Indirect adaptive fuzzy control
Indirect adaptive fuzzy control
Fuzzy controllers may be either static systems, which have fixed rule base, or adaptive systems, which have the ability to alter their rules. A discussion of adaptive fuzzy controllers and a comparison with corresponding algebraic techniques concludes that all previous adaptive fuzzy controllers have been of the direct adaptive type. Such controllers use observations of closed loop control performance to manipulate the controller rule base directly without any intermediate process model being produced. In this paper, an indirect adaptive fuzzy controller is proposed where an intermediate process model, identified for observed data, is used to peform on-line controller design. The resulting separation of the adaptation system from controller design enables learning convergence to be investigated. Examples are given of both fuzzy model identification and controller design for linear and nonlinear processes.
441--468
Moore, C.G.
79001bdf-4225-447b-bbe8-cf81c1711906
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1992
Moore, C.G.
79001bdf-4225-447b-bbe8-cf81c1711906
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Moore, C.G. and Harris, C.J.
(1992)
Indirect adaptive fuzzy control.
International Journal of Control, 56 (2), .
Abstract
Fuzzy controllers may be either static systems, which have fixed rule base, or adaptive systems, which have the ability to alter their rules. A discussion of adaptive fuzzy controllers and a comparison with corresponding algebraic techniques concludes that all previous adaptive fuzzy controllers have been of the direct adaptive type. Such controllers use observations of closed loop control performance to manipulate the controller rule base directly without any intermediate process model being produced. In this paper, an indirect adaptive fuzzy controller is proposed where an intermediate process model, identified for observed data, is used to peform on-line controller design. The resulting separation of the adaptation system from controller design enables learning convergence to be investigated. Examples are given of both fuzzy model identification and controller design for linear and nonlinear processes.
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Published date: 1992
Organisations:
Southampton Wireless Group
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Local EPrints ID: 250272
URI: http://eprints.soton.ac.uk/id/eprint/250272
PURE UUID: 0fa30c89-92ff-4dce-ae25-9e3fffb4cc88
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Author:
C.G. Moore
Author:
C.J. Harris
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