Neural and adaptive controllers for a non-minimum phase varying time-delay system

Abstract : In this paper we study a non-minimum phase discrete time system with varying time-delay. We first propose several open loop control architectures based on non-linear neural networks and study their ability to handle the different difficulties of the control problem. All the methods are tested and compared to a baseline linear controller, on a simulated river system. This plant is submitted to perturbations corresponding to water withdrawals and lateral inflows. The above architectures are not able to cope with such perturbations. We then propose a model combining a feed-forward neural network based learning controller and a feedback adaptive controller. The performances of this model are compared to a similar architecture containing linear feed-forward and feedback controllers.
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Submitted on : Tuesday, August 18, 2015 - 11:40:13 AM
Last modification on : Tuesday, May 14, 2019 - 10:32:34 AM

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Abdelmoumène Toudeft, Patrick Gallinari. Neural and adaptive controllers for a non-minimum phase varying time-delay system. Artificial Intelligence in Engineering , Elsevier, 1997, 11 (4), pp.431-439. ⟨10.1016/S0954-1810(97)00005-8⟩. ⟨hal-01184890⟩

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