双模糊神经网络的滑模控制感应电动机伺服驱动研究

根据感应电动机伺服驱动系统高性能的要求,设计了一种基于双模糊神经网络的滑模控制的感应电动机伺服驱动系统.控制策略具有设计过程简单、意义清晰、鲁棒性好等优点,对于参数未知、时变、负载扰动大的伺服系统,它是一种有效的实时控制策略.仿真和实验结果表明:该控制策略大大提高了系统的高速响应能力,鲁棒性强,系统具有较好的动、静态性能和抗干扰能力,是开发高精度、高性能交流伺服系统的一条有效途径.     

Robust speed-controlled induction motor drive based on recurrent neural network

This paper proposes a recurrent neural network speed controller for an induction motor drive. This speed controller consists of a recurrent neural network identifier (RNNI) and recurrent neural network controller (RNNC). The RNNI is used to provide real-time adaptive identification of the unknown motor dynamics. The RNNC is used to produce an adaptive control force so that the motor speed can accurately track the reference command. A back-propagation algorithm was used as the learning algorithm to automatically adjust the weights of the RNNI and RNNC in order to minimize the performance functions. The proposed control scheme can quickly estimate the plant parameters and produce a control force, such that the motor speed can accurately track the reference command. Both computer simulations and experimental results demonstrated that the proposed control scheme was able to obtain robust speed control.     

基于小波神经网络自适应反推的永磁同步电机位置伺服控制

提出一种基于小波神经网络(WNN)的自适应反推控制策略,该策略通过对系统中的非线性不确定性进行估计和补偿,可以自适应调节反推控制器的输出,以获得良好的位置跟踪效果和对各类不确定性的鲁棒作用。设计中通过李雅普诺夫稳定性原理保证了整个系统的稳定性并给出了证明。经理论分析和通过与PI控制器及传统反推法的对比仿真的结果证明了该方法的有效性。     

Sensorless induction spindle motor drive using fuzzy neural network speed controller

A sensorless induction spindle motor drive using synchronous PWM and dead-time compensator with fuzzy neural network (FNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type synchronous PWM technique are described in detail. Then, a speed observer based on the model reference adaptive system (MRAS) theory is adopted to estimate the rotor speed. To increase the accuracy of the estimated speed, the speed estimation algorithm is implemented using a digital signal processor. Moreover, since the control characteristics and motor parameters for high speed operated induction spindle motor drive are time-varying, an FNN speed controller is developed to reduce the influence of parameter uncertainties and external disturbances. In addition, the FNN is trained on-line using a delta adaptation law. Finally, the performance of the proposed sensorless induction spindle motor drive system is demonstrated using some simulation and experimental results.     

Direct self control of induction motor based on neural network

This paper presents an artificial-neural-network-based direct-self-control (ANN-DSC) scheme for an input inverter-fed three-phase induction motor. In order to cope with the complex calculations required in direct self control (DSC), the proposed artificial-neural-network (ANN) system employs the individual training strategy with fixed-weight and supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results obtained demonstrate the feasibility of ANN-DSC. Compared with the classical digital-signal-processor-based DSC, the proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized     

Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

The recurrent modified Elman neural network (NN) controlled a permanent magnet (PM) synchronous generator system, which is driven by wind turbine emulator of a PM synchronous motor servo drive, is developed to regulate output voltage of rectifier (or AC to DC power converter) and inverter (or DC to AC power converter) in this study. First, the wind turbine emulator of a closed loop PM synchronous motor servo drive is designed to produce the maximum power for the PM synchronous generator system. Then, the rotor speed of the PM synchronous generator, the output DC bus voltage and current of the rectifier are detected simultaneously to yield maximum power output of the rectifier through DC bus power control. Because the PM synchronous generator system is a nonlinear and time varying dynamic system, the online training recurrent modified Elman NN control system is developed to regulate DC bus voltage of the rectifier and AC line voltage of the inverter in order to improve the control performance. Furthermore, the online training recurrent modified Elman NN control system with the variable learning rate is derived based on Lyapunov stability theorem, so that the stability of the system can be guaranteed. Finally, some experimental results are verified to show the effectiveness of the proposed recurrent modified Elman NN controlled PM synchronous generator system.     

