This research work describes the development of a fully actuated 6 Degree of Freedom (DOF) Unmanned Underwater Vehicle (UUV), which can be used for environmental monitoring, three-dimensional (3D) reconstruction applications, as well a research platform. The main vehicle’s characteristics are: it is designed to have easy access to all components, it has eight thrusters in a vectored configuration, it is based on an open source ArduSub firmware, it has a vision system composed of a stereo camera and a powerful computer for image processing. The mechatronics design is presented, where the mechanical, electrical and electronics, and the vision system are described. Furthermore, a general dynamic model for 6 DOF based on Fossen’s methodology is presented. In addition, a reduced 3 DOF mathematical model is derived for control purposes, where the roll, pitch and depth dynamics are considered. To show the performance in trajectory tracking tasks, two classical control strategies are introduced: a Super Twisting Controller and a Robust Proportional Derivative (PD) Controller. Finally, in order to exhibit the satisfactory performance of the developed vehicle, some experiments were conducted with the Super Twisting and Robust PD Controllers, as well as a 3D reconstruction of a plastic cover on the pool wall.

The use of dual quaternions in the dynamic modeling of Unmanned Aerial Vehicles (UAVs) provides an elegant and singularity-free alternative to Euler angle-based representations, thereby eliminating the gimbal lock problem and enabling compact formulations of rigid body motion. Despite their advantages, dual quaternions have been underutilized in the explicit dynamic modeling and control design of multirotor UAVs, particularly in the presence of external disturbances and system uncertainties. To bridge this gap, this paper introduces a novel dual quaternion-based dynamic model that clearly decouples Coriolis, gravitational, control input, and disturbance components. Additionally, a set of mathematical identities analogous to classical matrix-based robotic systems is derived in the dual quaternion framework, which facilitates the Lyapunov-based stability analysis and could enable the future development of new control laws based on dual quaternions. Furthermore, a radial basis function neural network (RBFNN) control strategy is developed to estimate and reject lumped uncertainties in real time. The proposed method is rigorously analyzed using Lyapunov theory, and the boundedness of all closed-loop signals is formally established. Simulation results are presented for three trajectory tracking scenarios and benchmarked against two baseline controllers: a conventional proportional-derivative (PD) controller and a robust controller based on a second-order filter. Quantitatively, the proposed control scheme achieves a reduction of up to 47.5% in the mean squared tracking error under external disturbances, demonstrating its superior performance in uncertain environments. The key contributions of this work include: (i) the development of a structurally rich dual quaternion-based dynamic model for multirotor UAVs; (ii) the derivation of dual quaternion-based dynamic identities inspired by robotic matrix models, which simplify stability proofs and pave the way for future controller designs; (iii) the design of an online RBFNN-based control strategy to adaptively estimate and mitigate dynamic uncertainties; and (iv) the derivation of a formal proof ensuring the uniform ultimate boundedness of both tracking and estimation errors. These contributions represent a significant advancement in the robust and efficient control of UAVs operating in nonlinear and disturbed conditions.

In this work, the analysis of the dynamic general model of an unmanned underwater vehicle (UUV) based on dual quaternions is presented, then the general dynamic model is reduced to a specific vehicle of 4 DoF, this model eliminates the singularities that exist with the representation of the Euler angle and that the model is more compact than others proposed in the literature [1],[2]. To demonstrate the applicability of the model, three controller strategies are proposed for tracking a trajectory, the first controller is a PD + G, under unknown disturbances it produces a considerable tracking error, the second is an adaptive controller that estimates unknown hydrodynamic parameters, and the third is a robust controller for unknown disturbances and parameter uncertainties. The closed-loop system stability analysis for each controller is based on Lyapunov’s theory, a set of numerical simulations is performedto show the behavior of the vehicle with the proposed controllers. The efficiency of the controllers is shown in Table 2 where it is deduced that the adaptive controller has a better performance. The graphics show that the robust controller has little error tracking and the computational cost is lower.