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Delgado, A., Kambhampati, C. and Warwick, K. (1996) Dynamic input/output linearization using recurrent neural networks. In: IEEE International Conference on Neural Networks 1996, 3-6 Jun 1996, Washington DC, USA, pp. 1721-1726.
Abstract/Summary
A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.
| Additional Information | Proceedings ISBN: 0780332105 |
| Item Type | Conference or Workshop Item (Paper) |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/21642 |
| Item Type | Conference or Workshop Item |
| Refereed | Yes |
| Divisions | Science |
| Uncontrolled Keywords | PI controller, asymptotic stability, closed loop, control affine system, dynamic feedback, dynamic input/output linearization, dynamic recurrent neural network, globally linearizing control |
| Additional Information | Proceedings ISBN: 0780332105 |
| Publisher | IEEE |
| Download/View statistics | View download statistics for this item |
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