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Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown nonlinear processes

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown nonlinear processes
Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown nonlinear processes
A new controller scheme is introduced for unknown nonlinear dynamical processes that are modelled by an operating point neurofuzzy system. A neurofuzzy state space model of controllable form is initially constructed, based on a neurofuzzy design and model construction algorithm for nonlinear dynamical process, followed by a controller design based on closed loop pole assignment that ensures the system stability under some mild assumptions. This intelligent controller approach also includes a generalised Kalman filtering algorithm with coloured noise, based on the neurofuzzy state space model, to obtain an optimal state vector estimation for use in the state feedback stabilisation. The application of the algorithm to typical output tracking problem is used to demonstrate the applications of this new approach to intelligent control.
1474-6670
443-448
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03

Harris, C.J. and Hong, X. (2001) Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown nonlinear processes. IFAC Proceedings Volumes, 34 (14), 443-448. (doi:10.1016/S1474-6670(17)41662-4).

Record type: Article

Abstract

A new controller scheme is introduced for unknown nonlinear dynamical processes that are modelled by an operating point neurofuzzy system. A neurofuzzy state space model of controllable form is initially constructed, based on a neurofuzzy design and model construction algorithm for nonlinear dynamical process, followed by a controller design based on closed loop pole assignment that ensures the system stability under some mild assumptions. This intelligent controller approach also includes a generalised Kalman filtering algorithm with coloured noise, based on the neurofuzzy state space model, to obtain an optimal state vector estimation for use in the state feedback stabilisation. The application of the algorithm to typical output tracking problem is used to demonstrate the applications of this new approach to intelligent control.

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More information

Published date: August 2001
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 255952
URI: http://eprints.soton.ac.uk/id/eprint/255952
ISSN: 1474-6670
PURE UUID: c5f46c8c-e486-447b-b021-87111facdc29

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Date deposited: 06 Nov 2001
Last modified: 24 Mar 2020 17:46

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