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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 (α−β). 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.

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En este trabajo, se presenta el diseño de un control adaptable por modelo de referencia para un vehículo aéreo no tripulado sujeto a variaciones en su carga útil, de modo que la masa del vehículo y sus momentos de inercia se consideran parámetros inciertos en su dinámica. El diseño del controlador considera el análisis del modelo subactuado de la dinámica del vehículo aéreo, el cual puede descomponerse en tres subsistemas independientes. Para cada subsistema se propone un modelo de referencia y utilizando el formalismo de Lyapunov se obtiene la ley de control por realimentación para estabilizar al vehículo junto con una regla de adaptación que ajusta en línea los parámetros inciertos. Esto resulta en un esquema de control tipo backstepping con capacidades de adaptación. El análisis de estabilidad, también garantiza la convergencia de los estados de cada subsistema a los de su correspondiente modelo de referencia. Para evaluar el desempeño del controlador propuesto se realizaron simulaciones numéricas en MATLAB Simulink.