A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control


Vol. 14, No. 5, pp. 1008-1027, Sep. 2014
10.6113/JPE.2014.14.5.1008


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 Abstract

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor(PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linearcontroller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neuralnetwork (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear loaddisturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servodrive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspectorcontrol, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, isproposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN isderived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning ratesfor the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies arecarried out to show the effectiveness of the proposed control scheme through experimental results.


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Cite this article

[IEEE Style]

C. Lin, "A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control," Journal of Power Electronics, vol. 14, no. 5, pp. 1008-1027, 2014. DOI: 10.6113/JPE.2014.14.5.1008.

[ACM Style]

Chih-Hong Lin. 2014. A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control. Journal of Power Electronics, 14, 5, (2014), 1008-1027. DOI: 10.6113/JPE.2014.14.5.1008.