基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

改进型MRAS无速度传感器的无轴承异步电机矢量控制

无轴承异步电机(BIM)的转子磁链电压模型中含有纯积分环节,其积分初值和累计误差会影响磁链观测精度,进而使转速估计产生严重失真.为了实现BIM无速度传感器运行,本文借鉴模型参考自适应法(MRAS)基本结构,将改进二阶广义积分器与锁频环结合以代替原有纯积分器,提出了一种新的基于MRAS的BIM无速度传感器控制方法,构建了BIM转子磁链定向无速度传感器矢量控制系统.并且,基于MATLAB/Simulink的仿真验证和基于dSPACE的实验结果表明:与传统电压模型观测方法相比,所提出的转子磁链电压模型有效避免了纯积分环节带来的直流偏移和积分初值影响,有着更好的观测效果.同时,基于无轴承异步电机转子磁链定向无速度传感器矢量控制系统,电机能稳定悬浮运行,估算转速和实测转速具有很好的一致性.     

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

In this paper, an adaptive neural network sensorless control scheme is introduced for permanent magnet synchronous machines (PMSMs). The control strategy consists of an adaptive speed controller that capitalizes on the machine’s inverse model to achieve accurate tracking, two artificial neural networks (ANNs) for currents control, and an ANN-based observer for speed estimation to overcome the drawback associated with the use of mechanical sensors while the rotor position is obtained by the estimated rotor speed direct integration to reduce the effect of the system noise. A Lyapunov stability-based ANN learning technique is also proposed to insure the ANNs’ convergence and stability. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer, or mechanical parameters knowledge is required. Results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions.     

基于DSP的自适应速度辨识直接转矩控制系统研究

异步电机直接转矩控制能产生快速且良好的鲁棒性响应,采用自适应磁链观测器,取代传统的积分器,构造了新型的速度估计器,并结合模糊控制器,实现对定子磁链准确观测和系统无速度传感器运行状态。基于DSP(TMS320LF2407A)核心芯片建立数字化控制系统。仿真与实验表明,该系统对电机定子磁链的观测精度高,转速估算准确,尤其在低速下能保持很高的性能。     

基于无功功率的感应电机转速自适应辨识

在感应电机直接转矩控制的基础上,提出了基于无功功率的转速自适应辨识模型,并运用MATLAB/SIMULINK工具进行仿真,仿真结果证明,该速度辨识器具有较好的辨识效果。     

A new scheme for sensorless induction motor control drives operating in low speed region

A novel simple stator resistance estimation technique for high-performance induction motor drives is proposed. It makes use of a synchronously revolving reference frame aligned with the stator current vector, so that the resistance can be straightforwardly derived from the mathematical model of the induction motor. A sensorless direct field orientation scheme is employed to validate the proposed solution, with the drive operating in the critical area of low speeds. A combination of two observers is used: a Kalman filter observer to estimate the rotor flux, and a MRAS observer for speed estimation. The stator resistance estimator alleviates the usual performance degradation of MRAS-based drives at low speeds, caused by the thermal drift of stator resistance. Computer simulations, including realistic disturbances, show high effectiveness of the described approach.     

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。     

基于模型参考自适应的速度估计方法

为解决无速度传感器感应电动机矢量控制系统的速度估计问题,以模型参考自适应的理论为基础,利用感应电压矢量进行转速估计并进行仿真,转速估计结果理想,仿真实验所得到的波形与理论分析结果是一致的。并且使相应的无速度传感器矢量控制系统具有良好的静、动态性能。证明了采用模型参考自适应系统,利用感应电压矢量进行转速估计方法的正确性和可行性。     

TLS EXIN based neural sensorless control of a high dynamic PMSM

Sensorless vector control of the Permanent Magnet Synchronous Motors (PMSMs) has been a very challenging subject for many years. In general, the absence of the encoder in the drive permits to obtain high dynamical performance by exploiting increased reliability and also reduced cost. Among the different methodologies proposed in the literature, a model-based approach has been proposed here. In particular, the space-vector equations of the PMSM have been re-elaborated in a matrix form to permit the use of a Least Squares technique for the estimation of the speed of the PMSM. The problem has been then faced-up with the so-called TLS EXIN neuron, which is the only linear neural network able to solve the TLS problem on-line in a recursive form. Experimental tests have been performed on an experimental test set-up based on a fractional horsepower permanent magnet machine.     

