Abstract
Focused on far-distance rapid cooperative rendezvous between two spacecraft under continuous large thrust, this paper presents a series of artificial intelligence algorithms for fuel and time optimization. The process of far-distance rapid cooperative rendezvous was optimized by a type of hybrid algorithm-integrated Quantum-behaved Particle Swarm Optimization (QPSO) and Sequential Quadratic Programming (SQP). The convergent co-state vectors were obtained by QPSO and subsequently set as the initial values of SQP to search for the exact solutions in a smaller area. Applications of non-coplanar cooperative rendezvous are provided to demonstrate that the QPSO-SQP algorithm has better performance than other popular algorithms in less time consumption, faster convergence rate and highly stable solutions.
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