Human Memory/Learning Inspired Approach for Attitude Control of Crew Exploration Vehicles (CEVs)

This paper addresses the problem of attitude control of crew exploration vehicle (CEV). Unlike traditional spacecraft with surface deflections for attitude control, CEV uses RCS jet engines for attitude adjustment, which calls for control algorithms for firing the small propulsion engines mounted in the vehicle. In this work, by combining both actuation and attitude dynamics, we develop a strategy to control the vehicle attitude via adjusting reaction control system (RCS) throttle angles. Since the resultant (combined) dynamics of the vehicle are highly nonlinear and coupled with significant uncertainties, we explore a control approach based on human memory and learning mechanism, which does not reply on precise system information dynamics. Furthermore, the overall control scheme has simple structure and demands much less computation as compared with most existing methods, making it attractive for real-time implementation. The effectiveness of this approach is also verified via simulation.

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