Adaptive Neurofuzzy Control for A Class of State Dependent Nonlinear Processes
Adaptive Neurofuzzy Control for A Class of State Dependent Nonlinear Processes
This paper presents a neurofuzzy based scheme for modeling and control of a class of nonlinear systems with an ARMA like model (a generalised Takagi-Sugeno fuzzy model), whose parameters are unknown nonlinear functions of the input and output variables or states of the plant. An associative memory network is used to identify each nonlinear function. The controller is a feedback linearising control law which can decouple the nonlinearity of the system. For the cases of adaptive and the fixed model parameters, detailed closed-loop stability analysis is carried out. It is shown that the consequent closed-loop system is globally stable. The main assumptions placed on the system and model for stability are minimum phase and a limit on the modeling mismatch error or uncertainty. Simulation examples are given to illustrate the efficacy of the proposed approach.
759-771
Feng, M.
6baf8979-9e8e-482e-9686-6e5e7cb2125a
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1998
Feng, M.
6baf8979-9e8e-482e-9686-6e5e7cb2125a
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Feng, M. and Harris, C.J.
(1998)
Adaptive Neurofuzzy Control for A Class of State Dependent Nonlinear Processes.
International Journal of System Science, 29 (7), .
Abstract
This paper presents a neurofuzzy based scheme for modeling and control of a class of nonlinear systems with an ARMA like model (a generalised Takagi-Sugeno fuzzy model), whose parameters are unknown nonlinear functions of the input and output variables or states of the plant. An associative memory network is used to identify each nonlinear function. The controller is a feedback linearising control law which can decouple the nonlinearity of the system. For the cases of adaptive and the fixed model parameters, detailed closed-loop stability analysis is carried out. It is shown that the consequent closed-loop system is globally stable. The main assumptions placed on the system and model for stability are minimum phase and a limit on the modeling mismatch error or uncertainty. Simulation examples are given to illustrate the efficacy of the proposed approach.
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Published date: 1998
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250007
URI: http://eprints.soton.ac.uk/id/eprint/250007
PURE UUID: fdd78491-e986-4227-a536-50acd44159d7
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Date deposited: 30 Mar 2000
Last modified: 08 Jan 2022 11:43
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Author:
M. Feng
Author:
C.J. Harris
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