A vehicle able to navigate both in the air and underwater is referred to an unmanned aerial-underwater vehicle (UAUV). In this paper, the attitude and altitude control of UAUV with a quadrotor configuration during underwater navigation is studied theoretically, and an incremental dynamic inverse control (INDI) method with second-order low-pass filters is employed in the control system. Based on the underwater motion model of an UAUV and the incremental attitude and altitude control model, the relationship between thrust increment and vertical and angular acceleration increment is obtained through Taylor series expansion and angular acceleration feedback. Subsequently, attitude and altitude controllers based on nonlinear dynamic inversion (NDI) and INDI are designed and their robustness is analyzed. By introducing second-order low-pass filters at the part of control and feedback, the accuracy of the angular acceleration estimation is improved and the time synchronization of the control system is ensured. Simulation results demonstrated that when introducing external disturbances and uncertainties, the INDI control with second-order low-pass filters proposed in this paper can track the reference signal faster and more accurately than the NDI control.

Autonomous maneuvering decisions of unmanned aerial vehicle (UAV) in short-range air combat remain a challenging research topic, and a decision method based on an improved deep deterministic policy gradient (DDPG) is proposed. First, the problem model is improved from the perspective of energy–air combat, and a decision model with engine thrust, angle of attack, and roll angle as control variables is established. The normal and tangential overloads are determined by these control variables, and the decision is constrained by the flight stability and threshold range. Subsequently, the decision learning algorithm of the maneuver command is designed based on the DDPG framework. According to the energy air combat, speed is introduced into the return function in some states to make the return value more in line with reality. In view of the slow learning speed of the DDPG algorithm, the winning rate is introduced into the $\varepsilon $ -greedy strategy to adjust the exploration and application probabilities in real time. In view of the decrease in computational efficiency caused by the large amount of empirical data, a similar empirical exclusion was carried out based on the vector distance. The simulation results show that the DDPG-based algorithm realizes autonomous decisions of engine thrust, roll angle, and attack angle under constraints, and the comparative simulation shows that the improvement measures are effective.

Hybrid aerial underwater vehicles (HAUV) are newly borne vehicle concepts, which could fly in the air, navigate underwater, and cross the air-water surface repeatedly. Although there are many problems to be solved, the advanced concept, which combines the integrated multidomain locomotion of both water and air mediums is worth exploring. This paper presents the water–air trans-media status of the HAUV from the perspective of the configuration and trans-media control. It shows that the multi-rotor HAUV is relatively mature and has achieved a stable water–air trans-media process repeatedly. The morphing HAUV is still in its exploration stage, and has achieved partial success.

This paper presents a robust position tracking control scheme for underwater vehicles moving in a vertical plane. The idea comes from the demand of underwater position tracking control for the newly borne Trans-media Aerial Underwater Vehicle (TMAUV). Although position control of a TMAUV is still within the scope of autonomous underwater vehicles (AUVs) control, it has new features. An underwater reference path for the TMAUV could be characterized by a strong maneuver that many assumptions in the conventional AUV controller design could not be employed. In this paper, a Lyapunov-based backstepping controller is developed for a nonlinear coupled input system releasing all constraints on the pitch angle, heave velocity, and angular velocity. Furthermore, neural networks and parameter estimation are employed to develop a robust controller in the presence of model uncertainties, parameter uncertainties, and external disturbances. This paper also solves the problem of adaptive estimation for the system parameters under the coupled input condition. Simulations are presented to demonstrate the feasibility and effectiveness.

The classical ’Geometric Control (GC)’ of the quadrotors is susceptible to the 180° turnover singularity, which could result in a crash. To overcome the problem, an ’Improved Geometric Control (IGC)’ is developed based on a proposed attitude extraction method. The controller is cascaded with a position loop and an attitude loop. The two loops are bridged by the method. It geometrically determines the desired attitude from the desired thrust direction. Attitude tilt-prioritizing can also be achieved by scarifying the yaw control. Unlike the GC with almost global stability, the IGC is proved to be globally asymptotically stable. Simulation comparisons demonstrate excellent performance even when the quadrotor confronts large initial attitude and position errors. The singular problem is thoroughly avoided.