Low thrust minimum-fuel orbital transfer: a homotopic approach
By T. Haberkorn, Pierre Martinon, J. Gergaud. 2004
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We describe in this paper the study of an earth orbital transfer with a low thrust (typically electro-ionic) propulsion system. The objective is the maximization of the final mass, which leads to a discontinuous control with a huge number of thrust arcs. The resolution method is based on single shooting, combined to a homotopic approach in order to cope with the problem of the initial guess, which is actually critical for non-trivial problems. An important aspect of this choice is that we make no assumptions on the control structure, and in particular do not set the number of thrust arcs. This strategy allowed us to solve our problem (a transfer from Low Earth Orbit to Geosynchronous Equatorial Orbit, for a spacecraft with mass of 1500 kgs, either with or without a rendezvous) for thrusts as low as 0.1N, which corresponds to a one-year transfer involving several hundreds of revolutions and thrust arcs. The numerical results obtained also revealed strong regularity in the optimal control structure, as well as some practically interesting empiric laws concerning the dependency of the final mass with respect to the transfer time and maximal thrust.
Subfields: Aerospace Engineering, Astronomy and Astrophysics
View Paper: https://www.semanticscholar.org/paper/17ad21cb15a7365beaa44f02087cdc33163841b5
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In Aerospace Engineering
In Astronomy and Astrophysics
Citations in the corpus
- Constructive Methods for Initialization and Handling Mixed State-Input Constraints in Optimal Control (2008, background): “They appear to be well suited for aerospace trajectory optimization problems and are currently used, along with ideas fromnonlinear geometric control, on various ascent and reentry problems.”
- Practical Techniques for Low-Thrust Trajectory Optimization with Homotopic Approach (2012, methodology): “[18] constructed the easier problem with the performance index of the integral of the quadratic of thrust magnitude, which is called the energy-optimal problem.”