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http://dx.doi.org/10.6113/JPE.2019.19.3.771

SynRM Driving CVT System Using an ARGOPNN with MPSO Control System  

Lin, Chih-Hong (Department of Electrical Engineering, National United University)
Chang, Kuo-Tsai (Department of Electrical Engineering, National United University)
Publication Information
Journal of Power Electronics / v.19, no.3, 2019 , pp. 771-783 More about this Journal
Abstract
Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.
Keywords
Continuously variable transmission; Gegenbauer orthogonal polynomials neural network; Particle swarm optimization; Synchronous reluctance motor;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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