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where R , L , M are the resistance, inductance and mutual inductance of stator windings and ，， are phase voltage, back-EMF voltage and phase current of each phase of stator respectively. The electromagnetic torque is expressed as
Fig. 1 Three phase back EMF function
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The interaction of with the load torque determines N -m, B is the frictional coefficient in N -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 (θ r). The normalized functions of back emfs are shown in Fig. 1. From this, the phase back emf can be expressed as:
ean?6E??????????E ???
ean?6E???????2???E ???Where and 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 intelligence
Human abilities in controlling the complex systems, from 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 the intelligent control: ? Fuzzy logic control
? Neural network based control
? Neuro fuzzy control ( the first concept, the controller is represented as a set of rules, which acceptsgives the inputsoutputs in the form of linguistic variables. The main advantages of such a controller are:
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 PMBLDC motor drives a. Artificial intelligence control—As controller b. Sensorless operations—for variable estimation
In 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 classified 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 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abc变量(皮莱和Krishnan 1989)一般的伏安方程表示为:
00??ia??ea??Va??R00??ia??L?Md?????V???0R0???i???0?L?M0i?ebbb????????dt???b??0L?M??Vc????00R????ic????0???ic????ec?? 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
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电磁转矩和负载转矩的相互作用决定了电机速度的加快：