基于ESO的无速度传感器PMSM系统自适应滑模FCS-MPC

为了提高三相永磁同步电机(PMSM)控制系统的性能,基于反双曲正弦函数的扩张状态观测器(ESO)技术,提出一种新颖的无速度传感器自适应滑模有限控制集模型预测控制(FCS-MPC)策略,采用ESO技术构造PMSM系统转速和反电动势的观测器,实现对电机转速和反电动势快速准确估计.用带有负载ESO的自适应滑模控制作为系统的转速调节器,以提高系统的鲁棒性;利用基于快速矢量选择的FCS-MPC策略,达到减少转矩脉动、降低系统算法计算量的目的.仿真结果表明,基于ESO的无速度传感器自适应滑模FCS-MPC策略能够使PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于积分型滑模面的自适应滑模FCS-MPC策略相比,所提出的控制策略能使系统具有良好的动态性能和抗负载干扰能力.     

基于扩张状态观测器的无速度传感器容错逆变器驱动永磁同步电机系统自抗扰模型预测转矩控制

针对三相四开关逆变器驱动永磁同步电机(PMSM)系统,基于扩张状态观测器(ESO)技术,提出了无速度传感器的自抗扰模型预测转矩控制(ADRMPTC)策略.建立了三相四开关逆变器驱动PMSM系统的数学模型;采用ESO技术构造了PMSM系统转速观测器,以实现对转速快速准确地实时估计;用自抗扰控制器(ADRC)作为系统的转速调节器,以提高系统的鲁棒性;利用模型预测转矩控制(MPTC)方法,以达到减小转矩和磁链脉动的目的.所设计基于ESO的无速度传感器ADRMPTC策略能够使三相四开关逆变器驱动的PMSM系统可靠稳定运行,达到满意的转矩和转速控制效果.与基于PI的MPTC策略相比,本文控制策略使PMSM系统不仅具有良好的动态性能,而且具有较强的抗负载干扰能力.仿真结果验证了所提方法的正确性和有效性.     

基于GFTSM的永磁同步电机驱动系统单相电流传感器模型预测转矩控制

针对仅有单相电流传感器的永磁同步电机（PMSM）驱动系统,提出了基于全局快速终端滑模（GFTSM）的模型预测转矩控制（MPTC）策略.通常情况下MPTC系统必需两个相电流传感器,考虑PMSM系统仅有一相电流传感器以及定子电阻变化情况,本文设计了一种既能观测剩余两相定子电流又能观测定子电阻变化的新型自适应观测器.此外,考虑系统参数变化及外部扰动,设计了一种新型的基于GFTSM的转速调节器以此来增强系统鲁棒性.本文基于滑模控制理论的GFTSM,在到达阶段和滑动模态阶段同时采用了快速终端滑模.所设计的基于GFTSM的PMSM单相电流传感器MPTC系统具有同基于GFTSM的PMSM两相电流传感器MPTC系统几乎一致的优良动态性能.此外,同基于PI和基于SM转速调节器的PMSM MPTC系统相比,当出现负载变化时,本文所设计的系统具有更好的动态响应、更强的鲁棒性以及更小的三相定子电流THD值.仿真结果验证了所设计系统的正确性和有效性.     

改进滑模模型参考自适应的PMSM无传感控制

针对MRAS（模型参考自适应）观测器对PMSM（永磁同步电机）参数变化和外部干扰敏感的缺点,设计了一种用于PMSM无传感控制的软开关滑模模型参考自适应观测器。该观测器将滑模控制与MRAS相结合,并构造了边界层可变的正弦饱和函数,以抑制由于滑模控制引起的系统抖动,增强系统鲁棒性。同时引入sigmoid函数,以提高滑模软切换速度控制器的稳定性。实验仿真结果表明,软开关滑模模型参考自适应控制不仅可以实现边界层内的快速收敛、弱化抖动,而且跟踪能力强。     

基于双滑模变结构MRAS的PMSM矢量控制研究

永磁同步电机(PMSM)传统的模型参考自适应系统(MRAS)存在负载扰动、响应速度慢、跟踪精度差等问题。针对上述问题,提出了一种双滑模变结构的MRAS方法。通过把传统MRAS观测器中的PI环节和PI速度调节器用滑模变结构控制器代替,提高了系统的抗干扰能力,采用Sigmoid连续函数代替滑模变结构控制中的符号函数,削弱了滑模控制引起的系统震荡。仿真结果表明,双滑模变结构MRAS方法可以改善PMSM控制系统的动静态性能,具有更强的鲁棒性。     

