Unmanned Aerial Underwater Vehicles (UAUVs) with multiple propellers can operate in two distinct mediums, air and underwater, and the system modeling of the autonomous vehicles is a key issue to adapt to these different external environments. In this paper, only a single set of aerial rotors with switching propulsion abilities are designed as driving components, and then a compound multi-model method is investigated to achieve good performance of the cross-medium motion. Furthermore, some additional variables, such as water resistance, buoyancy and their corresponding moments are considered for the underwater case. In particular, a critical coefficient for air-to-water switching is presented to express these gradually changing additional variables in the cross-medium motion process. Finally, the sliding mode control method is used to reduce the altitude error and attitude error of the vehicles with external environmental disturbances. The proposed scheme is tested and the model is verified on the simulation platform.

In this paper, a switching dynamic event-triggered prescribed performance formation control (SDPPC) algorithm is proposed for underactuated autonomous surface vehicles (ASVs). The control objective is to achieve high-precision formation control tasks for the ASVs while ensuring transient performance, connectivity, collision avoidance, and communication resource conservation. Collision avoidance and connectivity maintenance are achieved by defining novel predefined performance functions. The radial basis function neural network (RBF-NN) is utilized to approximate the uncertainty of the unknown model dynamics in the form of a minimum learning parameter (MLP). The ‘algebraic loop’ issue frequently encountered in the back-stepping method is resolved by the inherent properties of the radial basis function. A novel switching dynamic event-triggered mechanism (SDETM) is developed, which can save the communication resources in case of oscillations and satisfy the high-precision formation control task. By utilizing Barbalat's Lemma and the Lyapunov function method, it has been proven that the formation tracking errors can converge to zero as time tends towards infinity. Finally, the feasibility and effectiveness of the SDPPC algorithm are demonstrated through numerical simulations.