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1、附录B:Artificial intelligence applications in Permanent Magnet Brushless DC motor drivesR. A. Gupta Rajesh Kumar Ajay Kumar BansalPublished online: 25 December 209 Springer Science Business Media B .V. 2009Abstract Permanent Magnet Brushless DC (PMBLDC) machines are more popular due its simple structu
2、re and low cost. Improvements in permanent magnetic materials and power electronic devices have resulted in reliable, cost effective PMBLDC drives, for many applications. Advances in artificial intelligent applications like neural network, fuzzy logic, Genetic algorithm etc. have made tremendous imp
3、act on electric motor drives. The brushless DC motor is a multivariable and non-linear system. In conventional PMBLDC drives speed and position sensing of brushless DC motors require high degree of accuracy. Unfortunately, traditional methods of control require detailed modelling of all the motor pa
4、rameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic inputoutput relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Va
5、rious AI techniques for PMBLDC motor drive sare described. Attempt is made to provide a guideline and quick reference for the researchers and practicing engineers those are working in the area of PMBLDC motor drives.Keywords PMBLDC Artificial intelligent Intelligent control Fuzzy Neural network1 Int
6、roductionThe permanent magnet (PM) brushless DC (BLDC) machine is increasingly being used for various applications and its market is rapidly growing. This is mainly due to its high torque, compactness, and high efficiency. Permanent magnet brushless motors have found wider applications due to their
7、high power density and ease of control. Advances in high-energy Permanent Magnet materials and power electronics have widely enhanced the applications of PMBLDC in variable speed drives similar to ac machines (Singh and Kumar 2002; Bose 1992). Recently, the PMBLDC motor has evolved as a replacement
8、of the standard brush type dc machine in many servo applications due to its high efficiency, low maintenance and good controllability (Mohan et al. 1995). Several models of this drive have been presented and discussed (Putta Swamy e t a l. 1995).Moreover, PMBLDC motors are a type of synchronous moto
9、rs means that the magnetic fields generated by both the stator and the rotor have the same frequency therefore, PMBLDC motors do not experience the “ slip” that is normally seen in induction motors (Hendershot and Miller 1994 ). The research is going on to identification of a suitable speed controll
10、er for the PMBLDC motor. Many control strategies have been proposed (Kaynak 2001; Miller 1989) in classical linear theory. As the PMBLDC machine h as nonlinear model, the linear PID may no longer be suitable. This has resulted in the increased demand for modern nonlinear control structures like self
11、-tuning controllers, state-feedback controllers, model reference adaptive systems and use of multi-variable control structure. Most of these controllers use mathematical models and are sensitive to parametric variations. Very few adaptive controllers have been practically employed in the control of
