In this study, a neuro-fuzzy controller (NFC) was developed for spacecraft attitude control to mitigate large computational load of the state-dependent Riccati equation (SDRE) controller. The NFC was developed by training a neuro-fuzzy network to approximate the SDRE controller. The stability of the NFC was numerically verified using a Lyapunov-based method, and the performance of the controller was analyzed in terms of approximation ability, steady-state error, cost, and execution time. The simulations and test results indicate that the developed NFC efficiently approximates the SDRE controller, with asymptotic stability in a bounded region of angular velocity encompassing the operational range of rapid-attitude maneuvers. In addition, it was shown that an approximated optimal feedback controller can be designed successfully through neuro-fuzzy approximation of the optimal open-loop controller.
Bibliographical noteFunding Information:
This work was supported by the Space Core Technology Development Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning of Republic of Korea ( 2013M1A3A3A02042448 ). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education of Republic of Korea ( 2013R1A1A2013091 ).
All Science Journal Classification (ASJC) codes
- Aerospace Engineering
- Astronomy and Astrophysics
- Atmospheric Science
- Space and Planetary Science
- Earth and Planetary Sciences(all)