Implementation of Artificial Neural Network-Based Tracking Controller for High-Performance Stepper Motor Drives

Two distinct multilayer perception neural networks (NNs) are implemented via laboratory experiment to simultaneously identify and adaptively control the trajectory tracking of a hybrid step motor assumed to operate in a high-performance drives environment. That is, a neural network identifier (NNI) which captures the nonlinear dynamics of the stepper motor drive system (SMDS) over any arbitrary time interval in its range of operation, and a neural network controller (NNC) to provide the necessary control actions as to achieve trajectory tracking of the rotor speed. The exact form of the control law is unknown, and must be estimated by the NNC. Consequently, the NNC is constructed as a nonlinear unknown function depending on the current state of the drive system supplies by the NNI and the reference trajectory we wish the outputs to follow. The two NNs are online trained using dynamic back-propagation algorithm. The composite structure is used as a speed controller for the SMDS. Performance of the composite controller is evaluated through a laboratory experiment. Experimental results show the effectiveness of this approach, and demonstrate the usefulness of the proposed controller in high-performance drives     

Comparison of universal field oriented (UFO) controllers indifferent reference frames

The principle of the universal field oriented (UFO) controller, operating in an arbitrary reference frame, is applied to a CRPWM inverter. Due to its high degree of generality,the UFO controller is compatible with rotor, stator, and air-gap flux field oriented control. The authors compare the stability regions and the steady state performance of these three types of controllers applied to the same machine. The rotor flux based controller has been selected as the basic reference. Digital simulation results are carried out to show that the performance of the complete UFO system, i.e., combined direct and indirect UFO, in a nonrotor flux based reference frame leads to a robust high performance drive with no dependency on machine parameters in steady state     

Multilayer control of an induction motor drive:A strategic step for automotive applications

Fault tolerance is a critical attribute in automotive electrical and propulsion systems. In this paper, a control scheme is presented that allows an induction motor drive system to operate in the event of multiple sensor failures. Automatic diagnosis of sensor fault and recovery is performed and used to reconfigure the drive system controls to achieve the best performance in lieu of component degradation. This approach couples a new digital delta-hysteresis regulation scheme with a model reference adaptive system scheme in order to provide fault tolerance for both phase-current and rotor position (speed) sensors. Simulation and experimental results are provided to show the effectiveness of the proposed scheme.     

Toward unique identifiers

This paper discusses the creation and use of unique identifiers for intellectual property. General concepts applicable to unique identifiers are defined and discussed [identifier, digital object, dumb and intelligent identifiers, readability, affordance or computability, multiple identification, resolution, metadata, persistence, granularity, derivatives (e.g., versions, formats, manifestations, and copies), check digits, and intermediate objects]. Requirements for unique identifiers are reviewed. Capacity issues for an identifier scheme and business issues (cost, antitrust considerations, and intellectual property rights) are explored. Technical and administrative issues of identifiers are discussed, with particular reference to the uses of identifiers, which necessitates intelligence within a system of unique identifiers (scope, protocol independence, multiple roles, fungibility, persistence, standards, and emerging structural metadata approaches). Two brief illustrations of failure in unique identifiers are given. The role of unique identifiers on the Internet is discussed with explanation of the architecture of uniform resource addressing, specifying the resource from a uniform resource name (URN), names and addresses, URN implementations, and a future digital object infrastructure. Brief examples of unique information identifier systems in music, text, and digital objects in general are discussed. Recommendations are made for actions to assist in the development of future identifiers     

基于MRAS无速度传感器矢量控制系统研究

根据异步电动机矢量控制基本原理,构建了基于转子磁链定向的模型参考自适应系统,并针对其基准模型易受积分初值和漂移问题的影响,提出了一种改进的速度辨识方法。该方法由改进的电压参考模型和电流可调模型构成。利用Matlab/Simulink对系统进行了仿真,仿真结果表明该系统能较好地估计电机的磁链及转速,收敛速度快,具有良好的...     

