Abstract
On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.
Highlights
The on-orbit service of a spacecraft is a type of space operation that humans, robots, or both extend lives and improve their abilities in performing tasks of various spacecraft [1,2,3]
Part D (Section 5): the feasibility of all optimization models in the present study is verified through comparison with other intelligent algorithms, and the fuzzy adaptive particle swarm optimization (FAPSO) algorithm is more efficient in solving the deployment problem of orbit service vehicle (OSV)
After the convergence results are entered into the objection function of Formula (9), the weight of the remaining fuel obtained through the FAPSO algorithm is 23 kg, 22 kg, 26 kg, and 12 kg more than that obtained by the particle swarm optimization (PSO) algorithm, PSO+TS algorithm, genetic algorithm (GA), and GA+SA algorithm, respectively
Summary
The on-orbit service of a spacecraft is a type of space operation that humans, robots, or both extend lives and improve their abilities in performing tasks of various spacecraft [1,2,3]. Part B (Section 3): at the time t of an on-orbit serving mission order set and taking the maximum value of both the count of satellites that can be served and the minimum value of a single OSV’s optimization index A among all OSVs as the optimization index (optimization index B), an assignment optimization model of multiple OSVs serving multiple satellites is established based on DPSO algorithm. Given the count of OSVs and taking the maximum value of optimization index C as the optimization index (optimization index D), the deployment optimization model of multiple OSVs serving multiple satellites is established based on the fuzzy adaptive particle swarm optimization (FAPSO) algorithm. Part D (Section 5): the feasibility of all optimization models in the present study is verified through comparison with other intelligent algorithms, and the FAPSO algorithm is more efficient in solving the deployment problem of OSVs
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