Optimization of perturbed and constrained fuel-optimal impulsive rendezvous using a hybrid approach

The perturbed and constrained fuel-optimal impulsive rendezvous problem is formulated, developed, and solved. A non-linear programming model is established for the general perturbed fuel-optimal multiple-impulse time-fixed rendezvous with path constraints, and a hybrid approach is proposed as a global and efficient optimization tool. A floating-coded genetic algorithm is employed in this hybrid approach to locate an initial reference solution for sequential quadratic programming (SQP) using a simplified analytical propagator. Subsequently, SQP is used to locate the accurate solution using numerical integration of the high-fidelity trajectory dynamic equations. The hybrid approach is evaluated in three test cases: (i) Holman rendezvous and Lambert rendezvous, (ii) a three-impulse homing rendezvous with and without communication window constraints, and (iii) a four- and five-impulse non-coplanar multi-revolution rendezvous. The results show that the hybrid approach is effective and efficient in optimizing the perturbed and constrained fuel-optimal multiple-impulse rendezvous.