Nanosatellite Cluster Keeping Under Thrust Uncertainties

Zhang, Hao; Gurfil, Pini

doi:10.2514/1.g000554

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Recalling that, for near-circular orbits, e ∼ OJ 2 , Δ e has a minor influence on the secular drift compared with the effects of Δ a and Δ i [6]. Thus, the J 2 -invariance condition in the current context becomes…”

Section: Lyapunov Control For Cluster Flightmentioning

confidence: 94%

“…We note that the purpose of this work is to derive astrodynamical conditions for the optimal target values, and not to present a new satellite control algorithm; the algorithm used herein is one of many that use constant thrust. In this context, measurement uncertainties do not play an important role, because adaptive control and/or an estimator can be integrated into the control algorithm, restoring the nominal performance (most likely on account of convergence time and fuel consumption) [6,18].…”

Section: Lyapunov Control For Cluster Flightmentioning

confidence: 99%

“…In addition, it is assumed herein that the satellite thruster is constrained to provide fixed-magnitude thrust, which is a common situation in practical applications (e.g., nanosatellites using cold-gas thrusters [6]). Semimajor axis control and inclination control are decoupled, and the thrust vector's direction is controlled by the attitude control system, enabling control of each variable separately along different arcs of the orbits, as will be explained later on.…”

Section: Lyapunov Control For Cluster Flightmentioning

confidence: 99%

“…Hence, cluster flight mitigates the strict requirements of relative position maintenance and relative navigation. However, active control is still needed for cluster keeping [6].…”

Section: Introductionmentioning

confidence: 99%

“…The only constraint guaranteeing zero relative drift according to the linearized differential mean elements model requires that the mean semimajor axis, inclination, and eccentricity of the satellites are identical. For cluster flight on near-circular orbits, it is sufficient to equalize the mean semimajor axis and inclination only [6].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Target States for Satellite Cluster Flight Control on Near-Circular Orbits

Zimmerman

Gurfil

2015

Journal of Guidance, Control, and Dynamics

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One of the methods for maintaining a cluster of satellites in long-term bounded relative distances is keeping the satellites on near-circular orbits having the same mean semimajor axes and mean inclinations. This approach allows some freedom in determining the reference mean semimajor axis and reference mean inclination for the cluster. In this paper, this freedom is used to find the optimal target values of the mean semimajor axis and mean inclination, with the optimization criteria being either the total propellant consumption of the cluster or the fuel consumption differences among satellites. The optimization problems are solved analytically, assuming that a fixed-magnitude thruster is used for closed-loop orbit control, and new results are presented, providing simple closed-form expressions for the optimal target states. Simulations are used for validating the results, showing that much propellant can be saved by properly setting the cluster reference orbit. Nomenclature a, a = semimajor axis, mean semimajor axis, m a ⋆ η = optimal target semimajor axis, m C D = drag coefficient e, e = eccentricity, mean eccentricity f = true anomaly, rad h = orbital angular momentum, m 2 ∕s i, i = inclination, mean inclination, rad i ⋆ η = optimal target inclination, rad J 2 = second-order zonal harmonic M = mean anomaly, rad m = mass, kg n = mean motion, rad∕s r = magnitude of the position vector, m u h = acceleration in the cross-track direction, m∕s 2 u t = acceleration in the velocity direction, m∕s 2 v = speed, m∕s Δm = propellant consumption, kg ϵ = efficiency parameter θ = argument of latitude, rad μ = standard gravitational parameter, m 3 ∕s 2 ϒ = maximum propellant consumption difference, kg ω = argument of perigee, rad Ω = argument of the ascending node, rad

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Section: Lyapunov Control For Cluster Flightmentioning

confidence: 94%

Section: Lyapunov Control For Cluster Flightmentioning

confidence: 99%

Section: Lyapunov Control For Cluster Flightmentioning

confidence: 99%

“…Hence, cluster flight mitigates the strict requirements of relative position maintenance and relative navigation. However, active control is still needed for cluster keeping [6].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations