Torque ripple minimization in PM synchronous motors using iterative learning control

Parasitic torque pulsations exist in permanent magnet synchronous motors (PMSMs) due to nonsinusoidal flux density distribution around the air-gap, errors in current measurements, and variable magnetic reluctance of the air-gap due to stator slots. These torque pulsations vary periodically with rotor position and are reflected as speed ripple, which degrades the PMSM drive performance, particularly at low speeds. Because of the periodic nature of torque ripple, iterative learning control (ILC) is intuitively an excellent choice for torque ripple minimization. In this paper, first we propose an ILC scheme implemented in time domain to reduce periodic torque pulsations. A forgetting factor is introduced in this scheme to increase the robustness of the algorithm against disturbance. However, this limits the extent to which torque pulsations can be suppressed. In order to eliminate this limitation, a modified ILC scheme implemented in frequency domain by means of Fourier series expansion is presented. Experimental evaluations of both proposed schemes are carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed control schemes in reducing torque ripple by a factor of approximately three under various operating conditions.     

Torque-ripple reduction in PM synchronous motor drives using repetitive current control

The paper deals with the torque-ripple reduction in a permanent magnet synchronous motor (PMSM) drive with distorted back electromotive force. A smooth torque is obtained by tracking a modified current reference which is periodic over one-sixth of the electrical time period in the synchronous reference frame. An accurate tracking involves, however, very high current loop bandwidth, which is usually not achievable with conventional linear controllers. In order to improve current tracking in the presence of periodic reference signals and disturbances, the paper proposes the application of repetitive techniques to the current control in a field-oriented PMSM drive, where the q-axis current reference has been modified to achieve constant torque. The paper investigates the advantages and pitfalls of the method, through a mathematical analysis and an experimental validation obtained on a laboratory prototype. Particular emphasis is placed on the adjustments that have been specifically studied to enhance the overall system performance.     

Indirect torque control of switched reluctance motors using iterative learning control

This paper proposes the use of iterative learning control (ILC) in designing a torque controller for switched reluctance motors (SRMs). The demanded motor torque is first distributed among the phases using a torque-sharing function. Following that, the phase torque references are converted to phase current references by a torque-to-current converter and the inner current control loop tracks the phase current references. SRM torque is a highly nonlinear and coupled function of rotor position and phase current. Hence, the phase current references for a given demanded torque can not be obtained analytically. Assumption of linear magnetization characteristics results in an invertible torque function. However, the nominal phase current references obtained using this torque function will lead to some torque error as motor enters into magnetic saturation. For a constant demanded torque, the error in the phase current references will be periodic with rotor position. Hence, we propose to use ILC to add a compensation current to the nominal phase current references so that torque error is eliminated. Similarly, current tracking for the nonlinear and time-varying system is achieved by combining a simple P-type feedback controller with an ILC controller. The proposed scheme uses ILC to augment conventional feedback techniques and hence, has better dynamic performance than a scheme using only ILC. Experimental results of the proposed scheme for an 8/6 pole, 1-hp SRM show very good average as well as instantaneous torque control.     

基于模糊理论的永磁同步电机直接转矩弱磁控制

针对现有永磁同步电机的直接转矩弱磁控制,难以做到实时监测负载条件和参数变化,只能在空载时实现全速运行问题,文中给出一种基于模糊控制理论的弱磁控制算法。该算法基于实时转速和转矩的变化,根据模糊理论智能地调节直接转矩控制中的磁链给定值,从而实现弱磁控制。通过Matlab/Simulink仿真验证了算法的正确性。     

MATLAB/SIMULINK永磁同步电机无传感器矢量控制系统仿真

永磁同步电机矢量控制系统在电动汽车、轮船等交通运输领域具有广泛的应用前景。使用MATLAB/SIMULINK的仿真功能,采用模块化的设计结构,分别对速度环调节、电流PI（Proportion Integration）调节、SVPWM（Space Vector Pulse Width Module）波的产生、dq/αβ、双闭环的整个系统模型进行仿真研究。仿真在线调试,转子转速和转子转角、定子电流、以及转矩通过Scope模块进行观察,及时调整系统模型参数,使系统性能达到最佳化,实现了永磁同步电机矢量控制和正反转调速。结果表明该种控制方法具有很好的鲁棒性,且该种方法可以提高设计的效率并缩短系统设计时间。     

基于TMS320F2806的感应电机控制系统

设计了一种基于TMS320F2806无速度传感器的感应电机矢量控制系统,采用转子磁场定向,实现磁链与转矩的解耦控制,利用PI自适应控制原理控制速度和电流,通过磁通估算模块计算磁链和磁通角,然后通过开环速度估计模块估算转子的角速度。实验结果表明,该系统设计简单实用,性能良好。     

