Robust Optimization with Tchebysheff Decomposition for Mars Entry Probe Design

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 288)


An evidence based robust design optimization method with Tchebycheff decomposition is proposed for micro Mars probe design under epistemic uncertainty. Super-formula based super-ellipse is used for the probe geometric configuration instead of the conventional sphere-cone configuration. Evidence based multi-objective optimization(MOO) method is used to optimally design the probe. The MOO problem is casted into a set of scalar optimization problems with Tchebycheff decomposition. Individuals are grouped with an adaptive clustering algorithm. In each cluster,individuals are analyzed with Proper Orthogonal Decomposition(POD) technique, and sorted by the ”energy” levels occupied by the components. A new population is produced by projecting the cluster centroid to the principal component vectors, modeling the distribution and reproducing new individuals. A strategy similar to steepest descend method in single-objective optimization is implemented for reproducing the new population, pushing forward the front to the true Pareto front. Performance and efficiency of the new algorithm are tested on a set of standard benchmark test problems. To reduce computational cost of evidence computation, an Evolutionary Binary Tree (EBT) algorithm and response surface model is employed. Finally, numerical simulation of a Mars micro probe heat shield design with the proposed optimization algorithm is presented.


robust optimization Tchebysheff decomposition Mars entry probe 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Astronautic DynamicsXi’an Satellite Control CenterXi’anChina
  2. 2.Xi’an Jiaotong UniversityXi’anChina

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