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。     

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).     

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     

Multiphase rotor current observers for current predictive control: A five-phase case study

The use of multiphase drives has gained importance in recent times due to some advantages that they provide over conventional three-phase ones. High performance stator current control can be achieved by means of direct command of voltage source inverter. In this context finite-state model predictive control is a very flexible strategy that has been recently proposed and analyzed. Nevertheless, its implementation must solve the problem of estimating rotor quantities, being the conventional solution a simple backtracking procedure. In this respect, observers appear as an attractive alternative. However, while they have been used with FOC, sensorless drives and for fault detection, they have not been used yet for predictive control of drives as a way to provide rotor values estimates. In this paper the authors propose to incorporate a full-order rotor current observer in a finite-state model predictive controller of a five-phase induction machine. Pole placement design based on Butterworth filters is used. The new estimation scheme and the standard procedure are compared. By means of experimental tests, the differences between both approaches and the benefits of including a rotor observer are illustrated and verified.     

基于滤波补偿和自适应观测的直接转矩控制

直接转矩控制(DTC)是继矢量控制技术后发展起来的一种新型的交流调速控制方法,但其在低速运行时存在较大的电流、转矩脉动,为了改善直接转矩控制系统的低速性能,提出一种新型的定子磁链低通滤波补偿和转速自适应观测相结合的直接转矩控制方法.该方法提高了定子磁链和转子转速的观测精度,改善了直接转矩控制系统的动静态性能.仿真和实验...     

基于神经网络可调模型的直接转矩控制系统速度辩识

基于无速度传感器辨识精度和动态性能的提高,在传统的模型参考自适应(MRAS)速度辨识模型的基础上,参考模型采用转子磁链电压模型。应用神经网络理论,对其可调模型进行了改进。并在无速度传感器直接转矩控制系统中对该速度辨识模型进行了研究,仿真结果验证了该速度辨识模型具有满意的辨识精度和动态性能。     

Grid-connected vector-controlled slip-ring induction machine drive with out speed sensor

A speed estimation method is presented in this paper for a grid-connected doubly-fed slip-ring induction machine drive. The proposed method is formulated with reactive power based model reference adaptive system (MRAS). The method does not require the estimation of stator/rotor flux. So, the integrator related problems at synchronous speed are overcome. Also, the estimation method is independent of stator and rotor resistance variation. Extensive simulation results are presented to validate the technique.     

Elimination of the real positivity condition in the design of parallel MRAS

All the algorithms developed hitherto in order to obtain a global asymptotically stable "parallel" MRAS (model reference adaptive system) require that a certain transfer function should be made strictly positive real. This condition is restrictive in the case of parallel MRAS used as identifiers or as adaptive state observers, because part of the unknown parameters of the plant to be identified appear in the transfer function, which should be made strictly positive real. A new algorithm is proposed in which the parameters of the compensator acting on the generalized error are no more constant but are adapted recursively. The new adaptation algorithm assures the global asymptotic stability of the adaptive system, without requiring the satisfaction of a positive-real condition. Simulation results illustrate that the proposed algorithm provides excellent results when used for identification in the presence of measurement noise. The design proposed can be extended also for adaptive state observers and adaptive model-following control systems.     

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.     

一种改进的无速度传感器感应电机直接转矩控制

直接转矩控制(DTC)对于异步电机而言，它能产生快速及好的鲁棒性响应。介绍了一种新的对于无速度感应电动机基于空间矢量(SVM)模型的直接力矩和磁链控制，它能降低稳态时力矩、磁链和速度的波动。在全速度范围无速度传感应用中将产生更好的稳态性能的同时保留DTC的暂态优点。仿真结果显示该方法有着比传统的DTC更好的性能。     

基于遗传算法优化的BP网络速度观测器的研究

根据三相异步电机的数学模型,提出了一种基于智能算法优化的速度观测器,以实现无速度传感器在直接转矩控制系统中的速度闭环控制。在通过BP神经网络训练得到的DTC系统的速度观测器的基础上,针对BP神经网络寻优参数多、易陷于局部极值以及初始设置对训练结果影响大等的不足,采用遗传算法对其进行优化设计。由仿真结果可知,用遗传算法优化后的BP神经网络较单纯的BP神经网络速度观测器具有更高的精度。