基于SVPWM的异步发电机模糊直接转矩控制系统仿真

为了解决电机传统直接转矩控制方法中存在转矩和磁链脉动大的问题,本文在研究异步发电机、正弦脉宽调制（SVPWM）、及直接转矩控制算法的基础上,提出了改进型异步发电机直接转矩控制算法。利用Matlab/Simulink仿真平台搭建了异步发电机直接转矩控制算法系统仿真模型,包括电机模块、测量模块、逆变模块、速度和转矩计算调节模块等,并进行系统的仿真分析。从结果可知,与传统异步发电机直接转矩控制算法相比较,改进型直接转矩控制系统控制精度高明显提高、响应速度得到改善、鲁棒特性好,证明了该控制算法的有效性与可行性。     

Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing

A quadratic-optimal iterative learning control (ILC) method has been designed and implemented on an experimental rapid thermal processing system used for fabricating 8-in silicon wafers. The controller was designed to control the wafer temperatures at three separate locations by manipulating the power inputs to three groups of tungsten-halogen lamps. The controller design was done based on a time-varying linear state-space model, which was identified using experimental input-output data obtained at two different temperatures. When initialized with the input profiles produced by multiloop PI controllers, the ILC controller was seen to be capable of improving the control performance significantly with repeating runs. In a series of experiments with wafers on which thermocouples are glued, the ILC controller, over the course of ten runs, gradually steered the wafer temperatures very close to the respective reference trajectories despite significant disturbances and model errors.     

Analysis of direct torque control in permanent magnet synchronousmotor drives

This paper describes an investigation of direct torque control (DTC) for permanent magnet synchronous motor (PMSM) drives. It is mathematically proven that the increase of electromagnetic torque in a permanent magnet motor is proportional to the increase of the angle between the stator and rotor flux linkages, and, therefore, the fast torque response can be obtained by adjusting the rotating speed of the stator flux linkage as fast as possible. It is also shown that the zero voltage vectors should not be used, and stator flux linkage should be kept moving with respect to the rotor flux linkage all the time. The implementation of DTC in the permanent magnet motor is discussed, and it is found that for DTC using available digital signal processors (DSPs), it is advantageous to have a motor with a high ratio of the rated stator flux linkage to stator voltage. The simulation results verify the proposed control and also show that the torque response under DTC is much faster than the one under current control     

Direct torque control of brushless DC motor with nonidealtrapezoidal back EMF

In this paper a method of the torque control attenuating the undesired torque pulsation for brushless DC motor with nonideal trapezoidal back EMF is presented. It is the direct torque control method in which the applied output voltage is calculated from the reference torque and the torque of the previous step in the two-phase conducting period and in the commutation period considering the back EMF waveform. The time delay due to the calculation is compensated by the one step ahead current prediction. To measure the instantaneous torque ripple, a torque observer is constructed using a high precision encoder of 50000 pulse per revolution. The simulation and experimental results show that the proposed method reduces the torque ripple significantly and that it keeps the torque control dynamics as well     

基于MRAS的永磁同步电机直接转矩控制系统的研究

研究了一种基于模型参考自适应无速度传感器的永磁同步电机直接转矩控制系统:将永磁同步电机的磁链模型作为参考模型,估算的定子磁链模型作为可调模型,设计了自适应定律对电机的转速与定子电阻同时进行跟踪辨识,使用空间电压矢量调制技术组成了永磁同步电机无速度传感器直接转矩控制系统。仿真实验结果表明该系统获得了近似圆形的定子磁链,在转速与转矩变化时均能准确的估算出电机转速,具有良好的动、静态性能。     

Industrial robot accurate trajectory generation by nested loop iterative learning control

Common error sources of industrial robot manipulators include joint servoing error, imprecise kinematics, mechanical compliance, and transmission error. In this work we present a nested loop iterative learning control (ILC) feedforward structure: an inner loop that compensates for motor dynamics, and an outer loop that corrects the deviation along the path tracked, that features practically efficient implementation. Taking advantage of industrial robot’s speed reduction transmission, single-input-single-output method is demonstrated effective for the nonlinear coupled robot dynamics. Data-based inversion technique that incorporates motion constraint is used for fast inner loop convergence. The outer loop utilizes inverse Jacobian matrix for joint reference modification. For nonlinear static friction that is difficult to be compensated for with only joint command, notch filtering is utilized in the learning process to avoid exciting vibration inherently exists in the robot. The proposed nested loop ILC requires only the nominal kinematic parameters from the robot manufacturer, and can be readily implemented without modifying the existing robot controllers. The effectiveness of the proposed method is experimentally verified on a six degree-of-freedom robot manipulator.     