12、electric drives due to their complexity and inferior performance.The design of current and speed controllers for permanent magnet brushless DC(PMBLDC) motor drive remains to large extent a mystery in the motor drives field. A precise speed control of PMBLDC motor is complex due to nonlinear coupling
13、 between winding currents and rotor speed as well as nonlinearity present in the developed torque due to magnetic saturation of the rotor.The PMBLDC machines can be categorized based on the permanent magnets mounting and shape of the back-EMF. The permanent magnets can be surface mounted on the roto
14、r or installed inside of the rotor (interior permanent magnet), and the back-EMF shape can either be sinusoidal or trapezoidal. The surface mounted PM (SMPM) machine is easy to build. Also, from the machine design point of view, skewed poles can be easily magnetized on this round rotor to minimize c
15、ogging torque. Typically, for this type of motor, the inductance variation by rotor position is negligibly small since there is no magnetic saliency. The interior permanent magnet (IPM) machine is a good candidate for high-speed and traction applications. It is noted that there is an inductance vari
16、ation by rotor position for this type of motor because of the magnetic saliency.This paper will give bigger focus on the artificial intelligent applications to PMBLDC motor drives. In this paper, conventional and recent advancement of AI operation methods for P M BLDC drives are presented.2 Modellin
17、g of PMBLDC motorThe PMBLDC motor is modelled in the stationary reference frame using 3 -phase abc variables (Pillay and Krishnan 1989). The general volt-ampere equation be expressed as:where R , L , M are the resistance, inductance and mutual inductance of stator windings and ， are phase voltage, b
18、ack-EMF voltage and phase current of each phase of stator respectively. The electromagnetic torque is expressed asFig. 1 Three phase back EMF functionThe interaction of with the load torque determines how the motor speed builds up: where is load torque in N -m, B is the frictional coefficient in N -
19、ms/ rad, and J is the moment of inertia, kg-. The per phase back emf in the PMBLDC motor is trapezoidal in nature and are the functions of the speed and rotor position angle ( r). The normalized functions of back emfs are shown in Fig. 1. From this, the phase back emf can be expressed as: Where and
20、can be described by E and normalized back emf function shown in Fig. 1. . The back emf function of other two phases and are defined in similar way using E and the normalized back emf function and as shown in F ig. 1.3 .Artificial intelligenceHuman abilities in controlling the complex systems, has en
21、couraged scientists to pattern from human neural network and decision making systems. Firstly there searches began in two separate fields and resulted in establishment of the fuzzy systems and artificial neural networks (Giridharan e t a l. 2006). There are primarily three concepts prevailing over t
22、he intelligent control: Fuzzy logic control Neural network based control Neuro fuzzy control (hybrid control)In the first concept, the controller is represented as a set of rules, which accepts/gives the inputs/outputs in the form of linguistic variables. The main advantages of such a controller are
