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Based on the consensus theory, distributed coordinated control for coupled relative orbits and attitudes of a multiple spacecraft system is investigated. With adoption of hyperbolic tangent function, more specifically, a distributed adaptive coordinated control algorithm is proposed for the case that the leader spacecraft state may only be available to only a subset of follower spacecrafts in the presence of input saturation constraints and external disturbances. Firstly, for the case that the leader spacecraft has the time-varying trajectory, three sliding-mode estimators are presented for each follower to obtain the estimates of the leader spacecraft's information. Then, for the case that the relative velocities and relative angular velocities among the follower spacecrafts are not measured, an adaptive law based on Chebyshev neural networks is proposed only using the leader's information. Furthermore, the distributed adaptive coordinated control algorithm with input saturation enables a fleet of followers to track the dynamic leader spacecraft. Simulation results illustrate effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a distributed adaptive cooperative tracking control algorithm 1s investigated for multispacecraft formation flight, in which a directed conununication topology is used to characterize the interaction among spacecrafts considering uncertainties of spacecraft moment of inertia and external disturbances. Specifically, for the error dynamics equations of spacecraft described by the Modified Rodriguez Parameters (MRP), a sliding surface including the relative attitude errors and absolute attitude errors is designed. Then, a nonregressor-based adaptive control approach is presented to deal with the model uncertainties and external disturbance such that all of spacecrafts in the formation converge to the desired attitude cooperatively. Finally, an illustrative example is conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The existing reconstruction algorithms for 2-D signals, most of which convert the original signals into 1-D signals, could be more efficient and accurate. Based on the 2-Dimensional measurement model (2-DMM), a novel recovery algorithm is proposed and then its effectiveness is proved. In 2-DMM, the rows and columns of 2-D signals are measured simultaneously by two independent sensing matrixes. The new algorithm reconstructs the signals as a whole. It relieves the artificial effects and scale expansion caused by the traditional algorithms. Both theoretical analysis and experiment simulation demonstrate that the recovery probability and Reconstruction-SNR performance of the new algorithm are better than those of the existing reconfiguration algorithms under the condition of 2-D measurement. While the operation complexity of the new algorithm is lower than the counterpart of 1-D conversion algorithms. [ABSTRACT FROM AUTHOR]