Lists
Lists
Delgado, A., Kambhampati, C. and Warwick, K. (1996) Stable linearization using multilayer neural networks. In: UKACC International Conference on Control, 2-5 Sep 1996, Exeter, UK, pp. 194-198.
Abstract/Summary
The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedback coefficients that linearize a nonlinear control affine plant. The stability of the linearizing closed loop can be guaranteed if the autonomous plant is asymptotically stable and the state feedback is bounded.
| Item Type | Conference or Workshop Item (Paper) |
| URI | https://reading-clone.eprints-hosting.org/id/eprint/21651 |
| Item Type | Conference or Workshop Item |
| Refereed | Yes |
| Divisions | Science |
| Uncontrolled Keywords | asymptotic stability, autonomous plant, exact knowledge, linearization theory, linearizing closed loop, multilayer network, multilayer neural networks, nonlinear control affine plant, practical problems, stable linearization, state feedback, state feedback coefficients |
| Download/View statistics | View download statistics for this item |
University Staff: Request a correction | Centaur Editors: Update this record
