Integrated control and allocation method for fully distributed robust cooperative attitude control

To address challenges in takeover control spacecraft systems involving a large number of cellsats, module heterogeneity, local communication constraints, communication delays, and uncertainties and external disturbances, a fully distributed control method with integrated control and allocation is proposed. First, the attitude tracking error kinematics and dynamics model of the combined spacecraft is transformed into a State Dependent Coefficients (SDC) model. Then, based on the system model with an augmented input matrix, the traditional Tube Model Predictive Control (TMPC) framework is modified according to the characteristics of the control allocation problem. By combining the Delay-Tolerant Augmented Consensus Tracking Alternating Direction Method of Multipliers (DTAC-ADMM) distributed optimization algorithm under communication delays, the cooperative attitude control problem is transformed into a multi-decision-variable optimization problem with coupled constraints. The proposed framework explicitly considers actuator constraints, dynamic constraints of the attitude tracking error under prescribed performance conditions, as well as communication delays and packet losses among cellsats modules. Consequently, each module can independently compute its required control torque using only information from its neighboring modules. Finally, simulations are conducted to verify the correctness and effectiveness of the proposed control scheme, demonstrating its suitability for co-operative control in heterogeneous satellite clusters with only local communication capabilities. Compared with the traditional two-layer “control + allocation” framework, the proposed single-layer framework eliminates the need for a centralized controller for computation and allocation, and can accommodate the online addition or removal of cellsats during control. It fully exploits the control capability of each module, avoids actuator saturation, resolves the strong coupling difficulties of traditional TMPC in control allocation, achieves global optimization of total control torque energy consumption, and exhibits strong disturbance rejection capability. Moreover, the distributed optimization remains applicable even in the presence of communication delays and packet losses. The proposed method achieves fully distributed, simplifies controller parameter tuning, provides strong robustness, and is suitable for practical engineering applications.