A sensorless vector control system for induction motors usingq-axis flux with stator resistance identification

This paper presents a sensorless vector control system for general-purpose induction motors, which is based on the observer theory and the adaptive control theories. The proposed system includes a rotor speed estimator using a q-axis flux and stator resistance identifier using the d-axis flux. The advantages of the proposed system are simplicity and avoidance of problems caused by using only a voltage model. Since the mathematical model of this system is constructed in a synchronously rotating reference frame, a linear model is easily derived for analyzing the system stability, including the influence of the observer gain, motor operating state, and parameter variations. In order to obtain stable low-speed operation and speed control accuracy, an algorithm for compensating for the deadtime of the inverter and correcting the nonideal features of an insulated gate bipolar transistor was developed. The effectiveness of the proposed system has been verified by digital simulation and experimentation     

基于一体化网络的普适服务研究

在深入分析研究、归纳总结各种传统网络服务的共性机理与理论的基础上,设计构思基于一体化网络的普适服务总体模型体系,创建了服务标识机制与理论,完成对各种网络服务的统一描述和标识,以实现多种服务的统一处理;建立连接标识机制与理论,有效解决了普适服务的移动性和安全性问题,并在此基础上给出新传输协议,以支持语音、数据等多种服务;提出服务标识解析映射和连接标识解析映射机理与理论,实现了一体化网络下的服务多连接建立和多路径传输;最后通过原型系统实现和仿真分析证明普适服务机理和理论的正确性、有效性和可行性.     

A New Model-Based Technique for the Diagnosis of Rotor Faults in RFOC Induction Motor Drives

This paper proposes a new model-based diagnostic technique, which is the so-called virtual current technique (VCT), for the diagnosis of rotor faults in direct rotor field oriented controlled (DRFOC) induction motor drives. By measuring the oscillations at twice the slip frequency found in the rotor flux of the machine, and by conjugating this information with the knowledge of some motor parameters, as well as the parameters of the flux and current controllers, it is possible to generate a virtual magnetizing current which, after normalization, allows the detection and quantification of the extension of the fault. The proposed method allows one to overcome the major difficulties usually found in the diagnosis of rotor faults in closed-loop drives by providing information about the condition of the machine in a way that is independent of the working conditions of the drive such as the load level, reference speed, and bandwidth of the control loops. Although the VCT was primarily developed for traction drives used in railway applications, it can be incorporated in any DRFOC drive at almost no additional cost. Several simulation results, obtained with different types of DRFOC drives, as well as experimental results obtained in the laboratory, demonstrate the effectiveness of this new diagnostic approach.      

Fuzzy neural networks for identification and control of ultrasonicmotor drive with LLCC resonant technique

This paper demonstrates the applications of fuzzy neural networks (FNNs) in the identification and control of the ultrasonic motor (USM). First, the USM is derived by a newly designed high-frequency two-phase voltage-source inverter using LLCC resonant technique. Then, two FNNs with varied learning rates are proposed to control the rotor position of the USM. The USM drive system is identified by a fuzzy neural network identifier (FNNI) to provide the sensitivity information of the drive system to a fuzzy neural network controller (FNNC). A backpropagation algorithm is used to train both the FNNI and FNNC on-line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNNs. In addition, the effectiveness of the FNN-controlled USM drive system is demonstrated by experimental results. Accurate tracking response can be obtained due to the powerful on-line learning capability of the FNNs. Furthermore, the influence of parameter variations and external disturbances on the USM drive system can be reduced effectively     

Design and implementation of the extended Kalman filter for thespeed and rotor position estimation of brushless DC motor

