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Journal of Mechanical Science and Technology

, Volume 32, Issue 9, pp 4399–4412 | Cite as

Novel mingled reformed recurrent hermite polynomial neural network control system applied in continuously variable transmission system

  • Jung-Chu Ting
  • Der-Fa Chen
Article
  • 5 Downloads

Abstract

Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is spurred by the synchronous reluctance motor (SynRM). The better control performance obtained by use of the proposed novel mingled reformed recurrent Hermite polynomial neural network (MRRHPNN) control system can be presented dynamic behavior for the nonlinear uncertainties of CVT system. The novel MRRHPNN control system can carry out overlooker control system, reformed recurrent Hermite polynomial neural network control (RRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the RRHPNN and the appraised law of the reimbursed control are established. Furthermore, two varied learning rates of two weights for the RRHPNN according to increment-type Lyapunov function are derived to help improving convergence. At last the obtained better control performances by use of the proposed control method are verified through the illustrated results by the comparative experimentations.

Keywords

Synchronous reluctance motor Recurrent Hermite polynomial neural network Lyapunov stability Continuously variable transmission 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial Education and TechnologyNational Changhua University of EducationChanghuaTaiwan

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