Implementation of an optimal control strategy for a hydraulic hybrid vehicle using CMAC and RBF networks | Zendy

Amir Taghavipour | Zendy; Mahmoud Saadat Foumani | Zendy; Mehrdad Boroushaki | Zendy

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Implementation of an optimal control strategy for a hydraulic hybrid vehicle using CMAC and RBF networks

Author(s) -

Amir Taghavipour,

Mahmoud Saadat Foumani,

Mehrdad Boroushaki

Publication year - 2012

Publication title -

scientia iranica

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.299

H-Index - 51

eISSN - 2345-3605

pISSN - 1026-3098

DOI - 10.1016/j.scient.2012.02.019

Subject(s) - cerebellar model articulation controller , computer science , radial basis function , artificial neural network , controller (irrigation) , reduction (mathematics) , control theory (sociology) , nonlinear system , fuel efficiency , cluster analysis , control (management) , artificial intelligence , control engineering , engineering , automotive engineering , mathematics , physics , geometry , quantum mechanics , agronomy , biology

A control strategy on a hybrid vehicle can be implemented through different methods. In this paper, the Cerebellar Model Articulation Controller (CMAC) and Radial Basis Function (RBF) neural networks were applied to develop an optimal control strategy for a split parallel hydraulic hybrid vehicle. These networks contain a nonlinear mapping, and, also, the fast learning procedure has made them desirable for online control. The RBF network was constructed with the use of the K-mean clustering method, and the CMAC network was investigated for different association factors. Results show that the binary CMAC has better performance over the RBF network. Also, the hybridization of the vehicle results in considerable reduction in fuel consumption

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