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.

Autonomous agricultural robots require accurate trajectory tracking to perform precision field operations such as seeding, fertilization, and pruning. Classical kinematic models fail to capture the nonlinear dynamics inherent to real field conditions, limiting the performance of model-based control strategies. This work proposes an optimal hybrid methodology integrating Takagi–Sugeno (T–S) fuzzy model identification and Pure Pursuit (PP) control within a Particle Swarm Optimization (PSO) framework for a simulated pruning tractor. Data-driven T–S fuzzy models for incremental displacements ${\mathcal{M}}_{\Delta x}$ and ${\mathcal{M}}_{\Delta y}$ are identified using Fuzzy C-Means and parameterized via PSO. These fuzzy models are embedded in a PP feedback control scheme with discrete-time PI velocity and PD steering controllers, whose four gains are tuned by a second PSO instance. The fuzzy models achieve identification Root-Mean-Square Errors (RMSEs) of 10.598 × 10⁻³ m and 8.125 × 10⁻³ m. Integrated into the control loop, the system yields a lateral RMSE of 6.6 × 10⁻³ m on the training path and generalizes effectively across twelve complex agricultural coverage trajectories, maintaining a lateral RMSE below 12 × 10⁻³ m and heading RMSE under 1 degree. This interpretable, fuzzy rule-based approach provides an accurate and replicable simulation baseline for future experimental implementation on physical platforms.

A novel structure for a Robust Dynamic Neural Network Identifier (RDNNI) based on sliding modes is proposed for the online identification of nonlinear second-order systems. A stability analysis using Lyapunov theory demonstrates that both the neural weight estimation errors and the sliding surface converge to zero in finite. Most of the identification schemes based on dynamic neural networks reported in the literature only assure the asymptotic convergence of the synaptic weights and the identification process. There exist some works which modifies the identifier structure to guarantee the finite time convergence of the identification process. However, all these schemes have the limitation that identification errors converge to a region around the origin because of the upper boundaries introduced by the system disturbances. Compared these types of dynamic neural network identifiers found in the literature, the proposed identifier combines the non-singular terminal sliding mode surface with the synaptic weights’ adaptation laws to guarantee that the identification errors converge to zero in a finite time. To evaluate the performance of the developed structure, a simulation is conducted for the identification of a Duffing oscillator, together with a series of experimental tests to identify the dynamics of depth and yaw in an unmanned underwater vehicle. Finally, a quantitative comparison of the identification performance with a recently introduced State-Input Affine Differential Neural Network (SIADNN) identifier demonstrates the superior performance of the proposed design, both in simulations and in real-time experiments.