基于自适应观测器和自抗扰控制的PMSM直接转矩控制

提出一种新的自适应观测器,用于永磁同步电机直接转矩控制系统的定子磁链和定子电阻估计。该观测器利用静止坐标系下的电压模型估计定子磁链,采用转子坐标系下的电流模型估计定子电流,把定子电流估计误差通过增益矩阵作为反馈信号。定子电阻自适应律根据李亚普诺夫稳定性理论推出,观测器的增益矩阵采用极点配置方法得到,观测器的收敛性根据李亚普诺夫稳定性分析来保证。此外,采用自抗扰控制技术设计了速度控制器,该控制器能对负载扰动进行估计和补偿。仿真结果证明了所提自适应观测器的有效性和自抗扰控制器的抗负载扰动能力。

Abstract：

A new adaptive observer was proposed to estimate the stator flux and stator resistance in the direct torque controlled permanent magnet synchronous motor （PMSM） drive. The stator flux was estimated by using the motor voltage model in the stationary frame, and the stator currents were estimated by using the motor current model in the rotor frame. The current estimation errors were then used as feedback signals through a gain matrix. Stator resistance adaptive law was developed based on Lyapunov stability theory. The observer gain matrix was obtained by using the pole placement method. The stability of the adaptive observer was guaranteed by the Lyapunov stability analysis. In addition, a speed controller was designed based on active disturbance rejection control （ADRC） technique to estimate and compensate the load disturbance. Simulation results have confirmed both the effectiveness of the adaptive observer and the anti-load-disturbance performance of the active disturbance rejection controller.     

Application of neural networks for permanent magnet synchronous motor direct torque control

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.     

感应电机无速度传感器模糊控制仿真研究

基于Takagi-Sugeno（T-S）模糊模型,提出了一种感应电机的模糊转速磁链观测器,并实现了一种新的感应电机无速度传感器模糊控制方法。该方法利用Lyapunov稳定性理论分析了观测器的稳定性,通过求解受线性矩阵不等式（LMI）约束的广义特征值问题来得到观测器的反馈矩阵。在Matlab软件平台上,利用SimPowerSystems Blocks（PSB）电气模块和Simulink通常模块搭建了基于模糊观测器的无速度传感器控制系统,仿真实验验证了该系统的正确性。仿真结果表明该方法在全速度范围各种工况下都能稳定工作,准确观测定子磁链和转速。

Abstract：

Based on Takagi-Sugeno （T-S） fuzzy model,a fuzzy speed and flux observer of induction motor （IM） was proposed and a novel speed sensorless fuzzy control for IM drives was developed based on fuzzy observer design. This method utilized Lyapunov stability theory to analyze the stability for the observer. The observer gains were obtained by solving a generalized eigenvalue problem （GEVP） under linear matrix inequality （LMI） constraints. The speed sensorless control system of IM drives on the basis of the fuzzy observer was built with PSB blocks and Simulink blocks in Matlab platform and verified by simulation results. The simulation results indicate that the proposed method could operate stably in whole range of speed with preferable observation precision of stator flux and rotor speed.     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

Robust stability analysis for a cruise missile

A global controller design methodology for a flight stage of the cruise missile is proposed. This methodology is based on the method of least squares, To prove robust stability in the full airspace with parameter disturbances, the concepts of convex polytopic models and quadratic stability are introduced, The effect of aerodynamic parameters on system performance is analyzed. The designed controller is applied to track the overloading signal of the cruise segment of the cruise missile, avoiding system disturbance owing to controller switching, Simulation results demonstrate the validity of the proposed method.     

汽车ASS与EPS分层协调控制

以提高汽车行驶平顺性和操纵稳定性为出发点,建立了汽车ASS与EPS的整车动力学模型,提出了一种汽车ASS与EPS集成控制的方法。分别设计了ASS子系统模糊控制器与EPS子系统模糊控制器,利用分层协调控制思想建立了上层协调控制器,对两个子系统进行协调控制。仿真结果表明：采用分层协调控制策略控制的ASS与EPS集成系统可使车身垂直加速度、车身俯仰角、横摆角速度、车身侧倾角等性能参数得到优化,汽车行驶平顺性和操纵稳定性有明显改善,提高了整车综合性能。

Abstract：

Vehicle dynamics model of ASS and EPS was built.A coordination control method of vehicle ASS and EPS was presented to improve vehicle ride performance and handling stability.ASS controller and EPS controller were designed separately.The controller of ASS was designed using fuzzy logic control theory and the EPS system was designed using fuzzy logic control theory too.In order to coordinate the two subsystems,an upper coordination controller was set up based on an idea of delaminating control.The simulation results show that layered coordination control strategy can optimize the vehicle performance parameters and the vehicle integrated performance is improved obviously.     

