Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

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.

A New Spike Membership Function for the Recognition and Processing of Spatiotemporal Spike Patterns: Syllable-Based Speech Recognition Application

“…Figure 10a shows the normalized magnitude of the melody with musical notes of solfeggio; Figure 10b shows the square pulse train proportional in frequency to the amplitude of the normalized magnitude of the melody; Figure 10c shows the train of ∼ y sawtooth (49) after signal processing with STFT of the normalized magnitude of the melody; Figure 10d shows the train of spikes based on the Augmented FAN-STEPAF-SPKAF model (55); and Figure 10e shows the recognized pattern of the syllable SI with (56)-(61).…”

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

“…The solution to this problem is the application of adaptive methods that estimate the unknown, uncertain, and nonlinear parameters online while the robot is moving. In this case, we are dealing with the whole range of adaptive strategies, such as: Support Vector Regression (SVR) 19 , Extended Kalman Filter 20 , 21 , neural networks 22 – 26 , neuro-fuzzy systems 27 and fuzzy inference systems 28 .…”

A LSSVR Interactive Network for AUV Motion Control

“…Simulation experiments proved the controller to be effective in engineering practice. Munoz Filiberto et al [31] focused on a method that improved the accuracy of four-degree-of-freedom AUV trajectory tracking operations with considerations of external interference. A dynamic neural network control system was designed including an adaptive neural network controller based on non-parameter identification and additional mass parameter estimation.…”