Stable linearization using multilayer neural networks

Delgado, A.; Kambhampati, Chandrasekhar; Warwick, K.

Abstract

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.

Citation

Delgado, A., Kambhampati, C., & Warwick, K. (1996). Stable linearization using multilayer neural networks. . https://doi.org/10.1049/cp%3A19960551

• Conference Name: UKACC International Conference on Control. Control '96
• Conference Location: Exeter, UK
• Start Date: Sep 2, 1996
• End Date: Sep 5, 1996
• Acceptance Date: Dec 31, 1996
• Online Publication Date: Aug 6, 2002
• Publication Date: 1996
• Journal: IEE Conference Publication
• Print ISSN: 0537-9989
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 427
• Issue: 1
• Pages: 194 - 198
• ISBN: 0852966660
• DOI: https://doi.org/10.1049/cp%3A19960551
• Keywords: Asymptotic stability; Multilayer perceptrons; Feedforward neural nets; Neurocontrollers; State feedback; Linearisation techniques; Closed loop systems; Nonlinear control systems
• Public URL: https://hull-repository.worktribe.com/output/409689