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首页> 外文期刊>International Journal of Advanced Robotic Systems >Sensorless Control of Electric Motors with Kalman Filters: Applications to Robotic and Industrial Systems
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Sensorless Control of Electric Motors with Kalman Filters: Applications to Robotic and Industrial Systems

机译:带有卡尔曼滤波器的电动机的无传感器控制:在机器人和工业系统中的应用

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The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. Sensorless control of the induction motor is again implemented through feedback of the estimated state vector. Additionally, a state estimation-based control loop is implemented using the Unscented Kalman Filter. Moreover, state estimation-based control is developed for the induction motor model using a nonlinear flatness-based controller and the state estimation that is provided by the Extended Kalman Filter. Unlike field oriented control, in the latter approach there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. The efficiency of the Kalman Filter-based control scheme...
机译:本文使用卡尔曼滤波技术研究直流和感应电动机的无传感器控制。首先考虑直流电动机的情况,并实施基于卡尔曼滤波器的控制。接下来,检查磁场定向感应电动机的非线性模型,并通过扩展卡尔曼滤波器估计电动机的角速度,该滤波器处理转子角的测量值。感应电动机的无传感器控制再次通过估计状态向量的反馈来实现。此外,使用无味卡尔曼滤波器可实现基于状态估计的控制环路。此外,使用基于非线性平坦度的控制器和扩展卡尔曼滤波器提供的状态估计,为感应电动机模型开发了基于状态估计的控制。与磁场定向控制不同,在后一种方法中,没有关于转子速度动力学和磁通量动力学之间去耦的假设。基于卡尔曼滤波器的控制方案的效率...

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