K. J. C. Kumara, M.H.M.R.S. Dilhani
Abstract
Radial Basis Function neural networks (RBF-NN) are popular among other NNs as they are simple in structure and fast learning and universal approximation properties and even their applicability in developing deep learning applications. Its basic form with center states and standard deviations with weight adaptation makes limited variability and complex in tuning when such embedded to the model. Dynamics systems are nonlinear especially behavior is uncertain and unpredictable and complete mathematical modeling or model-based controlling have limited applicability for stability and accurate control. Shape adaptive RBF-NN presented in the paper theoretically proved for stability control using the Lyapunov analysis. The autonomous surface vessel controlling selected for the numerical simulation consists of a mathematical model developed using marine hydrodynamics for a prototype vessel and classical proportional-derivative (PD) controller. Results indicated that shape adaptive RBF-NN blended controlling is more accurate and has a fast learning ability in intelligent transportation vessel development.
Keywords: Autonomous surface vessel, Nonlinear Control, Radial Basis Function NN
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