This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.

Conventional deadbeat control strategies for permanent magnet synchronous machines (PMSMs) are commonly developed reference frames, however, coupling dynamics affect the performance drive, and rotational transformations are required for the synthesis of the final voltage vector (VV). To improve robustness against parameter variations and to directly synthesize the reference voltage vector, in this paper a deadbeat predictive torque and flux control for a PMSM is presented. The proposed controller is developed in the stationary reference frame ($\alpha -\beta$). First, the reference VV is obtained from a predictive deadbeat controller. Then, the reference VV is applied to the power inverter by the combination of two voltage vectors. A duty cycle optimization is employed to calculate the required time for the application of each voltage vector. Experimental results based on an FPGA and a comparison of the conventional and the proposed deadbeat controller are presented to validate the proposed methodology.