Evidence-Based Robust Optimization of Pulsed Laser Orbital Debris Removal Under Epistemic Uncertainty

An evidence-based robust optimization method for pulsed laser orbital debris removal (LODR) is presented. Epistemic type uncertainties due to limited knowledge are considered. The objective of the design optimization is set to minimize the debris lifetime while at the same time maximizing the corresponding belief value. The Dempster–Shafer theory of evidence (DST), which merges interval-based and probabilistic uncertainty modeling, is used to model and compute the uncertainty impacts. A Kriging based surrogate is used to reduce the cost due to the expensive numerical life prediction model. Effectiveness of the proposed method is illustrated by a set of benchmark problems. Based on the method, a numerical simulation of the removal of Iridium 33 with pulsed lasers is presented, and the most robust solutions with minimum lifetime under uncertainty are identified using the proposed method.

[1]  K. Baker,et al.  Removing orbital debris with lasers , 2011, 1110.3835.

[2]  Massimiliano Vasile,et al.  Evidence-based robust design of deflection actions for near Earth objects , 2012, ArXiv.

[3]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[4]  Massimiliano Vasile.  A behavioral-based meta-heuristic for robust global trajectory optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  A. Shamsai,et al.  Multi-objective Optimization , 2017, Encyclopedia of Machine Learning and Data Mining.

[6]  Earl H. Dowell,et al.  Proper Orthogonal Decomposition Reduced-Order Model for Nonlinear Aeroelastic Oscillations , 2014 .

[7]  W. Schall.  Laser Radiation for Cleaning Space Debris from Lower Earth Orbits , 2002 .

[8]  Ekaterina Auer,et al.  A verified realization of a Dempster–Shafer based fault tree analysis , 2011, Computing.

[9]  Tapabrata Ray,et al.  A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization , 2015, IEEE Transactions on Evolutionary Computation.

[10]  Stephen Kemble,et al.  Preliminary space mission design under uncertainty , 2010 .

[11]  Massimiliano Vasile.  Robust Mission Design Through Evidence Theory and Multiagent Collaborative Search , 2005, Annals of the New York Academy of Sciences.

[12]  Juan Du,et al.  Non-linear model reduction for the Navier-Stokes equations using residual DEIM method , 2014, J. Comput. Phys..

[13]  N. L. Johnson,et al.  Continuous Multivariate Distributions, Volume 1: Models and Applications , 2019 .

[14]  John E. Renaud,et al.  Uncertainty quantification using evidence theory in multidisciplinary design optimization , 2004, Reliab. Eng. Syst. Saf..

[15]  Piet Demeester,et al.  ooDACE toolbox: a flexible object-oriented Kriging implementation , 2014, J. Mach. Learn. Res..

[16]  C. Levit,et al.  Orbital debris–debris collision avoidance , 2011, 1103.1690.

[17]  T. S. Cssi Kelso,et al.  Analysis of the Iridium 33 Cosmos 2251 Collision , 2009 .

[18]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[19]  Yuanli Cai,et al.  Robust design optimization using integrated evidence computation — With application to Orbital Debris Removal , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[20]  T. Moon,et al.  Mathematical Methods and Algorithms for Signal Processing , 1999 .

[21]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[22]  C. Phipps,et al.  Pulsed laser interactions with space debris: Target shape effects , 2013, 1305.3659.

[23]  Wei Tan,et al.  Multi-fidelity design optimization under epistemic uncertainty , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[24]  D. Gavel,et al.  ORION: Clearing near-Earth space debris using a 20-kW, 530-nm, Earth-based, repetitively pulsed laser , 1996 .

[25]  R. Broucke.  Orbital motion , 1980 .