To address the problem of multiple underwater unmanned vehicle (UUV) formation tracking under bounded communication delay, considering the existence of model parameter perturbations of UUVs as well as external ocean current disturbances, a distributed robust model predictive controller (DRMPC) with a dual closed-loop control structure and self-triggering mechanism is proposed. Among them, the outer-loop controller transforms the target trajectory into the target speed required by the inner-loop controller, and the inner-loop controller solves the effects of model parameter perturbations and bounded disturbances by RMPC and a finite-time extended state observer (FESO). To reduce the communication load of the system, a self-triggering mechanism is designed to determine the next triggering moment while solving the outer-loop optimization problem. The feasibility and stability of this controller are analyzed via mathematical induction and the ISpS Lyapunov theory. Finally, the effectiveness of this formation controller is validated through simulation experiments with multi-UUV.

This paper proposes a Unified Monocular-Stereo Visual-Inertial State Estimator (UMS-VINS) that combines monocular, stereo vision, and inertial measurements for vehicle localization in degenerated scenarios. UMS-VINS is a tightly coupled visual-inertial odometry (VIO), which requires a stereo camera and a low-cost inertial measurement unit (IMU). On the one hand, we introduce additional two-dimensional sub-pixel features from the left and/or right cameras. With monocular-stereo features, UMS-VINS can improve the positioning accuracy and robustness by enhancing the quality and quantity of features. On the other hand, a mode selection-based visual-inertial initialization strategy is designed to dynamically choose between stereo visual odometry or VIO according to the inertial motion state and initialization status, which can guarantee successful initialization. The performance on both new real-world and public datasets demonstrates its effectiveness in terms of localization accuracy, localization robustness, and environmental adaptability.