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.
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
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 17, 2015; final manuscript received November 22, 2016; published online March 13, 2017. Assoc. Editor: Heikki Handroos.
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Lin, C. (March 13, 2017). "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." ASME. J. Dyn. Sys., Meas., Control. May 2017; 139(5): 051007. https://doi.org/10.1115/1.4035349
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