基于神经网络的模糊控制在开关磁阻电机中的应用研究

把神经网络与模糊逻辑结合起来,利用神经网络的学习控制算法调节模糊逻辑隶属函数,通过对开 关磁阻电机运行特性的分析,提出了一种可应用于开关磁阻电机驱动系统的智能控制方法,理论和仿真结果均证明了这种基于神经网络模糊控制方法在开关磁阻电机驱动系统中应用的可行性和可靠性。     

PWM-CSI induction motor drive with phase angle control

The authors describe a pulse-width-modulated current-source inverter drive system using an induction motor. Its dominant feature is that it provides adequate control of either torque or speed over a wide range without requiring a shaft position or speed sensor. The capacitor-loaded current-source invertor system has the advantages of simplicity, low switching frequency, four-quadrant operation, overcurrent protection, and low harmonic content in the motor current and voltage. The choice of maximum value for the capacitor depends on the degree of nonlinearity that can be tolerated in the torque-stator current relation, whereas its minimum value depends on the need for a low impedance path for inverter current harmonics. The control strategy is based on the concept of controlling the stator phase angle of the induction motor. The stator voltage is sensed, and the current-source inverter is used to inject current into the motor with the desired phase angle     

Robust speed estimation and control of an induction motor drive based on artificial neural networks

In this paper, a speed estimation and control scheme of an induction motor drive based on an indirect field‐oriented control is presented. On one hand, a rotor speed estimator based on an artificial neural network is proposed, and on the other hand, a control strategy based on the sliding‐mode controller type is proposed. The stability analysis of the presented control scheme under parameter uncertainties and load disturbances is provided using the Lyapunov stability theory. Finally, simulated results show that the presented controller with the proposed observer provides high‐performance dynamic characteristics and that this scheme is robust with respect to plant parameter variations and external load disturbances. Copyright © 2007 John Wiley & Sons, Ltd.     

电液位置伺服系统的模糊神经网络控制

针对电液位置伺服系统中的非线性、参数时变性等复杂因素,设计了一种模糊神经网络控制方案。由于常规的模糊神经网络学习算法具有权值调整复杂,收敛速度慢的缺点,因此采用模糊逐级误差逼近方法来调整模糊神经网络的权值。该算法易于实现,网络权值在线学习速度较快,而且计算量小于其他的常规神经网络学习算法。将该方法应用于电液位置伺服控制系统中,在对象参数摄动情况下,进行了仿真研究。仿真结果表明,采用该方法所设计的控制器满足系统对快速性和稳态精确度的要求,系统的鲁棒性增强,验证了方法的有效性。     

用神经网络实现对感应电机转速估计

根据数学模型．提出了一种利用神经网络进行感应电机转速估计的新方法。方法是采用一个3层神经网络作为速度估计器来估计转速。仿真结果表明，这种基于人工神经网络的转速估计模型可以准确地跟踪感应电机转速的变化，具有良好的动态跟随性能。     

Fuzzy control of integrated current-controlledconverter-inverter-fed cage induction motor drive

This paper presents a performance analysis of a vector-controlled cage induction motor drive. The drive is fed from a current-controlled converter-inverter system. The pulsewidth modulation current control technique is used to derive the switching signals for the converter, as well as the inverter. Closed-loop control of the cage induction motor utilizes a fuzzy proportional integral derivative speed controller. The advantages offered by the current control converter-inverter link are exploited. In order to examine the dynamic performance of the drive system, its model is simulated and results are analyzed. The potential applications of this particular drive system are outlined     

A digital signal processor based robust integral-proportional controller for an induction motor servo drive

A digital signal processor (DSP) based computer controlled indirect field oriented induction motor servo drive with robust control characteristics is proposed in this paper. First, the DSP based induction motor servo drive is implemented and the dynamic model of the system at the nominal condition is identified by a curve fitting technique. Based on this model, an integral-proportional controller is quantitatively designed to match the prescribed position tracking specifications. Then a simple robust controller is designed and augmented to reduce the performance degradation due to uncertainties.     

