Application of hybrid recurrent Laguerre-orthogonal-polynomial NN control in V-belt continuously variable transmission system using modified particle swarm optimization

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• 作者：Chih-Hong Lin
• 关键词：V ; belt continuously variable transmission ; Laguerre ; orthogonal ; polynomial neural network ; Lyapunov stability ; Particle swarm optimization
• 刊名：Journal of Mechanical Science and Technology
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：29
• 期：9
• 页码：3933-3952
• 全文大小：9,015 KB
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• 作者单位：Chih-Hong Lin (1)
  
  1. Department of Electrical Engineering, National United University, Miaoli, 360063, Taiwan
  
• 刊物类别：Engineering
• 刊物主题：Mechanical Engineering
  Structural Mechanics
  Control Engineering
  Industrial and Production Engineering
  
• 出版者：The Korean Society of Mechanical Engineers
• ISSN：1976-3824

文摘

Because a V-belt continuously variable transmission system driven by Permanent magnet synchronous motor (PMSM) has many nonlinear and time-varying characteristics, the linear control design with better control performance has to execute a complex and time consuming procedure. To reduce this difficulty and raise robustness of system under the occurrence of the uncertainties, a hybrid recurrent Laguerre-orthogonal-polynomial Neural network (NN) control system which has online learning ability to respond to the system’s nonlinear and time-varying behavior is proposed in this study. This control system consists of an inspector control system, a recurrent Laguerre- orthogonal-polynomial NN control with adaptive law and a recouped control with estimated law. Moreover, the adaptive law of online parameter in the recurrent Laguerre-orthogonal-polynomial NN is derived using Lyapunov stability theorem. Two optimal learning rates of the parameters based on modified Particle swarm optimization (PSO) are proposed to achieve fast convergence. Finally, to verify the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results. Keywords V-belt continuously variable transmission Laguerre-orthogonal-polynomial neural network Lyapunov stability Particle swarm optimization