Hybrid recurrent Laguerre-orthogonal-polynomials neural network control with modified particle swarm optimization application for V-belt continuously variable transmission system

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.

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RETRACTED: Design of a composite recurrent Laguerre orthogonal polynomial neural network control system with ameliorated particle swarm optimization for a continuously variable transmission system

Control Engineering Practice ◽

10.1016/j.conengprac.2016.02.001 ◽

2016 ◽

Vol 49 ◽

pp. 42-59 ◽

Cited By ~ 3

Author(s):

Chih-Hong Lin

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Control System ◽

Orthogonal Polynomial ◽

Continuously Variable Transmission ◽

Network Control ◽

Neural Network Control ◽

Swarm Optimization ◽

Polynomial Neural Network ◽

Variable Transmission

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

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4030061 ◽

2015 ◽

Vol 10 (5) ◽

Cited By ~ 4

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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