基于智能模糊后步法观测器设计的感应电机鲁棒非线性无传感器控制

K. Abed, H. Zine
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摘要

简介感应电机(IM)的控制算法严重依赖于其参数,因此这些参数的任何变化(尤其是转子电阻的变化)都会不可避免地产生误差。为避免这一问题,研究人员提出了多种解决方案,包括可变结构控制(VSC)、自适应观测器(如模型参考自适应系统、扩展卢恩伯格观测器(ELO)和扩展卡尔曼滤波器(EKF)),这些解决方案可减少磁通和转速的估计误差。作为本文的新颖之处,模型速度观测器使用估计的电流和电压作为状态变量;我们通过误差反馈校正器开发了这一观测器。间接转子磁场定向控制(IRFOC)使用正确的速度观测值;在我们的研究中,我们通过使用后退式滑动模式(SM)控制来改进观测器的性能。目的。生成脉宽调制逆变器脉冲,以快速减少参数变化引起的误差。方法。为实现这一目标,我们开发了两种不同的线性观测器,用于高性能 VSC IM 驱动器,该驱动器对速度和负载转矩变化具有鲁棒性。首先,我们介绍了用于为 IM 供电的三电平逆变器;然后介绍了其建模和控制方法,最后在实验平台上展示了其输出信号。我们展示了 IRFOC 的方框图;我们用数学公式分析了建模的各个阶段,清楚地展示了解耦理论和无传感器控制技术。研究描述了两种观测器,即 ELO 和 EKF,用于估计 IM 速度和扭矩。在此基础上,我们利用模糊逻辑优化了阶跃响应,这有助于系统产生 PI 控制器增益。这两种观测器都由 SM 电流控制器转发,SM-ELO 和 SM-EKF 结构的收敛性通过将实际电流和观测电流之间的误差最小化为零来保证。结果。本文给出了几项结果,以显示所提方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent fuzzy back-stepping observer design based induction motor robust nonlinear sensorless control
Introduction. The control algorithm of Induction Motor (IM) is massively dependent on its parameters; so, any variation in these parameters (especially in rotor resistance) gives unavoidably error propagates. To avoid this problem, researches give more than solution, they have proposed Variable Structure Control (VSC), adaptive observers such as Model Reference Adaptive System, Extended Luenberger Observer (ELO) and the Extended Kalman Filter (EKF), these solutions reduce the estimated errors in flux and speed. As novelty in this paper, the model speed observer uses the estimated currents and voltages as state variables; we develop this one by an error feedback corrector. The Indirect Rotor Field Oriented Control (IRFOC) uses the correct observed value of speed; in our research, we improve the observer’s labour by using back-stepping Sliding Mode (SM) control. Purpose. To generate the pulse-width modulation inverter pulses which reduce the error due of parameters variations in very fast way. Methods. We develop for reach this goal an exploration of two different linear observers used for a high performance VSC IM drive that is robust against speed and load torque variations. Firstly, we present a three levels inverter chosen to supply the IM; we present its modelling and method of control, ending by an experiment platform to show its output signal. A block diagram of IRFOC was presented; we analyse with mathematic equations the deferent stages of modelling, showed clearly the decoupling theory and the sensorless technique of control. The study described two kinds of observers, ELO and EKF, to estimate IM speed and torque. By the next of that, we optimize the step response using the fuzzy logic, which helps the system to generate the PI controller gains. Both of the two observers are forward by SM current controller, the convergence of SM-ELO and SM-EKF structures is guaranteed by minimizing the error between actual and observed currents to zero. Results. Several results are given to show the effectiveness of proposed schemes.
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