Universal approximation using radial-basis-function networks
Neural Computation
Neural net based MRAC for a class of nonlinear plants
Neural Networks
Nonlinear adaptive control using neural networks and multiple models
Automatica (Journal of IFAC)
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Adaptive multilayer perceptrons with long- and short-term memories
IEEE Transactions on Neural Networks
A GUI simulation system for integrating photovoltaic and wind units into power grids
Proceedings of the 2010 Conference on Grand Challenges in Modeling & Simulation
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In this paper a new approach to a neural network based intelligent adaptive controller, which consists of an online growing dynamic Radial Basis Function Neural Network (RBFNN) structure along with a Model Reference Adaptive Control (MRAC), is proposed. RBFNN control is used to approximate the nonlinear function and the MRAC control adapts when plant parametric set changes. The adaptive laws, including neural network approximation error, are derived based on a Lyapunov function. The update details of the RBFNN width, centers, and weights are derived in order to ensure the error reduction and for improved tracking accuracy. Main advantage and uniqueness of the proposed scheme is the controller's ability to complement each other in case of parametric and functional uncertainty. Moreover, the online neural network produces a plant functional approximation control with growing and pruning nodes. The theoretical results are validated by conducting simulation studies on a single machine infinite bus (SMIB) system for electric generator control.