Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62746
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dc.titleSelf-organizing rule-based control of multivariable nonlinear servomechanisms
dc.contributor.authorNie, J.
dc.contributor.authorLee, T.H.
dc.date.accessioned2014-06-17T06:54:25Z
dc.date.available2014-06-17T06:54:25Z
dc.date.issued1997
dc.identifier.citationNie, J.,Lee, T.H. (1997). Self-organizing rule-based control of multivariable nonlinear servomechanisms. Fuzzy Sets and Systems 91 (3) : 285-304. ScholarBank@NUS Repository.
dc.identifier.issn01650114
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62746
dc.description.abstractBy employing a feedforward and feedback PD control structure, this paper presents a simple approach to the problem of controlling multivariable nonlinear servomechanisms. The feedforward control action is deduced by a rule-based system employing a simplified fuzzy reasoning algorithm. Instead of relying on human experts, the required rule-base is constructed automatically via a self-organizing counterpropagation network in cooperation with an on-line learning mechanism providing the required teacher signals. The convergence property of the learning mechanism is analyzed in some detail. Particular attention is paid to the problem of generalization, that is, the problem of how the learned knowledge can be used to handle novel situations without need for relearning. In the paper, it is suggested that local generalization may be achieved by nonlinear interpolation of fuzzy reasoning algorithm whereas linear generalization can be obtained by the appropriate utilization of the linear factor. A particular system under consideration is a multivariable nonlinear passive line-of-sight (LOS) stabilization system. Simulation results on the LOS system have shown that the proposed control structure yields better performances than PD control alone, the rule-base can be constructed relatively fast in terms of requiring only a few learning cycles, and the suggested schemes for achieving generalization are useful and effective. © 1997 Elsevier Science B.V.
dc.sourceScopus
dc.subjectFuzzy reasoning
dc.subjectLearning
dc.subjectRule-based system
dc.subjectSelf-organizing
dc.subjectServo-control
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleFuzzy Sets and Systems
dc.description.volume91
dc.description.issue3
dc.description.page285-304
dc.description.codenFSSYD
dc.identifier.isiutNOT_IN_WOS
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