Abstract
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.
Highlights
In recent years, the use of underwater robotics as a technological tool for solving a wide variety of problems in underwater environments has experienced a great increase
Regarding the sigmoidal activation function parameters bi, ci, and di, they can be selected by trial and error to enhance the identifier response; for simplicity in the simulation experiments, we considered some baseline values suggested by related works [39,54] that implements the kind of dynamic neural network used in the proposed Dynamic Neural Control System (DNCS)
We present a comparison between the DNCS, a standard feedback controller designed by using Linear Quadratic Regulator (LQR) [55], and a Proportional Derivative (PD) controller implementing an online auto tuning method to set its parameters by using the successive approximation method (SAM) [56]
Summary
The use of underwater robotics as a technological tool for solving a wide variety of problems in underwater environments has experienced a great increase. In [30], an adaptive tracking controller based on a combination of backstepping and terminal sliding mode techniques for a tethered AUV is designed This type of vehicle suffers from unknown underwater disturbances as well as time-variant nonlinear cable dynamics. The inputaffine AUV model considered in this work resulted in unknown input gains dependent on constant added mass coefficients This fact does not represent a limitation for the neural network adaptive controller proposed in the present work, but rather, it contemplates a reasonable consideration of real conditions when these parameter uncertainties (added mass-dependent gains) are present in the system structure.
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