Stable Nonlinear Receding Horizon Regulator Using RBF Neural Network ModelsAhmida, Z., Charef, A. and Becerra, V. (2006) Stable Nonlinear Receding Horizon Regulator Using RBF Neural Network Models. In: 2006 14th Mediterranean Conference on Control and Automation. IEEE, pp. 1-5. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1109/MED.2006.328824 Abstract/SummaryThe general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.
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