Robust neuro-adaptive attitude control for high-precision Sun-pointing on a halo orbit

Abstract

A novel neuro-adaptive control design approach is presented to maintain a high-precision Sun-pointing attitude of a spacecraft on a Sun–Earth L1 halo orbit. In the presence of parametric inaccuracies and unknown disturbances, the proposed method effectively learns the modeling uncertainty and adapts the control action to achieve its objective. First, large attitude errors are driven towards zero by applying a conventional back-stepping approach that uses the standard quadratic Lyapunov function (QLF). Once the errors are within the required error bounds, a barrier Lyapunov function-based (BLF) approach is excited, ensuring that the attitude errors remain constrained from thereon. Moreover, following the philosophy of meta-cognition, learning is purposefully stopped when the attitude error becomes too small but excited again whenever it grows large. The robustness of the proposed control law is illustrated by carrying out several simulations incorporating modeling uncertainties and disturbance torques, including high-magnitude solar flares. Finally, the effectiveness of the proposed BLF-based adaptive control law is demonstrated by comparing it with a QLF-based adaptive control law following the same philosophy.