23、:Fig. 2 PMBLDC motor AI controllers scheme(1) Approximate knowledge of plant is required(2) Knowledge representation and inference is simple.(3) Implementation is fairly easy.The artificial intelligence mainly has two functions in PMBLDC motor drivesa. Artificial intelligence controlAs controllerb.
24、Sensorless operationsfor variable estimationIn these the conventional controllers like PI，PID etc. are replaced or combined with AI controllers. All artificial-intelligence-based control strategies, such as fuzzy logic control, neural network control, neurofuzzy control, and genetic control, are cla
25、ssified as artificial intelligent control (AIC). Among them, the fuzzy logic control and the neural network control are most mature and attractive for the PMBLDC drives since they can effectively handle the systems nonlinearities and sensitivities to parameter variations (Fig. 2).附录C 中文译文基于人工智能在永磁无刷
26、直流电机驱动中的应用摘要由于其结构简单和低成本的原因，永磁无刷直流电机越来越受到青睐。永磁材料的进步和电力电子器件的可靠性能，使得永磁无刷直流电机成本更加低廉，有了更多方面的应用。人工智能应用的进步如神经网络算法、模糊逻辑算法、遗传算法等对电机驱动器产生了巨大的影响。无刷直流电机是一个多变量、非线性系统。在传统的永磁无刷直流电机中驱动器的速度和在无刷直流电机中的位置检测都需要很高的精度。不幸的是,传统的控制方法需要详细的所有电动机参数建模来实现这个。智能控制技术、模糊逻辑控制、神经网络控制等使用启发式的输入-输出关系来处理模糊和复杂的情况。本文是一篇将智能控制技术用于永磁无刷直流电机驱动的调查
27、文献。各种人工智能技术对永磁无刷直流电机驱动描述。试着为那些工作在永磁无刷直流电机领域的执行工程师和研究员们提供一种指导和有效地参考。关键词：永磁无刷直流电机，人工智能，智能控制，模糊控制，神经网络 1.介绍永磁无刷直流电机越来越多的被应用于各种应用场合并且它的市场也在快速地增长。这主要是由于其高转矩、紧密度、效率高。永磁无刷直流电机由于其功率密度大并易于控制被广泛投入到使用中。高性能永磁材料和电力电子器件的进步已经广泛的应用在提高永磁无刷直流电机中的变速驱动器中类似于交流电机(辛格和库马尔2002;玻色1992)。最近,永磁无刷直流电动机已逐渐成为替代标准的刷式直流电机，由于其效率高、维护成
28、本低和良好的可控性(Mohan et al . 1995) 在许多伺服应用中都被用到。已经提出几个模型的驱动并为之讨论(PuttaSwamy e t1995)。此外,永磁无刷直流电机是一种同步电机，也就是说由定子和转子所产生的磁场都有相同的频率。因此, 永磁无刷直流电机不会出现通常出现在感应电动机 “滑”的这种现象, (Hendershot和米伊勒河1994)。该项研究是为永磁无刷直流电机确定一个合适的速度控制器。在经典的线性理论中人们提出了许多控制策略 (2001;1989年Kaynak Miller)。随着永磁无刷直流电机作为非线性模型、线性PID可能不再适和当前的对象。这已经导致现代非线
29、性自整定控制器，状态反馈控制器，模型参考自适应系统和多变量控制结构中使用的入耳式控制结构需求的增加。大多数这些控制器使用的数学模型和运行参数的变化都很敏感。由于其复杂的密度和低质的性能几乎很少的自适应控制器用于控制电动驱动器。永磁无刷直流电（PMBLDC）机驱动电流及速度控制器的设计上在电机驱动领域中很大程度上仍然是一个谜。对于永磁无刷直流电机来说精确的速度控制是复杂的，由于绕组之间的电流和转子速度，以及由于磁饱和转子的转矩中存在非线性的非线性耦合。永磁无刷直流电机的分类可以基于永磁体安装和反电动势的波形来区分。永久磁铁可以安装在转子的表面或安装在转子的内部(内置式永磁)，反电动势的形状可以是
30、正弦波或是梯形波。表面安装PM(SMPM)机器很容易建立。同时,从机械设计的角度来看,倾斜磁极可以很容易在圆形磁极上磁化，以尽量减少齿槽转矩。通常,对于这种类型的电动机而言, 由转子位置的的电感距离变化，是小到可以忽略不计，因为没有磁性的显着性。内部永磁（IPM）的机器，是高速和牵引应用的一个很好的备用。值得注意的是，因为这种类型的磁凸极转子位置的变化它又是电感电机。本文将会在人工智能应用于永磁无刷直流电机驱动器上给予更多的关注。在本文中,会涉及到对于人工智能在无刷直流电机驱动器中传统操作方法和常规操作方法的最新进展。2.永磁无刷直流电机的建模永磁无刷直流电机的建模仿照静止参考系，采用3相ab
31、c变量(皮莱和Krishnan 1989)一般的伏安方程表示为: 式中，R，L，M是定子绕组的电阻，电感和互感，Vx是相电压，ex ，ix 分别是反电动势的电压和各相定子的相电流。电磁转矩表示为：图1 三相反电动势方程.电磁转矩和负载转矩的相互作用决定了电机速度的加快：可知：负载转矩的单位N - m,B代表摩擦系数，单位是N - ms / rad,J代表惯性矩,单位是kg -。永磁无刷直流电机的每相的反电动势都是梯形波图，电机的速度和转子位置角（R）的功能反向电动势的归一化函数示于图1，相位反电动势ean可以表示为： 3.人工智能人类在控制复杂系统的能力,鼓励科学家从人类神经网络和决策系统。首
32、先在两个单独的字段搜索开始,建立模糊系统和人工神经网络(Giridharan e t一个l。2006)。在智能控制中主要有三个概念盛行:模糊逻辑控制神经网络控制神经模糊控制(混合控制)在第一个概念,控制器都被表示为一组规则,接受/给出了输入/输出语言变量的形式。这样一个控制器的主要优点:图2 永磁无刷直流电机人工智能控制方案(1)近似知识的植物是必需的(2)知识表示和推理是简单的。(3)实现是相当容易的。人工智能主要有两个功能在永磁无刷直流电机驱动器中：A.人工智能控制的控制器B.无传感器操作变量估计在这些传统的控制器如PI、PID等替换或结合人工智能控制器。所有基于人工智能控制策略,如模糊逻辑控制、神经网络控制、神经模糊控制和遗传控制,分为人工智能控制(AIC)。其中,模糊逻辑控制和神经鞘、网络控制是最成熟的和有吸引力的驱动器,因为它们可以有效处理系统的非线性和参数的敏感变化。为了实现高性能永磁无刷直流电机驱动、位置反馈几乎是强制性的。为了摆脱昂贵和笨重的位置编码器,无位置传感器控制(SC)变得有吸引力。有各种各样的SC技术可分为动态EMF,电感变化和磁链变化。基本上,位置信息是由在线分析得到的电压和在机内绕组流过的电流。