A method for speed and rotor position estimation of a brushless DC motor (BLDCM) is presented in this paper. An extended Kalman filter (EKF) is employed to estimate the motor state variables by only using measurements of the stator fine voltages and currents. When applying the EKF, it was necessary to solve some specific problems related to the voltage and current waveforms of the BLDCM. During the estimation procedure, the voltage- and current-measuring signals are not filtered, which is otherwise usually done when applying similar methods. The voltage average value during the sampling interval is obtained by combining measurements and calculations, owing to the application of the predictive current controller which is based on the mathematical model of motor. Two variants of the estimation algorithm are considered: (1) speed and rotor position are estimated with constant motor parameters and (2) the stator resistance is estimated simultaneously with motor state variables. In order to verify the estimation results, the laboratory setup has been constructed using a motor with ratings of 1.5 kW, 2000 r/min, fed by an insulated gate bipolar transistor inverter. The speed and current controls, as well as the estimation algorithm, have been implemented by a digital signal processor (TMS320C50). The experimental results show that is possible to estimate the speed and rotor position of the BLDCM with sufficient accuracy in both steady-state and dynamic operation. Introducing the estimation of the stator resistance, the speed estimation accuracy is increased, particularly at low speeds. At the end of the paper, the characteristics of the sensorless drive are analyzed. A sensorless speed control system has been achieved with maximum steady-state error between reference and actual motor speed of ±1% at speeds above 5% of the rated value     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

High-performance speed control of electric machine usinglow-precision shaft encoder

For a high-performance servo drive system, it is important to estimate and control the motor speed precisely over a wide-speed range. Therefore, the disturbance-rejection ability and the robustness to variations of the mechanical parameters such as inertia should be considered. This paper shows that the adaptive state estimator and self-tuning regulator based on the recursive extended least squares (RELS) parameter identification method can achieve high-performance speed control over a wide-speed range. The RELS method identifies the variations of mechanical parameters, and the estimated mechanical parameters are used to replace the role of manual tuning by adjusting the gain of the speed controller automatically for good dynamic response. Also, these estimated parameters are used to adapt the Kalman filter, which is an optimal state estimator, to provide good estimation performance for the rotor speed, rotor position and disturbance torque even in a noisy environment. Simulation and experimental results show an improved speed control performance in the wide-speed range     

Position control of a sensorless synchronous reluctance motor

This paper presents a novel position control for a sensorless synchronous reluctance drive system. By measuring the three-phase currents of the motor, a rotor position estimator is achieved. Then, a velocity estimator is derived from the estimated rotor position by using a state estimating technique. The estimated velocity tracks the real velocity well. Next, a robust position controller is designed to improve the transient and load disturbance responses. By using the proposed estimating techniques and control algorithm, a high-performance sensorless synchronous reluctance drive is obtained. A digital signal processor, TMS-320-C30, is used to execute the estimating and control algorithms. No hardware circuit or external signal is added as compared with the traditional drive system with an encoder or resolver. To evaluate the performance of the position control system, a moving table is connected with the drive system. The drive system can precisely control the moving table. Experimental results show that the proposed system has good performance. Several experimental results validate the theoretical analysis.     

The optimal control of a constrained drive system with brushless DCmotor

The optimal (according to the quadratic performance index) control method of a drive position system with an electronically commutated brushless DC motor is discussed. Initially developed for linear, unconstrained, and undistributed systems, this optimal control method is now applied to a system having constrained state and input variables (e.g. armature voltage, armature current, rotor speed) and unknown disturbances (e.g. load torque). The method uses an undisturbed and unconstrained model for a model-following adaptive control of the real system. The control method is verified through computer simulation using the data from a real drive position system. Results show that the controlled system operates effectively at the limiting state variables, which represent the real system. In addition, the final position is reached without overshoots     

Adaptive positioning control for a LPMSM drive based on adapted inverse model and robust disturbance observer

