Genetic algorithms applied to the solution of hybrid optimal control problems in astrodynamics |
| |
Authors: | Bradley J. Wall Bruce A. Conway |
| |
Affiliation: | (1) Aerospace Engineering, Embry-Riddle Aeronautical University, Prescott, AZ, USA;(2) Aerospace Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA |
| |
Abstract: | Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions; a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm. This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal, and possibly optimal, missions in many fewer evaluations than total enumeration would require. |
| |
Keywords: | Hybrid optimal control Genetic algorithm Spacecraft trajectory optimization Global trajectory optimization competition (GTOC) Bilevel programming problem (BLPP) |
本文献已被 SpringerLink 等数据库收录! |
|