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

A control strategy on a hybrid vehicle can be implemented through different methods. inthis paper, the cerebellar model articulation controller (cmac) and radial basis function (rbf) neuralnetworks 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 themdesirable for online control. the rbf network was constructed with the use of the k-mean clusteringmethod, and the cmac network was investigated for different association factors. results show that thebinary cmac has better performance over the rbf network. also, the hybridization of the vehicle resultsin considerable reduction in fuel consumption.