Intelligent Controller Design for Electric Vehicle(EV) Speed Control  Using An Inverse Model Based Reference Model Learning Algorithm                   

Abstract

The fuzzy rules are knowledge base of a the fuzzy controller which are determined by using the system internal structure and experience information representing the behavior of the system. However, there is a need for automatic determination methods for the extraction of fuzzy rules, since the experience knowledge will be insufficient for systems with nonlinear or variable behavior. In areas where the control parameters vary, an intelligent system can be developed that uses the adaptive fuzzy method to find the coefficients of the controller in the system to overcome this. In this study, an adaptive fuzzy controller is designed using a learning-based fuzzy inverse model to provide speed control of the electric vehicle. The results obtained in the study showed that the designed inverse model learning-based fuzzy controller model is applicable for the dc motor that will provide speed control of electric vehicles. In the simulation of this model, system behavior has been investigated by way of use constant and variable loads. The obtained results remark that the proposed method satisfies system stability in terms of electric vehicle speed control.