Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization

The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The 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 altered 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.