Optimization of low-thrust many-revolution transfers and Lyapunov-based guidance

Li, Xinfeng

doi:10.1016/j.actaastro.2009.05.013

Cited by 21 publications

(8 citation statements)

References 14 publications

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“…In this work, the unconstrained costate vector is used as a new control variable for MPSP controller design. This idea also has been utilized in NOC design [14] and Lyapunov guidance design [32]. Notice from Eqs.…”

Section: Mpsp Dynamics and Controlmentioning

confidence: 99%

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Wang,

Topputo

2019

Preprint

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Model Predictive Static Programming (MPSP) was always used under the assumption of continuous control, which impedes it for applications with bang-off-bang control directly. In this paper, MPSP is employed for the first time as a guidance scheme for low-thrust transfers with bang-off-bang control where the fuel-optimal trajectory is used as the nominal solution. In our method, dynamical equations in Cartesian coordinates are augmented by the mass costate equation, while the unconstrained velocity costate vector is used as control variable, and is expressed as a combination of Fourier basis functions with corresponding weights. A two-loop MPSP algorithm is designed where the weights and the initial mass costate are updated in the inner loop and continuation is conducted on the outer loop in case of large perturbations. The sensitivity matrix (SM) is recursively calculated using analytical derivatives and SM at switching points is compensated based on calculus of variations. An sample interplanetary CubeSat mission to an asteroid is used as study case to illustrate the effectiveness of the method developed.

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Section: Mpsp Dynamics and Controlmentioning

confidence: 99%

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Wang,

Topputo

2019

Preprint

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“…On the one hand, he used a Lyapunov-based transfer trajectory as an initial guess for optimization to obtain the optimal transfer solution [18] . On the other hand, he investigated the time-varying control gains through constructing a mapping between parameterized control law and the Lyapunov control law, furthermore obtained optimal closed form solutions [19] .…”

Section: Introductionmentioning

confidence: 99%

Optimal strategy for low-thrust spiral trajectories using Lyapunov-based guidance

Yang

Gao

2015

Advances in Space Research

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“…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%

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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Optimization of low-thrust many-revolution transfers and Lyapunov-based guidance

Cited by 21 publications

References 14 publications

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Optimal strategy for low-thrust spiral trajectories using Lyapunov-based guidance

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

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