利用人工神经网络进行感应电动机解耦控制

为了使感应电动机具有象直流电动机一样优良的转矩与转速控制性能，提出了一种基于人工神经网络的感应电动机解耦控制方法。由于实时递归网络具有较强地表达和处理瞬态信息的能力，适合解决非线性动态系统问题，因此用递归网络构成的解耦控制器具有良好的动态特性。为减少这种神经网络解耦控制器的学习时间，提出了一种自适应学习算法，通过在网络学习的过程中不断地调整学习速率，从而加快了网络学习速度。仿真计算结果表明，这种神经网络解耦控制方式具有优良的动态响应特性。

Decoupling control of induction motors using artificial neural networks

In order to make a induction motor has excellent control performance of torque and speed as DC motors, a novel approach of the decoupling control of the indution motor based on artificial neural networks is presented. A decoupling controller built by a real time recurrent neural network has excellent dynamic performance, because neural network has the capability of representing and processing temporal information, which is applicable for nonlinear dynamic systems. In order to reduce the training time of neural network decoupling controller, an improved adaptive training algorithm is presented. The learning rate is adjusted incessantly during the training of the network. Thus, the training of the network is accelerated. Simulation result shows that the neural network decoupling control has a good dynamic response characteristic.