both the inclination and semi-major axis need to be increased, particularly early on in the transfer, for a time-optimal solution. The classical Q-law is also known to overshoot the semi-major axis in order to make the inclination change less expensive [7,14]. However, this sacrifices time-of-flight and hence we see this trait removed when optimising for time-of-flight.…”

Section: A Gto-geo Transfer In Keplerian Dynamics -Case Amentioning

confidence: 96%

“…Here we consider both time-optimal and mass-optimal transfers for a similar GTO-GEO transfer with a larger ∆i, with J 2 and eclipse shadow effects (see Appendix V for the implementation). In this case, to match Shannon et al 2019 [7], the modelled spacecraft has mass 1200kg, Isp 1800s, Power 5kW and efficiency 0.55, giving a maximum engine thrust of 0.31N. The initial and target orbits are shown in Table 5.…”

Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadomentioning

confidence: 99%

“…For the mass-optimal case, we compare the same transfer to the results presented in [7], fixing the time-of-flight to 120.1 days. Given this time-fixed mass-optimal comparison is similar in time-of-flight to the time-optimal case, it is little surprise that the grey coasting regions seen in Fig 4b are quite short and focused at periapsis.…”

Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadomentioning

confidence: 99%

“…CLFD control laws only require knowledge of the current spacecraft state and the target state in order to determine the control to be applied. This makes them suitable as initial guesses for indirect and direct methods, as in [6,7], and they have great potential for on-board use. However, they are generally derived in low-fidelity dynamics and hence often struggle to perform in higher-fidelity dynamics.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters

Holt

Armellín

Scorsoglio

et al. 2020

AIAA Scitech 2020 Forum

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Low-thrust many-revolution trajectory design and orbit transfers are becoming increasingly important with the development of high specific impulse, low-thrust engines. Closed-loop feedback-driven (CLFD) control laws can be used to solve these trajectory design problems with minimal computational cost and offer potential for autonomous guidance. However, they have user-defined parameters which limit their optimality. In this work, an actor-critic reinforcement learning framework is proposed to make the parameters of the Lyapunov-based Q-law state-dependent, ensuring the controller can adapt as the dynamics evolve during a transfer. The proposed framework should be independent of the particular CLFD control law and provides improved solutions for mission analysis. There is also potential for future on-board autonomous use, as trajectories are closed-form and can be generated without an initial guess. The current results focus on GTO-GEO transfers in Keplerian dynamics and later with eclipse and J 2 effects. Both time-optimal and mass-optimal transfers are presented, and the stability to uncertainties in orbit determination are discussed. The task of handling orbit perturbations is left to future work.

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Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadosupporting

confidence: 76%

Section: A Gto-geo Transfer In Keplerian Dynamics -Case Amentioning

confidence: 96%

Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadomentioning

confidence: 99%

Section: Gto-geo Transfer In Keplerian Dynamics With J 2 and Shadomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters

Holt

Armellín

Scorsoglio

et al. 2020

AIAA Scitech 2020 Forum

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Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design

Shirazi

Holt

Armellín

et al. 2023

Springer Optimization and Its Applications

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Enhancements in evolutionary optimization techniques are rapidly growing in many aspects of engineering, specifically in astrodynamics and space trajectory optimization and design. In this chapter, the problem of optimal design of space trajectories is tackled via an enhanced optimization algorithm within the framework of Estimation of Distribution Algorithms (EDAs), incorporated with Lyapunov and Q-law feedback control methods. First, both a simple Lyapunov function and a Q-law are formulated in Classical Orbital Elements (COEs) to provide a closed-loop low-thrust trajectory profile. The weighting coefficients of these controllers are approximated with various degrees of Hermite interpolation splines. Following this model, the unknown time series of weighting coefficients are converted to unknown interpolation points. Considering the interpolation points as the decision variables, a black-box optimization problem is formed with transfer time and fuel mass as the objective functions. An enhanced EDA is proposed and utilized to find the optimal variation of weighting coefficients for minimum-time and minimum-fuel transfer trajectories. The proposed approach is applied in some trajectory optimization problems of Earth-orbiting satellites. Results show the efficiency and the effectiveness of the proposed approach in finding optimal transfer trajectories. A comparison between the Q-law and simple Lyapunov controller is done to show the potential of the potential of the EEDA in enabling the simple Lyapunov controller to recover the finer nuances explicitly given within the analytical expressions in the Q-law.

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Unconstrained Direct Optimization of Spacecraft Trajectories Using Many Embedded Lambert Problems

Ottesen

Russell

2021

J Optim Theory Appl

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Q-Law Aided Direct Trajectory Optimization of Many-Revolution Low-Thrust Transfers

Cited by 32 publications

References 16 publications

Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters

Low-Thrust Trajectory Design Using Closed-Loop Feedback-Driven Control Laws and State-Dependent Parameters

Time-Varying Lyapunov Control Laws with Enhanced Estimation of Distribution Algorithm for Low-Thrust Trajectory Design

Unconstrained Direct Optimization of Spacecraft Trajectories Using Many Embedded Lambert Problems

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