基于人工智能在永磁无刷直流电机驱动中的应用 毕业论文外文翻译

附录B:Artificial intelligence applications in Permanent Magnet Brushless DCmotor 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 structure 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 impact 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 parameters to achieve this. The Intelligent control techniques like, fuzzy logic control/Neural network control etc. uses heuristic input–output relations to deal with vague and complex situations. This paper presents a literature survey on the intelligent control techniques for PMBLDC motor drives. Various 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 IntroductionThe 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 brushlessmotors have found wider applications due to their 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 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 motors means that the magnetic fields generated by both the stator and the rotor have the same frequency therefore, PMBLDC motors do not exper ience the “ slip” that is norm ally seen in induction motors (Hendershot and Miller 1994 ). The research is going on to identification of a suitable speed controller 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-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 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 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 rotor or installed inside of the rotor (interior permanent magnet), and the back-EMF shape can either be sinusoidal or trapezoidal. The surface mountedPM (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 cogging 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 variation 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 Modelling 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:⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡+⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡---+⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡⨯⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡=⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡c b a c b c b a c b e e e i i i dt d M L M L M L i i i R R R V V a a 000000000000Vwhere R , L , M are the resistance, inductance and mutual inductance of stator windings and x V ，x e ，x i are phase voltage, back-EMF voltage and phase current of each phase of stator respectively. The electromagnetic torque is expressed asFig. 1 Three phase back EMF function[]c cn b bn a an r e i e i e i e 1T ++=ωThe interaction of e T with the load torque determines how the motor speed builds up:dtd J B r r L ωω++=T Te where is L T load torque in 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 spee d and rotor position angle (θ r ). The normalized functions of back emfs are shown in Fig. 1. From this, the phase back emfan e can be expressedas: E e an = o r o 1200<<θ()E E e an --⎪⎭⎫ ⎝⎛=θππ6 o r o 180120<<θ E e an -= o r o 300180<<θ()E E e an +-⎪⎭⎫ ⎝⎛=πθπ26 o r o 360300<<θWhereωb k E =and an e can be described by E and normalized back emf function ()r a f θshown in Fig. 1. ()r a an Ef e θ= . The back emf function of other two phases bn e and cn e are defined in similar way using E and thenormalized back emf function()r f θb and ()r c θf as shown in F ig. 1.3 .Artificial intelligenceHuman abilities in controlling the complex systems, has encouraged 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 the 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: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 control—As controllerb. Sensorless operations—for 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 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 handle the system’s 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。