Design of a composite recurrent Laguerre orthogonal polynomial neural network control system with ameliorated particle swarm optimization for a continuously variable transmission system. (April 2016) - Lin, Chih-Hong
Abstract: Because the nonlinear and time-varying characteristics of the V-belt continuously variable transmission system driven by a permanent magnet synchronous motor (PMSM) are unknown, improving the control performance of the linear control design is time-consuming. To overcome difficulties in the design of a linear controller for the PMSM servo-driven V-belt continuously variable transmission system with lumped nonlinear load disturbances, a composite recurrent Laguerre orthogonal polynomial neural network (NN) control system with ameliorated particle swarm optimization (PSO), which has the online learning capability to respond to the nonlinear time-varying system, was developed. The composite recurrent Laguerre orthogonal polynomial NN control system can perform inspector control, recurrent Laguerre orthogonal polynomial NN control which involves an adaptation law, and recouped control which involves an estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of ameliorated particle swarm optimization yielded two optimal learning rates for the parameters, which helped improve convergence. Finally, comparison of the experimental results of the present study with those of previous studies demonstrated the high control performance of the proposed control scheme. Highlights: A composite RLOPNN control with ameliorated PSO is proposed to control V-belt CVT. The simplified dynamic equations in the V-belt CVT are proposed. Control system consists of inspector control, RLOPNN control and recouped control. Online tuning method of parameters is based on Lyapunov stability theorem. Two optimal learning rates using ameliorated PSO is obtained.