The adaptive robust positioning control for a linear permanent magnet synchronous motor drive based on adapted inverse model and robust disturbance observer is studied in this paper. First, a model following two-degrees-of-freedom controller consisting of a command feedforward controller (FFC) and a feedback controller (FBC) is developed. According to the estimated motor drive dynamic model and the given position tracking response, the inner speed controller is first designed. Then, the transfer function of FFC is found based on the inverse model of inner speed closed-loop and the chosen reference model. The practically unrealizable problem possessed by traditional feedforward control is avoided by the proposed FFC. As to the FBC, it is quantitatively designed using reduced plant model to meet the specified load force regulation control specifications. In dealing with the robust control, a disturbance observer based robust control scheme and a parameter identifier are developed. The key parameters in the robust control scheme are designed considering the effect of system dead-time. The identification mechanism is devised to obtain the parameter uncertainties from the observed disturbance signal. Then by online adapting the parameters set in the FFC according to the identified parameters, the nonideal disturbance observer based robust control can be corrected to yield very close model following position tracking control. Meanwhile, the regulation control performance is also further improved by the robust control. In the proposed identification scheme, the effect of a nonideal differentiator in the accuracy of identification results is taken into account, and the compromise between performance, stability, and control effort limit is also considered in the whole proposed control scheme.     

Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the $hbox{MRAS}^{rm CC}$ rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.      

Stable identification of nonlinear systems using neural networks: theory and experiments

This paper presents an approach for stable identification of multivariable nonlinear system dynamics using a multilayer feedforward neural network. Unlike most of the previous neural network identifiers, the proposed identifier is based on a nonlinear-in-parameters neural network (NLPNN). Therefore, it is applicable to systems with higher degrees of nonlinearities. Both parallel and series-parallel models are used with no a priori knowledge about the system dynamics. The method can be considered both as an online identifier that can be used as a basis for designing a neural network controller as well as an offline learning scheme for monitoring the system states. A novel approach is proposed for the weight updating mechanism based on the modification of the backpropagation (BP) algorithm. The stability of the overall system is shown using Lyapunov's direct method. To demonstrate the performance of the proposed algorithm, an experimental setup consisting of a three-link macro-micro manipulator (M/sup 3/) is considered. The proposed approach is applied to identify the dynamics of the experimental robot. Experimental and simulation results are given to show the effectiveness of the proposed learning scheme.     

MLP/RBF neural-networks-based online global model identification of synchronous generator

This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN) for online identification of the nonlinear dynamics of a synchronous generator in a power system. The computational requirement to process the data during the online training, local convergence, and online global convergence properties are investigated by time-domain simulations. The performances of the identifiers as a global model, which are trained at different stable operating conditions, are compared using the actual signals as well as the deviation signals for the inputs of the identifiers. Such an online-trained identifier with fixed optimal weights after the global convergence test is needed to provide information about the plant to a neurocontroller. The use of the fixed weights is to provide against a sensor failure in which case the training of the identifiers would be automatically stopped, and their weights frozen, but the control action, which uses the identifier, would be able to continue.     

A sensorless initial rotor position estimation scheme for a direct torque controlled interior permanent magnet synchronous motor drive

In direct torque control (DTC) scheme, the requirement of the continuous rotor position sensor and coordinate transformation is eliminated since all the calculation is done in stator reference frame. However, the DTC scheme requires the position sensor to determine the initial position of the rotor at starting. Elimination of the shaft-mounted position encoder is a very desirable objective in many applications since this sensor is often one of the most expensive and fragile components in the entire drive system. This paper presents a sensorless method of determining the initial rotor position of a direct torque controlled interior permanent magnet (IPM) synchronous motor drive. The method consists of injecting a high frequency voltage to the windings and examining the effects of the saliency on the amplitude of the corresponding stator current components. This method does not depend on the level of static load and on any motor parameters. The magnet polarity of the rotor at its initial position is also identified using the effect of saliency. Modeling and experimental results verify the effectiveness of the proposed method.     

Wavelet neural network control for induction motor drive using sliding-mode design technique

This paper addresses an adaptive observation system and a wavelet-neural-network (WNN) control system for achieving the favorable decoupling control and high-precision position tracking performance of an induction motor (IM) drive. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor with a high sampling rate to make it possible to achieve good dynamics. Moreover, a WNN control system is developed via the principle of sliding-mode control to increase the robustness of the indirect field-oriented IM drive with the adaptive observation system for high-performance applications. In the WNN control system, a WNN is utilized to predict the uncertain system dynamics online to relax the requirement of uncertainty bound in the design of a traditional sliding-mode controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.