Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network
2016; Elsevier BV; Volume: 58; Issue: 6 Linguagem: Inglês
10.1016/j.asr.2016.05.039
ISSN1879-1948
AutoresWanmeng Zhou, Hua Wang, Guo-Jin Tang, Shuai Guo,
Tópico(s)Spacecraft Dynamics and Control
ResumoThe time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg–Marquardt algorithm, and finally determined by the Levenberg–Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.
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