Modelling and control of six-phase induction motor servo-driven continuously variable transmission system using blend modified recurrent Gegenbauer orthogonal polynomial neural network control system and amended artificial bee colony optimization

2016 ◽  
Vol 29 (5) ◽  
pp. 915-942 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony ◽  
Network Control ◽  
Neural Network Control ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Polynomial Neural Network ◽  
Variable Transmission ◽  
Bee Colony Optimization ◽  
And Control

Blend modified recurrent Gegenbauer orthogonal polynomial neural network control for six-phase copper rotor induction motor servo-driven continuously variable transmission system using amended artificial bee colony optimization

2016 ◽  
Vol 39 (6) ◽  
pp. 921-950 ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Orthogonal Polynomial ◽  
Artificial Bee Colony ◽  
Time Varying ◽  
Control Performance ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Polynomial Neural Network ◽  
Non Linear ◽  
Variable Transmission ◽  
Bee Colony Optimization

Because the non-linear and time-varying characteristics of the continuously variable transmission (CVT) system driven by using a six-phase copper rotor induction motor (IM) 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 six-phase copper rotor IM servo-driven CVT system with lumped non-linear load disturbances, a blend modified recurrent Gegenbauer orthogonal polynomial neural network (NN) control system, which has the online learning capability to return to the non-linear time-varying system, was developed. The blend modified recurrent Gegenbauer orthogonal polynomial NN control system can perform overseer control, modified recurrent Gegenbauer orthogonal polynomial NN control and recompensed control. Moreover, the adaptation law of online parameters in the modified recurrent Gegenbauer orthogonal polynomial NN is based on the Lyapunov stability theorem. The use of amended artificial bee colony optimization (ABCO) 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.

Comparative Dynamic Control of SynRM Servodrive Continuously Variable Transmission System Using Blend Amend Recurrent Gegenbauer-Functional-Expansions Neural Network Control and Altered Artificial Bee Colony Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Artificial Bee Colony ◽  
Continuously Variable Transmission ◽  
Control Performance ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Functional Expansions ◽  
Variable Transmission ◽  
Bee Colony Optimization

In comparison control performance with more complex and nonlinear control methods, the classical linear controller is poor because of the nonlinear uncertainty action that the continuously variable transmission (CVT) system is operated by the synchronous reluctance motor (SynRM). Owing to good learning skill online, a blend amended recurrent Gegenbauer-functional-expansions neural network (NN) control system was developed to return to the nonlinear uncertainties behavior. The blend amended recurrent Gegenbauer-functional-expansions NN control system can fulfill overseer control, amended recurrent Gegenbauer-functional-expansions NN control with an adaptive dharma, and recompensed control with a reckoned dharma. In addition, according to the Lyapunov stability theorem, the adaptive dharma in the amended recurrent Gegenbauer-functional-expansions NN and the reckoned dharma of the recompensed controller are established. Furthermore, an altered artificial bee colony optimization (ABCO) yields two varied learning rates for two parameters to find two optimal values, which helped improve convergence. Finally, the experimental results with various comparisons are demonstrated to confirm that the proposed control system can result in better control performance.

A Hermite neural network incorporating artificial bee colony optimization to model shoreline realignment at a reef-fronted beach

2018 ◽  
Vol 280 ◽  
pp. 32-45 ◽  
Author(s):  
George E. Tsekouras ◽  
Vasilis Trygonis ◽  
Andreas Maniatopoulos ◽  
Anastasios Rigos ◽  
Antonios Chatzipavlis ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization
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