神经网络在直接力矩控制中的研究及应用

提出了利用多层神经网络取代传统开关状态选择器在感应电动机直接力矩中的控制以获得最优的开关状态量的方法。利用MATLAB/MULINK软件做的直接力矩控制的仿真系统包括感应电动机、逆变器开关、触发电路和控制电路。在选定了代表开关状态选择器的最优神经网络后 ,设计了神经网络结构图并进行了验证。为了确定神经网络的可靠性和稳定性 ,将其作为直接力矩控制系统的一部分进行了仿真试验 ,结果表明神经网络直接力矩控制系统优于传统的直接力矩控制系统

APPLICATION OF NEURAL NETWORKS FOR DIRECT TORQUE CONTROL

The use of a multi layer neural network to emulate the traditional switching look up table method for induction motor direct torque control(DTC)obtaining optimal switching patterns,is presented.The complete system simulation of the DTC including induction motor,inverter switch,firing circuit and control unit is done using the MATLAB/MULINK program.After choosing the best type of neural network,which represents the switching lookup table,a suitable neural network configuration is deduced and then tested.Then the neural network is tested as a part of the DTC in order to know its stability and reliability.It is shown that the use of neural netwoks in this application gives advantages over the conventional DTC.