Neuro-Fuzzy-Based Control for Parallel Cascade Control

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dc.contributor.author Karthikeyan, Rangaswamy
dc.contributor.author Manickavasagam, K.
dc.contributor.author Tripathi, Shikha
dc.contributor.author Murthy, K.V.V.
dc.date.accessioned 2014-03-17T12:39:05Z
dc.date.available 2014-03-17T12:39:05Z
dc.date.issued 2013-06
dc.identifier.citation Murthy, K.V.V. et al., “Neuro-fuzzy-based control for parallel cascade control”, Chemical Product and Process Modeling, DOI: 10.1515/cppm-2013-0002, vol. 8, no. 1, pp. 1-12, Jun. 2013. en_US
dc.identifier.issn 1934-2659
dc.identifier.issn 2194-6159
dc.identifier.uri http://dx.doi.org/10.1515/cppm-2013-0002
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/874
dc.description.abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller. en_US
dc.description.statementofresponsibility by K.V.V. Murthy et al.,
dc.format.extent Vol. 8, No. 1, pp. 1-12
dc.language.iso en en_US
dc.publisher De Gruyter en_US
dc.subject ANFIS control en_US
dc.subject Fuzzy logic control en_US
dc.subject Internal model control en_US
dc.subject Neuro fuzzy control en_US
dc.subject Parallel cascade control en_US
dc.subject PID control en_US
dc.title Neuro-Fuzzy-Based Control for Parallel Cascade Control en_US
dc.type Article en_US
dc.relation.journal Chemical Product and Process Modeling


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