Table of Contents
International Journal of Vehicular Technology
Volume 2014, Article ID 867209, 14 pages
Research Article

Nonlinear Controllers for a Light-Weighted All-Electric Vehicle Using Chebyshev Neural Network

Department of Electrical Engineering, Motilal Nehru National Institute of Technology, Allahabad 211004, India

Received 24 October 2013; Revised 19 March 2014; Accepted 20 March 2014; Published 22 April 2014

Academic Editor: Tang-Hsien Chang

Copyright © 2014 Vikas Sharma and Shubhi Purwar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Two nonlinear controllers are proposed for a light-weighted all-electric vehicle: Chebyshev neural network based backstepping controller and Chebyshev neural network based optimal adaptive controller. The electric vehicle (EV) is driven by DC motor. Both the controllers use Chebyshev neural network (CNN) to estimate the unknown nonlinearities. The unknown nonlinearities arise as it is not possible to precisely model the dynamics of an EV. Mass of passengers, resistance in the armature winding of the DC motor, aerodynamic drag coefficient and rolling resistance coefficient are assumed to be varying with time. The learning algorithms are derived from Lyapunov stability analysis, so that system-tracking stability and error convergence can be assured in the closed-loop system. The control algorithms for the EV system are developed and a driving cycle test is performed to test the control performance. The effectiveness of the proposed controllers is shown through simulation results.