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

2016 ◽  
Vol 64 ◽  
pp. 405-417 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Control System ◽  
Orthogonal Polynomials ◽  
Continuously Variable Transmission ◽  
Transmission System ◽  
Variable Transmission ◽  
Modified Pso

An intelligent dynamic control of continuously variable transmission system using modified particle swarm optimization

2015 ◽  
Vol 230 (13) ◽  
pp. 2181-2207 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Orthogonal Polynomials ◽  
Dynamic Control ◽  
Continuously Variable Transmission ◽  
Transmission System ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Variable Transmission

In order to capture nonlinear and dynamic behaviors of the V-belt continuously variable transmission system with lots of unknown nonlinear and time-varying characteristics, an intelligent dynamic control system using modified particle swarm optimization is proposed for controlling a permanent magnet synchronous motor servo-drive V-belt continuously variable transmission system to raise robustness. The intelligent dynamic control system comprised an inspector control system, a recurrent Laguerre-orthogonal-polynomials neural network controller with adaptive law and a recouped controller with estimation law. The adaptive law of parameters in the recurrent Laguerre-orthogonal-polynomials neural network is derived according to Lyapunov stability theorem. To achieve better learning performance and faster convergence, the modified particle swarm optimization is employed to regulate two varied learning rates of the parameters in the recurrent Laguerre-orthogonal-polynomials neural network. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.

Application of a V-belt continuously variable transmission system by using a composite recurrent Laguerre orthogonal polynomial neural network control system and modified particle swarm optimization

2015 ◽  
Vol 23 (9) ◽  
pp. 1437-1462 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Control System ◽  
Orthogonal Polynomial ◽  
Continuously Variable Transmission ◽  
Transmission System ◽  
Time Varying ◽  
Control Performance ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Variable Transmission ◽  
Adaptation Law

The nonlinear and time-varying characteristics of the V-belt continuously variable transmission system driven by a permanent magnet synchronous motor (PMSM) are unknown, therefore, 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 the lumped nonlinear load disturbances, a composite recurrent Laguerre orthogonal polynomial neural network (NN) control system which has online learning capability to respond 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 weight parameters in the recurrent Laguerre orthogonal polynomial NN is based on Lyapunov stability theorem. The use of modified particle swarm optimization yielded two optimal learning rates for the weight 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.

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

2015 ◽  
Vol 10 (5) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Orthogonal Polynomials ◽  
Continuously Variable Transmission ◽  
Time Varying ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Variable Transmission ◽  
Adaptation Law

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.

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