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首页> 外文会议>2000 International Conference on Power System Technology >Neuro-optimal controller for three-phase induction motor based on Levenberg-Marquardt training algorithm
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Neuro-optimal controller for three-phase induction motor based on Levenberg-Marquardt training algorithm

机译：基于Levenberg-Marquardt训练算法的三相感应电动机神经最优控制器

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摘要

The features of neural network make the neuro-controllers to be fitted in electrical drives, in which the higher speed computations is required The core of the neurocontrol systems is the neurocontroller, and the key technique is the training alghoritms . In this paper the features of the Levenberg-Marquardt algorithm will be emphasized. The obtained neurocontroller, based on it, provides an approximate solution of matrix Riccati differential equation (MRDE). Hence, the obtained neuro-optimal controller performs, during on dynamic regimes, the main tasks: smooth response, no oscillations on the control interval, no overshoot, the fast compensation of the load torque, the input energy minimization. In order to obtain the accuracy of the optimal solution, the input pattern of the neuro-optimal controller was choice to include all the nonlinearities of the three-phase induction motor in rotor field oriented coordinates fed by optimal current.
机译：神经网络的特性使神经控制器可以安装在需要更高速度计算的电气驱动器中。神经控制系统的核心是神经控制器，关键技术是训练算法。本文将重点介绍Levenberg-Marquardt算法的功能。基于此获得的神经控制器，提供了矩阵Riccati微分方程（MRDE）的近似解。因此，获得的神经最优控制器在动态状态下执行主要任务：平稳响应，控制间隔无振荡，无超调，负载转矩的快速补偿，输入能量最小化。为了获得最优解的精度，选择神经最优控制器的输入模式，使其在由最优电流馈送的转子磁场定向坐标中包括三相感应电动机的所有非线性。

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