无刷直流电机系统的仿真研究

根据无刷直流电机系统的结构,在无刷直流电机数学模型的基础上,利用Matlab/Simulink建立无刷直流电机系统的仿真模型,电机系统模型包括电机本体模块、逆变电路模块、转矩速度计算模块、逻辑换相模块和控制模块。介绍了串级PI闭环控制,给出了仿真结果及其分析,论证了系统仿真模型的正确性。     

基于FPGA的直接转矩模糊控制技术研究

直接转矩控制技术是一种具有高静、动态性能的交流调速方法。本文以FPGA芯片为控制核心设计了感应电机直接转矩控制系统,用VHDL语言编写了系统的模糊控制软件.对软件中3/2变换模块、磁链和转矩估算模块、M/T测速模块、PI模块、模糊控制与启动模块、PWM模块进行详细的研究。然后通过实验初步验证了本文设计的基于模糊控制技术的感应电机直接转矩控制系统具有优良的动态和稳态性。     

A novel technique for estimation and control of stator flux of a salient-pole PMSM in DTC method based on MTPF

A permanent-magnet synchronous machine (PMSM) can be controlled using the direct torque control (DTC) technique in three different ways, i.e., by controlling flux, reactive torque and rotor d-axis current. Frequently, the DTC technique controls the speed of the motor by controlling stator flux with the aim of obtaining an optimal torque. A varying flux, proportional to the torque, may be used instead of a fixed flux, resulting in a maximum torque per ampere or maximum torque per flux (MTPF). In this paper, a reference-flux-generating method is followed to achieve the MTPF. An approximate equation is then derived using numerical techniques in order to obtain the reference flux from the torque. This equation is then applied to the DTC control system in order to obtain the reference flux. The control scheme has been verified by simulation and tests on a salient-pole permanent-magnet synchronous motor.     

Speed tracking control of a permanent-magnet synchronous motor withstate and load torque observer

This paper is concerned with the speed tracking control problem for a permanent-magnet synchronous motor (PMSM) in the presence of an unknown load torque disturbance. After a brief review of the mathematical model of the PMSM, a speed tracking control law using the exact linearization methodology is introduced. The tracking control algorithm is completed by adding an extended observer which provides, on the one hand, the motor speed and acceleration and, on the other hand, estimates the unknown load torque. The stability of the closed-loop system composed of a nonlinear speed tracking controller and an observer is studied by the way of Lyapunov theory. Furthermore, the decoupling of the state observer and the load torque observer is discussed. Finally, a real-time implementation and the experimental results of the proposed control strategy are presented     

基于模糊控制的凸极永磁同步电动机最大转矩／电流比控制策略系统仿真

永磁同步电机以其自身特殊的结构，较其他类型的电动机具有很多方面的优势，如：功率密度高，转子惯性低等，永磁同步电机可分为凸极和隐极式电机，结构不同其控制方法又存不同。本文针对凸极式永磁同步电机的特点，分析了最大转矩／电流比控制策略，并在调速系统中应用了模糊控制和PI控制相结合的转速调节器，仿真结果表明，这种控制疗法较传统的id=0的PI控制策略的有较大的改进。     

基于ESO磁链观测器的直接转矩控制的研究

讨论直接转矩控制方法在永磁同步电机中的应用问题,利用MATLAB仿真工具对永磁同步电机直接转矩控制系统仿真。针对直接转矩控制低速时存在较大转矩脉动的问题,采用观测器方法克服传统磁链模型中参数的不确定性对磁链观测精度的影响,保证全速范围内磁链的准确估计,提高控制性能。仿真表明观测磁链具有较高的精度,对电阻的不确定变化具有较强的鲁棒性,并可准确估计无速度传感器的速度及位置。     

Development of Robust Current 2-DOF Controllers for a Permanent Magnet Synchronous Motor Drive With Reaction Wheel Load

This paper develops robust 2-DOF current and torque control schemes for a permanent magnet synchronous motor (PMSM) drive with satellite reaction wheel load. A DSP-based experimental PMSM-driven reaction wheel system is established, and the key motor parameters are estimated for realizing the proposed control schemes. In the proposed current control schemes, the traditional 2-DOF controller is augmented with an internal model feedback resonant controller or a robust tracking error cancellation controller (RECC). Comparative performance and error analyses of these two proposed control schemes are given. Accordingly, an improved robust 2-DOF current control scheme combining the resonant controller and the RECC is further proposed. The resonant controller enhances the transient and steady-state tracking of the sinusoidal current, simultaneously rejecting the back electromotive force. A similar robust tracking control for the observed torque can be designed, which exhibits quick transient response. Effectiveness of the proposed controls and the driving performance of the whole reaction wheel are evaluated experimentally.      

Massively parallel classification of single-trial EEG signals using a min-max modular neural network

This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed. Second, the two-class subproblems are simply learned by individual smaller network modules in parallel. Finally, all the individual trained network modules are integrated into a hierarchical, parallel, and modular classifier according to two module combination laws. To demonstrate the effectiveness of the method, we perform simulations on fifteen different four-class EEG classification tasks, each of which consists of 1491 training and 636 test data. These EEG classification tasks were created using a set of non-averaged, single-trial hippocampal EEG signals recorded from rats; the features of the EEG signals are extracted using wavelet transform techniques. The experimental results indicate that the proposed method has several attractive features. 1) The method is appreciably faster than the existing approach that is based on conventional multilayer perceptrons. 2) Complete learning of complex EEG classification problems can be easily realized, and better generalization performance can be achieved. 3) The method scales up to large-scale, complex EEG classification problems.