An Adaptive Hyperparameter Strategy Optimization Method for Spacecraft Rendezvous and Orbital Transfer
Based on reinforcement learning(RL),an optimization method of rendezvous and orbit change strategy for fuel optimal geosynchronous orbit(GEO)spacecrafts with hyperparameter adaptation is proposed.Firstly,a GEO spacecraft rendezvous Lambert trajectory model is established.Taking the trajectory time as the decision variable and fuel consumption as the fitness function,an improved comprehensive learning particle swarm algorithm(ICLPSO)is used as the basic method for trajectory strategy optimization.Secondly,considering the optimality and rapidity of the solution,a reward function is redesigned with the particle swarm algorithm(PSO)optimization result as the reference baseline.A deep deterministic policy gradient neural network(DDPG)is trained using a typical family of GEO spacecraft rendezvous conditions.DDPG is combined with ICLPSO to form a reinforcement learning particle swarm algorithm(RLPSO),which realizes the adaptive dynamic adjustment of algorithm hyperparameters according to the real-time iterative convergence situation.Finally,simulation results show that compared with PSO and comprehensive learning particle swarm algorithm(CLPSO),RLPSO can give planning results with higher fitness after fewer iterations,reducing computational resource consumption during the iteration process.
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