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

Taghavipour, A ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1016/j.scient.2012.02.019
  3. Publisher: 2012
  4. Abstract:
  5. 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
  6. Keywords:
  7. Hydraulic hybrid vehicle ; Radial Basis Function (RBF) network ; Cerebellar model articulation controller ; CMAC network ; Control strategies ; Fast learning ; Hydraulic hybrid vehicles ; K-mean clustering methods ; Nonlinear mappings ; On-line controls ; Optimal control strategy ; Radial basis function neural networks ; Controllers ; Hybrid vehicles ; Land vehicle propulsion ; Optimal control systems ; Radial basis function networks ; Artificial neural network ; Cluster analysis ; Control system ; Optimization ; Performance assessment
  8. Source: Scientia Iranica ; Volume 19, Issue 2 , 2012 , Pages 327-334 ; 10263098 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S1026309812000466