Neurocontrol: towards an industrial control methodology
Neurocontrol: towards an industrial control methodology
Year 2000 Solutions for Dummies
Year 2000 Solutions for Dummies
Comparison of advanced learning algorithms for short-term load forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
The bio-inspired model based hybrid sliding-mode tracking control for unmanned underwater vehicles
Engineering Applications of Artificial Intelligence
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Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.