The Air Force Research Laboratory has identified multiple spacecraft formation flying as an enabling technology for several future space missions. A key benefit of formation flying is the ability to reconfigure the spacecraft formation to achieve different mission objectives. In this paper, generation of fuel optimal manoeuvres for spacecraft formation reconfiguration is modelled and analysed as a multi-agent optimal control problem. Multi-agent optimal control is quite different from the traditional optimal control for single agent. Specifically, in addition to fuel optimization for a single agent, multi-agent optimal control necessitates consideration of task assignment among agents for terminal targets in the optimization process. In this paper, we develop an efficient hybrid optimization algorithm to address such a problem. The proposed multi-agent optimal control methodology uses calculus of variation, task assignment, and parameter optimization at different stages of the optimization process. This optimization algorithm employs a distributed computational architecture. In addition, the task assignment algorithm, which guarantees the global optimal assignment of agents, is constructed using the celebrated principle of optimality from dynamic programming. A communication protocol is developed to facilitate decentralized decision making among agents. Simulation results are included to illustrate the efficacy of the proposed multi-agent optimal control algorithm for fuel optimal spacecraft formation reconfiguration.
|Translated title of the contribution||Fuel optimal manoeuvres for multiple spacecraft formation reconfiguration using multi-agent optimization|
|Pages (from-to)||243 - 283|
|Number of pages||40|
|Journal||International Journal of Robust and Nonlinear Control|
|Publication status||Published - Mar 2002|
Bibliographical notePublisher: Wiley
Yang, G., Yang, Q., Kapila, V., Palmer, DW., & Vaidyanathan, R. (2002). Fuel optimal manoeuvres for multiple spacecraft formation reconfiguration using multi-agent optimization. International Journal of Robust and Nonlinear Control, 12 (2-3), 243 - 283. https://doi.org/10.1002/rnc.684