Chebyshev Neural Network-Based Discrete-Time Adaptive Speed Control for a Light Weighted All-Electric Vehicle


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Abstract


In this paper, the Chebyshev Neural Network based discrete-time adaptive speed control is investigated for a light weighted all-electric vehicle. The electric vehicle (EV) is driven by DC motor. Firstly, the continuous-time EV system model is transformed by using appropriate state-transformation into the strict feedback form, from which the discrete-time model is derived by the approximation technique. Secondly, the state feedback control is presented via backstepping, applied to a discrete-time EV system. Here, Chebyshev Neural Network (CNN) is used to estimate the unknown nonlinearities and also to avoid the causality contradiction problem which is encountered in the discrete-time backstepping controller design. The unknown nonlinearities arise due to varying mass of passengers. Stability analysis is presented through Lyapunov function, so that system-tracking stability and error convergence can be assured in the closed-loop system. The CNN based control algorithm for the EV system is developed and a new European driving cycle (NEDC) test is performed to test the control performance. Through simulation results the effectiveness of the proposed control schemes are shown
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Keywords


Backstepping Control; Chebyshev Neural Network; Discrete-Time EV System; DC Motor; New European Drive Cycle

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