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首页> 外文期刊>Nonlinear dynamics >Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization
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Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization

机译：基于变粒子群优化算法的复合递归Laguerre正交多项式神经网络动态控制。

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摘要

The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The 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 optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.
机译：为了控制永磁同步电动机驱动的三角皮带无级变速器（CVT）系统，开发了一种基于变粒子群优化（PSO）的复合递归拉盖尔正交多项式神经网络控制系统。三角皮带无级变速系统的简化的动力学和运动学模型是根据守恒定律推导出来的。该控制系统由检查器控制，具有适应律的递归Laguerre正交多项式NN控制和具有估计律的补偿控制组成。此外，递归Laguerre正交多项式NN中在线参数的自适应律源自Lyapunov稳定性定理。另外，提出了借助于改变的PSO的两个参数的最佳学习率，以实现更好的收敛。最后，通过实验结果进行了比较研究，以说明所提出的控制方案的控制性能。

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