Velocity-Free Adaptive Neural-Fuzzy Predefined-Time Attitude Control for Spacecraft

The high-performance control of the spacecraft attitude is significant for successfully executing diverse tasks. To realize this goal, a velocity-free adaptive neural-fuzzy predefined-time attitude controller is presented for the spacecraft with uncertain inertia, exogenous disturbances, and input saturation. First, an improved predefined-time stable system is established, featuring an adjustable convergence time (CT) to enhance the flexibility of the controller design. Utilizing the robust approximation ability of the neural-fuzzy network, a state observer and a nonsingular sliding mode controller are developed to achieve accurate state measurements, improve strong robustness, and eliminate singularity issues. Subsequently, a modified antisaturation method is designed via the Gaussian function and auxiliary compensation system to resolve the input saturation problem. Based on the Lyapunov theorem, the predefined-time stability of the whole system is confirmed. Finally, through comparative simulations and numerical analysis, it can be concluded that: 1) the system state converges within a predefined time related to only a single parameter, and the actual CT is adjustable and 2) compared to existing control schemes, the proposed control scheme demonstrates superior antidisturbance ability, avoids potential singularities, achieves faster convergence, and eliminates input saturation.