Adaptive recurrent Chebyshev neural network control for permanent magnet synchronous motor servo-drive electric scooter. (October 2014)

• Record Type: Journal Article
• Authors: Lin, Chih-Hong
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 9(2014)
• Page Start: 699
• Page End: 714
• Publication Date: 2014-10
• Subjects: Permanent magnet synchronous motor -- Chebyshev neural network -- continuously variable transmission -- Lyapunov stability; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651814539475
• Languages: English
• ISSNs: 0959-6518

Abstract

Because of unknown nonlinearity and time-varying characteristic of electric scooter with V-belt continuously variable transmission system driven by using permanent magnet synchronous motor, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, an adaptive recurrent Chebyshev neural network control system is proposed to control for permanent magnet synchronous motor servo-drive electric scooter with V-belt continuously variable transmission under lumped nonlinear external disturbances in this study. The adaptive recurrent Chebyshev neural network control system consists of a recurrent Chebyshev neural network control and a compensated control with estimation law. In addition, the online parameter tuning methodology of the recurrent Chebyshev neural network and the estimation law of the compensated controller can be derived by using the Lyapunov stability theorem. Moreover, the two optimal learning rates of the recurrent Chebyshev neural network based on the discrete-type Lyapunov function are proposed to guarantee the convergence of tracking error. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.