Genetic algorithms for local controller network construction
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | McLoone, SF | |
dc.contributor.author | Irwin, GW | |
dc.date.accessioned | 2017-02-15T16:36:53Z | |
dc.date.available | 2017-02-15T16:36:53Z | |
dc.date.issued | 2005-01-01 | |
dc.identifier.issn | 1350-2379 | |
dc.identifier.issn | 1359-7035 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/8499 | |
dc.description.abstract |
Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues. © IEE, 2005. | |
dc.format.extent | 587-597 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Institution of Engineering and Technology (IET) | |
dc.title | Genetic algorithms for local controller network construction | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000232090000013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 5 | |
plymouth.volume | 152 | |
plymouth.publication-status | Published | |
plymouth.journal | Iee Proceedings-Control Theory and Applications | |
dc.identifier.doi | 10.1049/ip-cta:20045110 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.identifier.eissn | 1359-7035 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1049/ip-cta:20045110 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Journal Article/Review |