【作者】 易伯瑜；

【导师】 康龙云；

【作者基本信息】 华南理工大学 ， 电力电子与电力传动， 2014， 博士

【摘要】 永磁同步电动机由于具有体积小、功率密度和效率高，运行性能好等优点，在理论研究和实际应用中得到广泛重视；目前，永磁同步电动机作为控制系统执行元件的核心已广泛地应用于数控机床、机器人以及航空、航天和航海等领域中。本文结合电动汽车应用的需要，以无位置传感器永磁同步电动机驱动系统为研究对象，有针对性地对电机转子位置和转速的在线估计、电机的控制策略以及死区补偿等问题进行了较为深入的研究。目的是为了降低电动汽车电气驱动系统的成本与复杂性，进一步提高控制系统的可靠性和控制性能。在研究课题中，首先根据坐标变换理论推导了永磁同步电动机在两相静止坐标系下的电机模型，并结合扩展卡尔曼滤波器理论设计了相应的转子速度、位置观测器。虽然扩展卡尔曼滤波算法能实现电机的自启动，但由于转子初始位置角未知，算法在启动中可能会出现的收敛错误和失速问题，本文针对这一问题给出了详细解释并讨论了相关的解决方法。由于噪声协方差矩阵对估计性能有很大的影响，本文通过仿真分析了不同矩阵取值对结果产生的影响，并总结了一套参数试凑方法。由于扩展卡尔曼滤波器的估计精度受电机模型参数变化影响，通过仿真总结了参数变化对估计精度影响的规律。针对这一问题，对自适应渐消扩展卡尔曼滤波器进行了较为深入的研究。引入衰减因子对原扩展卡尔曼滤波器的误差协方差矩阵进行加权，这样能够减小陈旧量测值对估计的影响，强化新的量测数据在滤波中所起的校正作用，从而能提高跟踪速度和估计精度。考虑到卡尔曼滤波器在高阶时计算量大的问题，引入一种两段式结构将扩展卡尔曼滤波器分解成两个并行的低阶滤波器，达到节省运算量的目的，通过乘法和加法运算量的对比体现出两段扩展卡尔曼滤波器在运算量上所具有的优势，利用滤波器之间的等效性验证了所提出滤波器的稳定性。结合自适应扩展卡尔曼滤波器和双段扩展卡尔曼滤波器各自的特点，提出一种新的自适应双段扩展卡尔曼滤波器，并采用相同的等效性证明验证了其稳定性，这种滤波器是将双段结构应用到自适应扩展卡尔曼滤波器上而得出，同时具有自适应滤波器强跟踪、鲁棒性好和双段滤波器节省运算量的优点。在电流控制中，针对已有的线性比例微分控制策略存在的动态响应速度慢，对控制器参数的依赖度高等问题，采用一种无差拍预测电流控制方法来进行永磁同步电动机的电流控制。由于这类基于模型的控制方法对参数精确度要求较高，设计了扰动观测器来估计未建模的不确定项，针对电压型逆变器中的死区时间和非线性等因素造成的电压损失，通过相应的死区电压观测器在线估计，并将两个观测器的估计值加入到电压指令值中进行补偿。由于速度环PI控制器的非线性饱和特性，提出了一种变结构抗饱和PI速度控制器来提高转速控制性能。针对自适应双段扩展卡尔曼滤波器和无差拍预测电流控制方法，设计了基于Expert3系统的全数字无位置传感器永磁同步电动机控制系统。在此基础上，对各研究内容进行了深入的仿真研究和实验验证。更多还原

【Abstract】 Because of several advantages, such as compactness, high power density, high efficiencyand good operation performance, permanent magnet synchronous motors (PMSMs) areattracting extensive attention in theory research and practical application. Now, PMSMs,which are used as a core implementation component, have been widely used in CNC, robot,aerospace, marine and other fields. In this paper, according to the requirements of the electricvehicle control system, this dissertation made the studies on sensorless control of PMSM.Taking the sensorless PMSM control system as the research target, this dissertation conductedsome research in-depth on the following issues, including on-line estimation of the rotorposition and speed, control method of the motor, dead-time compensation and so on. It aimsto reduce the cost and complexity of drive system, and to further improve the reliability andcontrol performance of motor control system.Based on coordinate transformation, PMSM model in the two-phase frame system wasderived firstly, and then an Extended Kalman Filter (EKF) for closed-loop rotor speed andposition estimation of PMSM was designed based on the above model. Although the initialrotor position is unknown, the sensorless control stategy has key ability of self-startup.Whereas, during startup transient, convergence error and stall issues may happen. In order tosolve these problems, detailed explanations for these issues are given and relevant solutionsare discussed.Considering that the noise covariance matrix has a great impact on estimationperformance, the influence on estimates by noise covariance matrix is analyzed by simulationresults, and the trial-and-error method for setting noise covariance matrix is summarized.This extended Kalman filtering technique requires complete specifications of dynamicalmodel parameters to guarantee estimation accuracy, and impact on estimation accuracy byparameters variation is summarized by simulation results. To solve this problem, an in-depthstudy on adaptive fading extended Kalman filter (AFEKF) is made. A fading factor, whichenhances the influence of innovation information, may be incorporated as a multiplier forimproving the tracking capability and estimation accuracy in high dynamic control of PMSM.To reduce computational complexity, a nonlinear two-stage extended Kalman filter(NTSEKF), which employs the two-stage structure, is proposed by decoupling the EKF intotwo parallel reduced-order filters. By using the number of arithmetic operations(multiplications and additions) as the measure of computational complexity, thecomputational advantage of the two-stage Kalman filter over the conventional Kalman Filter has been demonstrated. Because EKF is uniformly asymptotically stable, the stability ofNTSEKF is verified by showing that NTSEKF is equivalent to EKF. Combining advantagesof NTSEKF and AFEKF, adaptive two-stage extended Kalman filter (ATEKF) is developedby decoupling the AFEKF into two parallel reduced-order filters. ATEKF has both strongrobustness against model-plant parameter mismatches and good real-time state tracking ability.The stability of ATEKF is verified by the same method as in NTSEKF.Currently, linear PI controllers were mostly employed for the control of the current loop.PI controller gains excellent steady-state performance, but the dynamic response is slow andthe control effect depends greatly on the PI parameters. In order to overcome the drawbacksof the PI controller, this paper presented a novel control scheme for current loop by replacingthe PI controllers with predictive current control (PCC) controller. Inaccuracy in systemmodels may seriously degrade the performance of the PCC controller. Treating inaccuraciesas disturbances, the disturbances caused by the parameter variations and dead time arecompensated by the two online observers, respectively. Aiming at the saturation nonlinearityof the speed control loop PI controller, a variable structure anti-windup PI controller isproposed to improve the speed control performance.Based on Expert3system, a full-digital sensorless PMSM control system is designed torealize the ATEKF and PCC controller. Based on these algorithms and hardware, in-depthsimulation research and experiment validation are made.更多还原