Attitude Control of Large Space Structures Using Active Disturbance Rejection Control Tuned by Bayesian Optimization

Large space structures (LSS) should be treated as flexible bodies due to the significant impact of their elastic deformation on attitude maneuvers. However, accurately modeling the entire infinite-dimensional system during ground tests under gravity is inherently challenging. This paper presents a model-independent framework for LSS attitude control that tunes active disturbance rejection control (ADRC) via Bayesian optimization. The tuning parameters are selected automatically by minimizing a cost function that balances tracking error, control effort, and its derivative, thereby eliminating the need for manual trial and error. The proposed approach is validated in simulation on a flexible-structure satellite model with several vibration modes. The Bayesian optimization process converges quickly, yielding a controller that achieves rapid settling without overshoot, smooth slewing, and effective spillover suppression. In comparison, data-driven single- and two-degree-of-freedom PID controllers, obtained through virtual reference feedback tuning (VRFT), achieve nearly the same response but exhibit slight residual oscillations. These results demonstrate that Bayesian-optimized ADRC is a practical and high-performance alternative for the attitude control of LSS.