The healthy and rapid development of the controlled cooling technology was hampered by the uneven cooling phenomenon. During the process of hot plate production,the homogeneous cooling along the length direction of pl...The healthy and rapid development of the controlled cooling technology was hampered by the uneven cooling phenomenon. During the process of hot plate production,the homogeneous cooling along the length direction of plate was constrained by lots of factors. And because the speed was a flexible control parameter,the calculation method of optimal speed profile was developed based on the measured start cooling temperature and its matrix equation was solved by the Cholesky decomposition method. The optimal speed profile was used in online control system. As a result,the temperature distribution along the plate length direction was relatively uniform,and 95% of measured final cooling temperature difference from the target temperature 700 ℃ was controlled within ±20 ℃.展开更多
This paper proposes a gain scheduled control method for a doubly fed induction generator driven by a wind turbine. The purpose is to design a variable speed control system so as to extract the maximum power in the reg...This paper proposes a gain scheduled control method for a doubly fed induction generator driven by a wind turbine. The purpose is to design a variable speed control system so as to extract the maximum power in the region below the rated wind speed. Gain scheduled control approach is applied in order to achieve high performance over a wide range of wind speed. A double loop configuration is adopted. In the inner loop, the rotor speed is used as the scheduling parameter, while a function of wind and rotor speed is used as the scheduling parameter in the outer loop. It is verified in simulations that a high tracking performance has been achieved.展开更多
Control model of ultrasonic motor is the foundation for high control performance.The frequency of driving voltage is commonly used as control variable in the speed control system of ultrasonic motor.Speed control mode...Control model of ultrasonic motor is the foundation for high control performance.The frequency of driving voltage is commonly used as control variable in the speed control system of ultrasonic motor.Speed control model with the input frequency can significantly improve speed control performance.Step response of rotating speed is tested.Then,the transfer function model is identified through characteristic point method.Considering time-varying characteristics of the model parameters,the variables are fitted with frequency and speed as the independent variables,and the variable model of ultrasonic motor system is obtained,with consideration of the nonlinearity of ultrasonic motor system.The proposed model can be used in the design and analysis of the speed control system in ultrasonic motor.展开更多
The mathematical model of ultrasonic motor(USM)is the foundation of the motor high performance control.Considering the motor speed control requirements,the USM control model identification is established with frequenc...The mathematical model of ultrasonic motor(USM)is the foundation of the motor high performance control.Considering the motor speed control requirements,the USM control model identification is established with frequency as the independent variable.The frequency-speed control model of USM system is developed,thus laying foundation for the motor high performance control.The least square method and the extended least square method are used to identify the model.By comparing the results of the identification and measurement,and fitting the time-varying parameters of the model,one can show that the model obtained by using the extended least square method is reasonable and possesses high accuracy.Finally,the frequency-speed control model of USM contains the nonlinear information.展开更多
风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—...风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—长短记忆(LSTM)模型,提出独立预测法、分量预测法和多变量预测法等3种风速与风向联合预测方法,并利用兰新高铁大风监测实测数据对沿线多个基站的短期风速和风向进行同步联合预测。首先,通过归一化预处理原始风向和风速序列,并运用控制变量法确定最优时间步长和模型参数。其次,采用BPTT(Backpropagation Through Time)和Adam算法进行迭代训练,并结合早停法控制收敛,得到优化后的网络结构。最后,利用训练好的LSTM网络,采用3种方法对风速和风向进行联合预测。4个基站的实验结果表明,优化后的LSTM模型可以有效提取风速风向时间序列的长期依赖特征,结合联合预测方法能够实现对风速和风向的高精度同步预测;3种联合预测方法都能在较小范围内准确预测风速和风向,除5520基站外,风速预测误差在15%以内,风向预测误差在20%以内,其中多变量预测法表现出最优的整体预测精度,独立预测法次之。本研究为风速风向的联合预测提供了新的视角,对保障高铁列车运行的安全性具有参考价值。展开更多
文摘The healthy and rapid development of the controlled cooling technology was hampered by the uneven cooling phenomenon. During the process of hot plate production,the homogeneous cooling along the length direction of plate was constrained by lots of factors. And because the speed was a flexible control parameter,the calculation method of optimal speed profile was developed based on the measured start cooling temperature and its matrix equation was solved by the Cholesky decomposition method. The optimal speed profile was used in online control system. As a result,the temperature distribution along the plate length direction was relatively uniform,and 95% of measured final cooling temperature difference from the target temperature 700 ℃ was controlled within ±20 ℃.
文摘This paper proposes a gain scheduled control method for a doubly fed induction generator driven by a wind turbine. The purpose is to design a variable speed control system so as to extract the maximum power in the region below the rated wind speed. Gain scheduled control approach is applied in order to achieve high performance over a wide range of wind speed. A double loop configuration is adopted. In the inner loop, the rotor speed is used as the scheduling parameter, while a function of wind and rotor speed is used as the scheduling parameter in the outer loop. It is verified in simulations that a high tracking performance has been achieved.
基金supported by the National Natural Science Foundation of China(No.U1304501)
文摘Control model of ultrasonic motor is the foundation for high control performance.The frequency of driving voltage is commonly used as control variable in the speed control system of ultrasonic motor.Speed control model with the input frequency can significantly improve speed control performance.Step response of rotating speed is tested.Then,the transfer function model is identified through characteristic point method.Considering time-varying characteristics of the model parameters,the variables are fitted with frequency and speed as the independent variables,and the variable model of ultrasonic motor system is obtained,with consideration of the nonlinearity of ultrasonic motor system.The proposed model can be used in the design and analysis of the speed control system in ultrasonic motor.
基金supported by the National Natural Science Foundation of China(No.U1304501)
文摘The mathematical model of ultrasonic motor(USM)is the foundation of the motor high performance control.Considering the motor speed control requirements,the USM control model identification is established with frequency as the independent variable.The frequency-speed control model of USM system is developed,thus laying foundation for the motor high performance control.The least square method and the extended least square method are used to identify the model.By comparing the results of the identification and measurement,and fitting the time-varying parameters of the model,one can show that the model obtained by using the extended least square method is reasonable and possesses high accuracy.Finally,the frequency-speed control model of USM contains the nonlinear information.
文摘风速和风向是影响高速列车运行安全的重要因素,对高铁沿线的大风风速和风向进行有效预测有助于及时地对列车运行状况进行评估和预警。目前高铁大风领域的研究主要集中在风速的预测,尚未考虑风速风向的联合预测。基于深度循环神经网络—长短记忆(LSTM)模型,提出独立预测法、分量预测法和多变量预测法等3种风速与风向联合预测方法,并利用兰新高铁大风监测实测数据对沿线多个基站的短期风速和风向进行同步联合预测。首先,通过归一化预处理原始风向和风速序列,并运用控制变量法确定最优时间步长和模型参数。其次,采用BPTT(Backpropagation Through Time)和Adam算法进行迭代训练,并结合早停法控制收敛,得到优化后的网络结构。最后,利用训练好的LSTM网络,采用3种方法对风速和风向进行联合预测。4个基站的实验结果表明,优化后的LSTM模型可以有效提取风速风向时间序列的长期依赖特征,结合联合预测方法能够实现对风速和风向的高精度同步预测;3种联合预测方法都能在较小范围内准确预测风速和风向,除5520基站外,风速预测误差在15%以内,风向预测误差在20%以内,其中多变量预测法表现出最优的整体预测精度,独立预测法次之。本研究为风速风向的联合预测提供了新的视角,对保障高铁列车运行的安全性具有参考价值。
