Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission

In space environment, perturbations make the spacecraft lose its predefined orbit in space. One of these undesirable changes is the in-plane rotation of space orbit, denominated as orbital precession. To overcome this problem, one option is to correct the orbit direction by employing low-thrust trajectories. However, in addition to the orbital perturbation acting on the spacecraft, a number of parameters related to the spacecraft and its propulsion system must be optimized. This article lays out the trajectory optimization of orbital precession missions using Evolutionary Algorithms (EAs). In this research, the dynamics of spacecraft in the presence of orbital perturbation is modeled. The optimization approach is employed based on the parametrization of the problem according to the space mission. Numerous space mission cases have been studied in low and middle Earth orbits, where various types of orbital perturbations are acted on spacecraft. Consequently, several EAs are employed to solve the optimization problem. Results demonstrate the practicality of different EAs, along with comparing their convergence rates. With a unique trajectory model, EAs prove to be an efficient, reliable and versatile optimization solution, capable of being implemented in conceptual and preliminary design of spacecraft for orbital precession missions.

[1]  Dario Izzo,et al.  Low-thrust trajectory design as a constrained global optimization problem , 2011 .

[2]  Hexi Baoyin,et al.  Low-cost transfer between asteroids with distant orbits using multiple gravity assists , 2015 .

[3]  Bernardetta Addis,et al.  A global optimization method for the design of space trajectories , 2011, Comput. Optim. Appl..

[4]  Yuan Ren,et al.  Low-thrust trajectory design with constrained particle swarm optimization , 2014 .

[5]  Royal Greenwich Observatory,et al.  The Astronomical Almanac for the year 2003 , 1999 .

[6]  Mauro Pontani.  Particle swarm optimization of ascent trajectories of multistage launch vehicles , 2014 .

[7]  A. H. Mazinan,et al.  Mathematical modeling of spacecraft guidance and control system in 3D space orbit transfer mission , 2016 .

[8]  Abolfazl Shirazi.  Analysis of a hybrid genetic simulated annealing strategy applied in multi-objective optimization of orbital maneuvers , 2017, IEEE Aerospace and Electronic Systems Magazine.

[9]  Alexander Mendiburu,et al.  A Distance-Based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem , 2014, IEEE Transactions on Evolutionary Computation.

[10]  Bruce A. Conway,et al.  Near-Optimal Low-Thrust Earth-Mars Trajectories via a Genetic Algorithm , 2005 .

[11]  Ya-zhong Luo,et al.  Simulated annealing for solving near-optimal low-thrust orbit transfer , 2005 .

[12]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[13]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[14]  J. Junkins,et al.  Analytical Mechanics of Space Systems , 2003 .

[15]  Slawomir J. Nasuto,et al.  Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories , 2007, J. Glob. Optim..

[16]  G. Genta,et al.  Optimal low-thrust trajectories for nuclear and solar electric propulsion , 2016 .

[17]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[18]  Bruce A. Conway,et al.  A Survey of Methods Available for the Numerical Optimization of Continuous Dynamic Systems , 2011, Journal of Optimization Theory and Applications.

[19]  Ryan P. Russell,et al.  Survey of spacecraft trajectory design in strongly perturbed environments , 2012 .

[20]  Bruce A. Conway,et al.  Spacecraft Trajectory Optimization: Contents , 2010 .

[21]  Alexander Mendiburu,et al.  Extending distance-based ranking models in estimation of distribution algorithms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[22]  Howard D. Curtis,et al.  Orbital Mechanics for Engineering Students , 2005 .

[23]  Massimiliano Vasile,et al.  Incremental planning of multi-gravity assist trajectories , 2015 .

[24]  Ya-zhong Luo,et al.  Optimization of multiple-impulse minimum-time rendezvous with impulse constraints using a hybrid genetic algorithm , 2006 .

[25]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[26]  Giulio Avanzini,et al.  Orbit transfer manoeuvres as a test benchmark for comparison metrics of evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[27]  Hanns Selig,et al.  Modelling of Solar Radiation Pressure Effects: Parameter Analysis for the MICROSCOPE Mission , 2015 .

[28]  John W. Dankanich.  Low-Thrust Mission Design and Application , 2010 .

[29]  Yufei Zhuang,et al.  Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm , 2014 .

[30]  I. Kolmanovsky,et al.  Enhanced Smoothing Technique for Indirect Optimization of Minimum-Fuel Low-Thrust Trajectories , 2016 .