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Electromagnetic torque control for synchronous reluctance motor servo-drive system applied in continuously variable transmission system

机译:用于同步磁阻电动机伺服驱动系统的电磁扭矩控制在连续变速器系统中应用

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摘要

Compared with the classical linear controller, the nonlinear controller can result better control performance for the nonlinear uncertainties of the continuously variable transmission (CVT) system which is driven by the synchronous reluctance motor (SynRM). The better control performance can be shown in the nonlinear uncertainties behavior of CVT system by using the proposed novel admixed improved recurrent Hermite polynomial neural network (AIRHPNN) control system. The novel AIRRHPNN control system can carry out overlooker control, improved recurrent Hermite polynomial neural network control (IRHPNN) with an adaptive law, and reimbursed control with an appraised law. Additionally, according to the Lyapunov stability theorem, the adaptive law in the IRHPNN and the appraised law of the eimbursed controller are established. Furthermore, two weights with two varied learning rates according to increment Lyapunov function are derived to help improving convergence. Finally, comparative examples are illustrated by the experimental results to confirm that the proposed control system could be obtained better control performance.
机译:与经典的线性控制器相比,非线性控制器可以为由同步磁阻电动机(SYNRM)驱动的连续变速器(CVT)系统的非线性不确定性来实现更好的控制性能。通过使用所提出的新型改进的经常性Hermite多项式多项式多项式神经网络(AIRHPNN)控制系统,可以在CVT系统的非线性不确定性行为中显示更好的控制性能。新型AirRHPNN控制系统可以开展忽略的控制,改善了经常性的Hermite多项式神经网络控制(IRHPNN),自适应法律,并通过评估法报销对照。此外,根据Lyapunov稳定性定理,建立了IRHPNN中的自适应法和所估计的控制器的评估法。此外,导出了两个具有两个不同学习率的重量,根据增量Lyapunov函数,以帮助提高收敛。最后,通过实验结果说明了比较例,以确认所提出的控制系统可以获得更好的控制性能。

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