PMSM无速度传感器最优转矩系统的复合控制

针对PMSM的数学模型和最优转矩矢量控制方案,提出了一种在同步旋转坐标系下的永磁同步电机滑模变结构速度调节策略结合模型参考自适应无速度传感器速度辨识方案,应用于PMSM最优转矩矢量控制系统中。取代了传统PI调节器,保证了系统对参数摄动和不确定因素有很强的鲁棒性,Popov超稳定性理论保证了系统的稳定性。理论分析和仿真结果表明,所提出的速度调节策略和辨识方案使最优转矩矢量控制系统具有较强的鲁棒性和较好的动静态性能。     

基于分数阶滑模观测器的三相八开关容错逆变器驱动 永磁同步电机系统无传感器FCS–MPC

针对三相八开关容错逆变器的永磁同步电机(permanent magnet synchronous motor,PMSM)驱动系统,基于分数阶滑模变结构技术,提出了一种新颖的无速度传感器有限控制集模型预测控制(finite-control-set model predictive control,FCS–MPC)策略.通过驱动系统的运行模式建立三相八开关逆变器和永磁同步电机的数学模型;采用分数阶滑模变结构方法构造PMSM系统转速和反电动势的观测器,以实现对系统转速和反电动势快速准确地实时估计;利用改进型的FCS–MPC策略,以达到减少控制系统计算量和电磁转矩脉动的目的,与此同时,本文中的电流反馈特性的容错控制可有效抑制直流侧母线电容分压不均衡对系统运行产生的不利影响.仿真结果表明,该方法能够保证八开关容错逆变器驱动PMSM系统可靠稳定运行、具有良好的动态性能,并能降低定子电流总谐波失真值.     

永磁同步电机系统的无速度传感器研究

为了提高永磁同步电机系统的抗干扰能力,提出一种无速度传感器方法,用于速度辨识.将滑模(SM)变结构控制与模型参考自适应系统(MRAS)方法相结合,选取电机本体作为参考模型,利用逆变器输出的电压和电流,构建基于磁链方程的可调模型,利用两模型误差运用SM变结构方法辨识速度.在Matlab仿真平台对无速度传感器方法进行了分析,研究结果表明:所提出的无速度传感器方法具有较好的动静态性能,可以实现对速度的准确辨识.     

永磁同步电动机无速度传感器矢量调速系统的积分反步控制

基于永磁同步电动机(PMSM)的数学模型, 设计了由积分反步控制和滑模变结构模型参考自适应系统组成的无速度传感器矢量控制系统. 其中带有积分作用的反步控制作为矢量系统的速度和电流控制器, 实现给定速度和电流的无静差跟踪; 而滑模变结构模型参考自适应方法作为速度辨识器估计电机速度, 能够快速准确的跟踪实际速度. 通过Lyapunov定理证明了所设计的速度控制器和辨识器的稳定性. 仿真结果验证了所设计的无速度传感器矢量调速系统良好的速度跟踪性能和抗扰动性能.     

基于高阶滑模速度控制器的异步电机模型预测转矩控制

针对三相异步电机驱动系统,提出一种基于高阶滑模速度控制器的模型预测转矩控制策略.为了降低负载扰动对系统运行性能的影响,设计一种基于二阶Super-Twisting滑模技术的速度环控制器,以代替传统PI速度控制器,并应用Lyapunov稳定性理论对其稳定性和鲁棒性进行分析,得到使速度控制系统收敛的参数范围.为了提升转矩控制精度,基于异步电机的数学模型,采用模型预测转矩控制理论,以转矩和定子磁链为控制目标设计评价函数,得到最优输出电压矢量驱动电机运行.仿真结果表明,所提出的控制方法能有效提升系统对负载扰动的鲁棒性,并有效降低转矩波动,使电机具有良好的动态和静态运行性能.     

