This paper proposes a sensorless field-oriented control (FOC) of an open-end stator winding five-phase induction motor (OESW-FPIM). The FOC technique used is associated with dual Space Vector Modulation (SVM) to provide a constant switching frequency and lower harmonics distortion. Furthermore, a simple hybrid observer is proposed which combines a model reference adaptive system (MRAS) and a sliding mode (SM) observer. The examined observer is designed for the estimation of the rotor flux and rotational speed as well as for the estimation of the load torque disturbances. Lyapunov theorem is used in this paper to prove the observer's stability. The work presented in this paper aims to enhance the researched motor's sensorless control and its robustness against external load disturbances and parameters variation. In the proposed MRAS-SM observer, the reference model is replaced by a SM model which uses a sigmoid function as a switching function to overcome the chattering problem. This combination is intended to make use of the advantages of both strategies. At the same time, to preserve the high-level performance of the sensorless FOC technique and to reduce system uncertainties, an estimation algorithm is developed to identify the rotor resistance and the stator resistance simultaneously during motor operation. The parameter estimation algorithm is combined with the proposed control to improve the speed estimation and control accuracy, particularly at low-speed operation. Finally, the effectiveness of the proposed control is validated in real-time by utilizing a hardware-in-the-loop (HIL) platform.

中文翻译：

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带参数估计的开端绕组五相感应电机无传感器磁场定向控制


本文提出了一种开端定子绕组五相感应电机（OESW-FPIM）的无传感器磁场定向控制（FOC）。所使用的 FOC 技术与双空间矢量调制 (SVM) 相关，可提供恒定的开关频率和较低的谐波失真。此外，提出了一种简单的混合观测器，它将模型参考自适应系统（MRAS）和滑模（SM）观测器结合起来。所检查的观测器设计用于估计转子磁通和转速以及负载扭矩扰动的估计。本文利用李亚普诺夫定理证明了观测器的稳定性。本文提出的工作旨在增强所研究电机的无传感器控制及其对外部负载干扰和参数变化的鲁棒性。在所提出的 MRAS-SM 观测器中，参考模型被 SM 模型取代，该模型使用 sigmoid 函数作为切换函数来克服抖振问题。这种组合旨在利用两种策略的优点。同时，为了保持无传感器 FOC 技术的高水平性能并减少系统不确定性，开发了一种估计算法以在电机运行期间同时识别转子电阻和定子电阻。参数估计算法与所提出的控制相结合，以提高速度估计和控制精度，特别是在低速运行时。最后，利用硬件在环（HIL）平台实时验证所提出控制的有效性。