空间矢量调制型凸极同步电动机直接转矩控制

凸极同步电动机的定子磁链变化时间常数通常较小,采用传统的双滞环直接转矩控制(DTC)策略使得电机的电磁转矩和定子磁链存在着较大的脉动。将空间矢量调制型直接转矩控制(SVM-DTC)策略引入到凸极同步电动机,优化的空间电压矢量组合实现了转矩、磁链误差的精确补偿,同时保证功率器件开关频率恒定。仿真结果表明,与凸极同步电动机传统DTC相比,基于SVM-DTC策略的控制系统能够使电机定子电流畸变率明显降低;转矩和定子磁链脉动显著减小;转矩响应速度快;运行平稳。     

STAP method based on atomic norm minimization with array amplitude-phase error calibration

In this paper,a space-time adaptive processing(STAP)method is proposed for the airborne radar with the array amplitude-phase error considered,which is based on atomic norm minimization(ANM).In the conventional ANM-based STAP method,the influence of the array amplitude-phase error is not considered and restrained,which inevitably causes performance deterioration.To solve this problem,the array amplitude-phase error is firstly estimated.Then,by pre-estimating the array amplitude-phase error information,a modified ANM model is built,in which the array amplitude-phase error factor is separated from the clutter response and the clutter covariance matrix(CCM)to improve the estimation accuracy of the CCM.To prove that the atomic norm theory is applicable in the presence of the array amplitude-phase error,the clutter sparsity is analyzed in this paper.Meanwhile,simulation results demonstrate that the proposed method is superior to the state-of-the-art STAP method.Moreover,the measured data is used to verify the effectiveness of the proposed method.     

神经网络模糊预测优化控制在VAV系统中的应用

将神经网络、模糊控制与非线性预测优化控制结合起来,提出了神经网络模糊预测优化控制方法,采用前馈神经网络作为预测模型,利用贝叶斯正则化方法对模型进行了辨识,以自调整模糊控制器作为优化控制器,通过多步预测方式,系统的优化性能指标综合考虑温度偏差最小和能耗最小这两方面因素,应用该方法对制冷工况变风量空调系统的送风温度和回风温度（室内温度）进行了仿真控制研究。控制结果表明了该方法的有效性,控制效果良好,并且可以达到节省能耗的目的。

Abstract：

Artificial neural network,fuzzy control and nonlinear optimal predictive control were combined.The algorithm of neural network nonlinear fuzzy predictive optimal control was proposed.Feed-forward neural network was adopted as the predictive model of the cooling VAV system.The model was identified by the method of Bayesian regularization.The self-adjusting fuzzy controller was adopted as optimal controller.The algorithm was applied in the cooling VAV system with multi-step predictive method.Indoor temperature and supply air temperature was controlled aimed at minimum temperature deviation and minimum energy consumption by this scheme in Matlab.Simulation results illustrate the effectiveness of this technique,and in the meantime illustrate that this technique can save energy consumption.     

基于无线传输的非线性网络控制系统的模糊控制

考虑控制器与被控对象之间采用无线传输,且被控对象为非线性模型的一类网络控制系统,对其设计了稳定的模糊控制器。首先基于T-S模糊模型将对象线性化,并利用模糊主导子系统和主导控制规则,简化了模糊控制器的设计。其次,将闭环控制系统方程等效为与无线传输跳数相关的离散切换系统,并证明了闭环系统的稳定性。最后,通过一个仿真例子验证了文中方法的有效性。

Abstract：

Considering a class of networked control systems using wireless transmission between the controller and the controlled object,and with nonlinear controlled object,the stable fuzzy controller was designed.Firstly,based on T-S fuzzy model,the object was linearized.And the design of the fuzzy controller was simplified by using the fuzzy dominant subsystem and the dominant control rule.Secondly,the equation of the closed-loop control system was equal to the discrete switched system related to the hop count of the wireless transmission.Finally,a simulation example was given to demonstrate the effectiveness of the proposed method.     

基于U-I模型的感应电机定子磁链估计的仿真研究

定子磁场定向的直接转矩控制逐渐被广泛应用于高性能异步电机调速系统。定子磁链估计精度是影响控制系统动态、稳态性能的主要因素。由于定子电阻参数变化及低通滤波器的影响，尤其低速运行时，传统定子磁链估计方法的估计精度及快速性往往很差。提出了一个全新的改进U-I模型定子磁链估计方案，不仅解决了定子电阻整定补偿问题。而且解决了低通滤波器截止频率优化补偿问题．在此基础上，建立直接转矩控制方案的仿真系统，仿真结果表明提出的方案能够使异步电机在低速到高速范围内都能得到令人满意的性能。     