基于改进型滑模观测器的永磁同步电机无位置传感器控制

永磁同步电机无位置传感器控制技术通常使用滑模观测器观测反电动势,进而获取转子位置和速度信息.为提升滑模观测器的性能,本文设计了一种改进型自适应超螺旋滑模观测器.首先,文章在超螺旋滑模观测器结构中增加观测误差的线性项,以提高观测器的动态性能.接着,为解决观测器增益在不同速域下参数不匹配的问题,本文提出一种观测器参数自适应调整策略,提升了观测器参数鲁棒性.在此基础上,采用同步参考系滤波器滤除输出信号的高次谐波,进一步提高观测精度.最后,仿真结果表明,与传统方法相比,本文提出的方法观测性能更好,鲁棒性更强.     

Adaptive interconnected observer-based backstepping control design for sensorless induction motor

In this paper, an observer-based controller scheme is designed to robustly drive a sensorless Induction Motor (IM) even for the case of low frequencies with unknown load torque. Combining the field oriented control strategy with the backstepping control method, we introduce additional integral terms to improve the robustness properties of the controller in spite of uncertainties and perturbations. The estimation of the fluxes, the speed, the load torque and moreover the stator resistance is given by an adaptive interconnected observer. The practical stability of the proposed observer–controller scheme is proved. Finally, some experimental test results are given on the framework of a specific sensorless induction motor benchmark.     

Hybrid control for speed sensorless induction motor drive

The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results     

采用MRAS速度观测器的异步电机无电压传感器DTC研究

为提高系统的抗干扰能力,降低运行成本,提出无电压传感器控制策略,通过逆变器的导通状态和直流侧电压估算三相定子电压,采用模型参考自适应系统（ MRAS）方法设计速度观测器,基于异步电机数学模型得到转子磁链的两种模型,根据合适的自适应机构得到精确的实际转速,实时调节模型参数,该控制方法简单,保持较高的控制精度。最后通过实验验证了控制策略的可行性。     

Sensorless Inverter‐Fed Compressor Drive System Using Back‐EMF Estimator with PIDNN Torque Observer

A saliency back‐EMF estimator with a proportional–integral–derivative neural network (PIDNN) torque observer is proposed in this study to improve the speed estimating performance of a sensorless interior permanent magnet synchronous motor (IPMSM) drive system for an inverter‐fed compressor. The PIDNN torque observer is proposed to replace the conventional proportional–integral–derivative (PID) torque observer in a saliency back‐EMF estimator to improve the estimating performance of the rotor flux angle and speed. The proposed sensorless control scheme use square‐wave type voltage injection method as the start‐up strategy to achieve sinusoidal starting. When the motor speed gradually increases to a preset speed, the sensorless drive will switch to the conventional saliency back‐EMF estimator using the PID observer or the proposed saliency back‐EMF estimator using the PIDNN observer for medium and high speed control. The theories of the proposed saliency back‐EMF rotor flux angle and speed estimation method are introduced in detail. Moreover, the network structure, the online learning algorithms and the convergence analyses of the PIDNN are discussed. Furthermore, a DSP‐based control system is developed to implement the sensorless inverter‐fed compressor drive system. Finally, some experimental results are given to verify the feasibility of the proposed estimator.     

A New Full‐Order Sliding Mode Observer Based Rotor Speed and Stator Resistance Estimation for Sensorless Vector Controlled Pmsm Drives

Sensorless control of a permanent magnetsynchronous motor (PMSM) at low speed remains a challenging task. In this paper, a sensorless vector control of PMSM using a new structure of a sliding mode observer (SMO) is proposed. To remove the mechanical sensors, a full‐order (FO‐SMO) is built to estimate the rotor position and speed of PMSM drives. The FO‐SMO, which replaces a sign function by a sigmoid function, can reduce the chattering phenomenon. In order to overcome time delay, we cancel the low pass filter. This sensorless speed control shows great sensitivity to stator resistance and system noise. To improve the robustness of sensorless vector control, a full‐order SMO technique has been used for stator resistance estimation. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. The validity of the proposed FO‐SMO with a 1.1 kw low‐speed PMSM sensorless vector control is demonstrated by experiments. In this paper, experimental results for FO‐SMO, back‐EMF SMO and MRAS techniques were obtained with fixed point DSP‐based (TMS320F240).