Performances of fuzzy-logic-based indirect vector control for induction motor drive

This paper presents a novel speed control scheme of an induction motor (IM) using fuzzy-logic control. The fuzzy-logic controller (FLC) is based on the indirect vector control. The fuzzy-logic speed controller is employed in the outer loop. The complete vector control scheme of the IM drive incorporating the FLC is experimentally implemented using a digital signal processor board DS-1102 for the laboratory 1-hp squirrel-cage IM. The performances of the proposed FLC-based IM drive are investigated and compared to those obtained from the conventional proportional-integral (PI) controller-based drive both theoretically and experimentally at different dynamic operating conditions such as sudden change in command speed, step change in load, etc. The comparative experimental results show that the FLC is more robust and, hence, found to be a suitable replacement of the conventional PI controller for the high-performance industrial drive applications.     

小波神经网络与PID相结合的负荷频率控制

针对跨区域互联电力系统负荷频率控制的严重非线性,及传统PID控制稳定性差、超调严重、响应速度慢,提出了将小波神经网络与传统PID控制相结合的控制模型。PID完成区域电网内的二级负荷频率控制,区域控制偏差作为有2个小波神经元的神经网络输入,输出用于共同控制负荷频率稳定。网络还采用了负反馈提高学习收敛速度,网络参数采用梯度法结合遗传算法寻优确定。仿真实验结果表明,该方案具有较好的控制效果和鲁棒性。     

基于模型参考自适应模糊神经网络的直线永磁同步电动机速度伺服系统

针对直线永磁同步电动机全闭环控制系统易受干扰而降低系统性能指标,甚至造成不稳定等问题,采用模型参考自适应模糊神经网络在线辩识方法,用梯度法实时修正模糊控制器的输入和输出隶属度参数,建立了模型参考自适应模糊神经网络速度伺服系统模型,并给出了模糊神经网络控制器的设计。通过仿真和实验结果证明,这种方法提高了速度检测装置的分辨率和动态响应能力,并且使系统具有很强的鲁棒性。     

MRAS-based sensorless control of a vector controlled five-phase induction motor drive

Multi-phase ac motor drives are nowadays considered for various applications, due to numerous advantages that they offer when compared to their three-phase counterparts. In principle, control methods for multi-phase machines are the same as for three-phase machines. Variable speed induction motor drives without mechanical speed sensors at the motor shaft have the attractions of low cost and high reliability. To replace the sensor, information of the rotor speed is extracted from measured stator currents and voltages at motor terminals. Vector controlled drives require estimating the magnitude and spatial orientation of the fundamental magnetic flux waves in the stator or in the rotor. Open-loop estimators and closed-loop observers are used for this purpose. They differ with respect to accuracy, robustness, and sensitivity against model parameter variations. This paper analyses operation of an open-loop and model reference adaptive system (MRAS)-based sensorless control of vector controlled five-phase induction machine with current control in the stationary reference frame. The MRAS-based sensorless operation of a three-phase induction machine is well established and the same principle is extended in this paper for an IRFOC five-phase induction machine. Performance, obtainable with hysteresis current control, is illustrated for a number of operating conditions on the basis of simulation results. Full decoupling of rotor flux control and torque control is realised. Dynamics, achievable with a five-phase vector controlled induction machine, are shown to be essentially identical to those obtainable with a three-phase induction machine. Experimental verification is also provided.     

AC induction servo sizing for motion control applications via lossminimizing real-time flux control

A design procedure for induction machine servo motion control applications that can be used to optimally select the minimum size machine and/or to obtain optimal time performance from a given machine is presented. The basis of the technique is in modeling and controlling of machine losses. In motion control servo applications, the position and velocity trajectories are often specified. For such cases, the rotor flux is a dynamic variable that dramatically affects the machine losses. By dynamically manipulating the flux, the losses can be minimized. To implement the design analysis, a dynamic programming tool is used. Its output is the minimal loss flux trajectory for a given cycle or for the minimum cycle time for a given thermal capability. The minimal loss flux trajectory is then modeled in reduced form for real-time implementation. Theoretical and experimental results demonstrate substantial performance advantages from such an approach     

基于模糊高斯基函数神经网络控制的交流伺服系统

将模糊控制与神经网络相结合,用神经网络来实现模糊推理,提出了一种把高斯基函数作为隶属函数的模糊神经网络,并将之用于交流伺服系统的控制中