Aiming at the formation control problem for unmanned surface vehicles under input overload and external disturbance, a leader-follower formation control law based on input saturation and the adaptive super-twisting algorithm was designed in this paper. Firstly, the mathematical model of underactuated unmanned surface vehicle formation based on the leader-follower method is established, and the virtual expected velocity is designed by the back-stepping method to improve the control accuracy of formation. Secondly, the parametric uncertainties and unknown external disturbances in the USV's dynamical model are compensated by the proposed adaptive super-twisting control laws. In addition, the input saturation function is added to the controller to avoid machine necrosis caused by input overload. Finally, Simulation results show that the controller can roughly keep the trajectory of the USV consistent with the expected trajectory and ensure that the input values are within the safe range, which proves the effectiveness of the method in this paper.INDEX TERMS unmanned surface vehicles, multi-agent system, super-twisting algorithm, input saturation.

A model predictive convex programming (MPCP) on SE(3) parametrized by trigonometric series control is proposed in this paper, to solve the optimal control problem of spacecraft attitude orbit integration. Firstly, the geometric modeling of the spacecraft with six degrees of freedom for the attitude orbit integration is performed by the differential manifold SE(3) , which can effectively avoid the problems of ambiguity, receding winding, and singularity that occurs in the conventional methods for rigid body attitude description. Then, based on differential geometric theories, such as the variational principle, the left-invariant principle of Lie group, and the topology of Lie algebraic space, MPCP is applied to SE(3) . It can solve a class of optimization problems with process constraints and control input constraints during spacecraft flight. Furthermore, a control framework of trigonometric series is constructed, which is seamlessly integrated into MPCP to achieve smoother trajectory optimization control. Finally, the practicality and effectiveness of the proposed method are verified by numerical simulation.