Compensatory neural fuzzy control for two wheels electric vehicle drive Online publication date: Mon, 10-Aug-2015
by Karima Houacine; Rabah Mellah; Said Guermah
International Journal of Electric and Hybrid Vehicles (IJEHV), Vol. 7, No. 2, 2015
Abstract: This paper presents a novel speed control design of electric vehicles (EV) to improve behaviour and stability under different road constraints conditions. The proposed control is intended to increase the efficiency of a circuit using adaptive fuzzy reasoning method with compensatory fuzzy operators. The compensatory neural fuzzy (CNF) networks are made of both control-oriented fuzzy neurons and decision-oriented fuzzy neurons. The CNF networks are not only adaptively adjust fuzzy membership functions but also dynamically optimise the adaptive fuzzy reasoning by using a compensatory learning algorithm. The proposed traction system consists of two induction motors (IMs) that ensure the drive of the two rear wheels. The controller is designed based on a control structure that realises an independent speed control. Simulation results show that the CNF control method reduces the transient oscillations and ensures efficient behaviour in all types of road constraints.
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