Application of V-Belt Continuously Variable Transmission System Using Hybrid Recurrent Laguerre Orthogonal Polynomials Neural Network Control System and Modified Particle Swarm Optimization

作者：Lin Chih Hong^*

来源：Journal of Computational and Nonlinear Dynamics, 2015, 10(5): 051019.

DOI：10.1115/1.4030061

• V-belt continuously variable transmission
• Laguerre orthogonal polynomials neural network
• Lyapunov stability
• particle swarm optimization

Because the V-belt continuously variable transmission (CVT) system spurred by permanent magnet synchronous motor (PMSM) has unknown nonlinear and time-varying properties, the better control performance design for the linear control design is a time consuming procedure. In order to conquer difficulties for design of the linear controllers, the hybrid recurrent Laguerre orthogonal polynomials neural network (NN) control system, which has online learning ability to react to unknown nonlinear and time-varying characteristics, is developed for controlling PMSM servo-driven V-belt CVT system with the lumped nonlinear load disturbances. The hybrid recurrent Laguerre orthogonal polynomials NN control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two varied learning rates of the parameters by means of modified particle swarm optimization (PSO) are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated in order to verify the effectiveness of the proposed control scheme.

• 出版日期2015-9