A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator. Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks (PDF Download Available). Available from: https://www.researchgate.net/publication/228337608_Stabilising_solutions_to_a_class_of_nonlinear_optimal_state_tracking_problems_using_radial_basis_function_networks [accessed Dec 16, 2015].
|Pages (from-to)||369 - 381|
|Journal||International Journal of Applied Mathematics and Computer Science|
|Publication status||Published - Jan 2005|