Novel application of continuously variable transmission system using composite recurrent Laguerre orthogonal polynomials modified PSO NN control system

Because the V-belt continuously variable transmission system spurred by permanent magnet (PM) synchronous motor has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the composite recurrent Laguerre orthogonal polynomials modified particle swarm optimization (PSO) neural network (NN) control system which has online learning capability to come back to the nonlinear and time-varying of system, is developed for controlling PM synchronous motor servo-driven V-belt continuously variable transmission system with the lumped nonlinear load disturbances. The composite 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 optimal learning rates of the parameters by means of modified PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.