Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training a...Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.展开更多
交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种...交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种注入多谐波电流产生的转矩补偿原有转矩脉动的控制策略。推导适用于任意次谐波磁链产生的转矩脉动通用解析模型;并基于此模型,给出利用谐波电流抑制转矩脉动的理论依据;提出在同步旋转坐标系下注入多次谐波电流的方法,抑制由2、4、5、7、11、13次谐波反电势引起的3、6、12次转矩脉动;并利用准-比例谐振控制器实现谐波电流的精确跟踪。最后,以一台三相9槽10极交替极永磁电机为例,通过不同工况下的转矩脉动抑制实验,验证所提控制策略的有效性。展开更多
基金the National Natural Science Foundation of China (60374032).
文摘Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.
文摘交替极永磁(consequent pole permanent magnet,CPPM)电机每对极下的气隙磁密不对称,在特定极槽配合下其反电动势(electromotive force,EMF)中存在2、4次等偶次谐波分量,引起额外的转矩脉动,降低转矩输出品质。为解决上述问题,提出一种注入多谐波电流产生的转矩补偿原有转矩脉动的控制策略。推导适用于任意次谐波磁链产生的转矩脉动通用解析模型;并基于此模型,给出利用谐波电流抑制转矩脉动的理论依据;提出在同步旋转坐标系下注入多次谐波电流的方法,抑制由2、4、5、7、11、13次谐波反电势引起的3、6、12次转矩脉动;并利用准-比例谐振控制器实现谐波电流的精确跟踪。最后,以一台三相9槽10极交替极永磁电机为例,通过不同工况下的转矩脉动抑制实验,验证所提控制策略的有效性。