基于模糊空间矢量调制的直接转矩控制方案

提出了一种基于模糊空间矢量调制(SVM)技术的异步电机直接转矩控制(DTC)方案。传统SVM-DTC中使用两个PI控制器来产生参考电压矢量的两个分量。设计了转矩模糊控制器代替原有的转矩PI控制器,并给出了基于此模糊控制器的SVM-DTC系统。该转矩模糊控制器的输入是转矩误差与转矩误差变化率,输出为参考电压矢量的转矩分量。针对所提出的方案进行了仿真实验,并与传统SVM-DTC相比较。仿真结果表明该模糊SVM-DTC方案能够提高系统的鲁棒性。     

基于SVPWM的直接转矩控制新方案

针对传统的直接转矩控制(DTC)转矩脉动大、开关频率不恒定等问题,提出了一种基于空间电压矢量脉宽调制(SVPWM)技术的直接转矩控制系统新方案.该系统采用经遗传算法优化后的模糊控制器代替常规的磁链和转矩控制器,以获得任意相位的空间电压矢量,然后利用SVPWM技术来合成该矢量.仿真结果表明该方案能有效减小磁链和转矩脉动,改善系统的性能.     

基于空间矢量调制的异步电机直接转矩控制

对一种基于空间矢量调制（SVM）技术的异步电机直接转矩控制（DTC）系统进行了建模与仿真研究，基于SVM的DTC方案可以有效地减小转矩波动。在SVM-DTC中使用了两个PI控制器，可以得到能够准确补偿当前定子磁链与转矩误差的参考电压矢量。然后，利用SVM技术将参考电压矢量转换为相应的PWM开关信号。给出在MATLAB／Simulink下对这种SVM-DTC系统的仿真结果，说明了模型的有效性。     

基于DSVM直接转矩控制的风力机实时模拟系统

为解决实验室无法用真实风力机研究风力发电的相关技术问题,提出了用先进的直接转矩控制（DTC）策略控制异步电机模拟风力机特性的技术。设计了风机转矩的计算方法,及异步电机直接转矩控制系统,使其跟随风机转矩变化,进一步利用离散空间矢量调制（DSVM）技术改善了系统的低速性能,并在RT-LAB仿真平台下对所设计的风力机模拟系统进行了实时仿真,验证了系统稳定性好,准确性高的特点,为实验室内研究风力发电技术提供了一个良好的方案。     

一种新的永磁同步电机直接转矩控制方法

提出了一种新的无速度传感器永磁同步电机直接转矩控制方法。针对内埋式永磁同步电机直接转矩控制存在较大脉动转矩和逆变器的开关频率不恒定的缺点,设计了一种自适应模糊控制器确定占空比的方法。与其它基于自适应模糊控制器的直接转矩方法不同,这种自适应模糊控制器通过控制正反电压矢量作用时间,在每一采样周期计算出最优占空比。该控制器输出部分的比例因子可以根据转矩的变化趋势经自适应机构的模糊规则库在线调整,不仅可以使永磁同步电机直接转矩控制系统保持恒定的开关频率,而且可以有效地减小磁链和转矩脉动,特别是低速时的转矩脉动。为了获得高性能的无速度传感器永磁同步电机控制系统,设计了一种无速度传感器方案。理论分析和仿真实验结果证明了自适应模糊永磁同步电机直接转矩控制策略的有效性。     

电网电压跌落故障下双馈风力发电机无源性控制

为提高电网电压跌落故障情况下双馈风力发电系统不间断运行的能力,提出了一种基于无源性理论的双馈风力发电机低电压穿越控制策 略. 通过分析双馈风力发电机系统模型的无源性,设计了状态反馈控制器;根据双馈风力发电机在电网电压跌落故障时的运行特点,给出了一种 计及电网电压变化的期望状态值计算方法,状态期望值跟随电网电压变化而迅速改变,通过状态反馈控制器可实现电机电流快速跟踪. 仿真结果 表明在电网电压跌落故障下所提出的控制策略可以有效的抑制双馈风力发电机定、转子过电流,并且降低了电磁转矩波动幅值.     

基于强跟踪滤波器的PMSM无速度传感器DTC策略

针对扩展卡尔曼滤波器(EKF)对模型误差鲁棒性差的问题,提出了基于强跟踪滤波器(STF)的永磁同步电机(PMSM)无速度传感器直接转矩控制(DTC)策略,该方法利用永磁同步电机定子坐标系下的非线性数学模型和强跟踪滤波技术建立了STF观测器,从而实现了对电机定子磁链、转速和转子位置的实时在线观测。仿真结果表明,STF观测器可以准确地观测出电机定子磁链和转速,同时与EKF观测器相比,该观测器对于系统参数摄动、外部干扰等系统模型失配具有更强的鲁棒性。