LQR based training of adaptive neuro-fuzzy controller

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dc.contributor.authorRashid, Usman
dc.contributor.authorJamil, Mohsin
dc.contributor.authorGilani, Syed Omer
dc.contributor.authorETAL..
dc.date.accessioned2023-12-14T08:57:53Z
dc.date.available2023-12-14T08:57:53Z
dc.date.issued2016
dc.descriptionLinear quadratic regulator (LQR) is a widely used technique in optimal control applications.
dc.description.abstractThe focus of this paper is the design and implementation of adaptive network based fuzzy inference (ANFIS) controller by using the training data obtained from a system controlled by Linear Quadratic Regulator (LQR). This work is motivated by the need to remove stochastic observer required for LQR in noisy environments while at the same time to have optimal performance. This theory is validated by taking the well investigated case of Active Suspension System of a Quarter Vehicle. The performance of the obtained ANFIS controller is tested using (Quanser) Active Suspension Plant. It is observed that the ANFIS controller gives good close loop performance, while removing the requirement of a stochastic observer. Keywords: Active suspension system, Quarter vehicle model, LQR, Fuzzy inference system, Adaptive neuro fuzzy inference system, Hardware-in-loop
dc.identifier.citationRashid, U., Jamil, M., Gilani, S. O., & Niazi, I. K. (2016). LQR based training of adaptive neuro-fuzzy controller. In Advances in Neural Networks: Computational Intelligence for ICT (pp. 311-322). Springer International Publishing.
dc.identifier.doihttps://doi.org/10.1007/978-3-319-33747-0_31
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/240
dc.language.isoen
dc.publisherSpringer Link
dc.titleLQR based training of adaptive neuro-fuzzy controller
dc.typeConference Paper

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