A rotor-flux-observer-based composite adaptive speed controller foran induction motor

A composite adaptive speed controller for an induction motor based on a rotor-flux-observer is proposed in this paper. The rotor flux is estimated with the simplified rotor-flux-observer on the rotor reference frame and the input-output linearization theory is used to decouple the motor speed and the rotor flux. Then, the composite adaptive algorithm is used as the speed controller of the induction motor drive. The resulting system is verified to be stable. Some experimental results are provided to demonstrate the effectiveness of the proposed adaptive controller. Good speed tracking and load regulating responses can be obtained by the proposed composite adaptive controller. Moreover, the system can be operated in a wide range of speed and is robust to parameter variations     

Model reference neural network controller for induction motor speedcontrol

This paper proposes a novel robust speed control method for induction motor drives based on a two-layered neural network plant estimator (NNPE) and a two-layered neural network PI controller (NNPIC). The NNPE is used to provide a real-time adaptive estimation of the unknown motor dynamics. The widely used projection algorithm is used as the learning algorithm for these neural networks to automatically adjust the parameters of the NNPIC and to minimize the differences between the motor speed and the speed predicted by the NNPE. The simulation and experimental results demonstrate that the proposed robust control scheme can improve the performance of an induction motor drive and reduce its sensitivity to parameter variations and load disturbances     

Speed and efficiency control of an induction motor withinput-output linearization

A combination of a composite adaptive speed controller and an explicit efficiency control algorithm is proposed to control the speed and power efficiency of the induction motor in this paper. First, the input-output linearization method is used to dynamically decouple the motor speed and rotor flux. Then, a composite adaptive control algorithm is designed to control the speed of the induction motor. At steady-state light-load condition, the magnetizing current command is adjusted on the basis of the product of magnetizing current command and torque current command such that the steady-state power loss is minimum. A PC-based experimental drive system has been implemented, and some experimental results are provided to demonstrate the effectiveness of the presented approach     

Self-tuning control of induction motor drive using neural networkidentifier

This study presents a new self-tuning PI speed controller with load torque observer and feedforward compensation based on neural network identification for an induction motor. A two-layer neural estimator is also used to provide a real-time adaptive estimation of the unknown motor dynamics. The widely used projection algorithm is used as the learning algorithm for this network, to minimize the difference between the motor's actual response and that predicted by the neural estimator. The proposed neural estimator uses this learning to adjust PI speed controller with a load torque observer to generate the control signal online, thereby bringing the motor output to a desired reference trajectory. The theoretical analysis, simulation and experimental results demonstrate the proposed scheme's effectiveness     

Adaptive Self-Tuning Speed Control for Permanent-Magnet Synchronous Motor Drive With Dead Time

In this paper, an adaptive self-tuning speed control for a permanent-magnet synchronous motor (PMSM) drive with dead time is proposed. Firstly, to equivalently place the dead time element outside the closed-loop speed control, a dead time compensator (DTC), based on the Smith predictor and a self-tuning proportional-integral model-following controller (ST-PI-MFC) is proposed. The model-following error is used to adaptively update the gains of the ST-PI-MFC via the affine projection algorithm (APA). Secondly, a disturbance observer, based on the time delay control (TDC) approach is used for torque feed forward control. The system's model is greatly simplified when the disturbance observer is combined with the motor. Relying on the simplified model, a natural adaptive observer is used to estimate the motor speed. Unknown motor parameters are estimated by minimizing the state estimation error using an iterative gradient algorithm offered by the affine projection. The estimated parameters are used to update the gains of the integral-proportional (IP) servo loop controller, the disturbance observer and the Smith model. The validity and usefulness of the proposed control scheme are verified through simulation and experimental results     

基于双重速率QDMC的液压型风力发电机组变量马达转速控制

由于二次动态矩阵控制(quadratic dynamic matrix control,QDMC)算法中二次规划求解计算量大、实时性差等问题,难以满足液压型风力发电机组变量马达转速的高精度快速控制需求.该文提出一种双重速率二次动态矩阵控制算法(dual rate QDMC,DR-QDMC),将控制增量的求解分解为快速层...     

A new composite adaptive speed controller for induction motor basedon feedback linearization

A new adaptive control technique is proposed to control the speed of the induction motor in this paper. First, the rotor flux is estimated with the simplified rotor flux observer on the rotor reference frame and the feedback linearization theory is used to decouple the rotor speed and the flux amplitude. Then, a new composite adaptive control algorithm based on an integral cost function is designed to control the speed of the induction motor. The overall speed control system is verified to be stable and robust to the parameter variations and external disturbances. Experimental results are provided to demonstrate the effectiveness of the presented approach. The good speed tracking and load regulating responses can be obtained by the proposed controller     

基于模糊推理的摩托车速度特性测试系统

本文介绍了基于电涡流测功机的摩托车发动机速度特性测试系统，给出了转速控制系统的结构，提出了基于模糊逻辑控制器的转速控制器及采用Mamdani算法实现模糊逻辑控制器的推理。着重介绍了实现Mamdani算法的C语言程序。该程序具有简捷、易于实现和移植好等特点。实验表明该速度特性测试系统是合理的，并能获得满意的控制效果。     

Advanced fault diagnosis of a DC motor

This paper presents a model of the DCc motor with an eccentric rotor. The winding function theory shows the effect of eccentricity fault on the motor inductances and the simulation is done using a nonsymmetric air-gap function. A modified equation is presented to show the existence of rotor slot harmonics in the DC motor current. To detect the eccentricity fault, a pattern recognition technique is utilized. The proposed algorithm works at steady state and uses armature current as input. The rotor speed is needed in order to provide the appropriate feature for the classifier. Therefore, rotor speed is estimated from the armature current using the commutation harmonics. The experimental results obtained from a 1/3-hp shunt DC motor verifies the proposed method. In order to cover different motor conditions, data are collected at different shaft speeds for both a healthy dc motor and a dc motor with an unbalanced load which exhibits static eccentricity.     

直流电机PID控制器的FPGA设计与实现

针对嵌入式数字控制器应用中经常遇到的模数信号接口控制算法取近似值的问题,提出了一种采用VHDL语言、基于FGPA实现的直流电机运动数字PID控制器。该PID控制器具有自定义功能的输入和输出端口,应用于运动控制系统的速度控制回路中,在Xilinx Spartan 6 FPGA芯片上完成硬件测试。试验测试和仿真表明,运用所提的PID控制器控制输入的PWM信号生成,可用于控制电机驱动电路、破译光编码器的数据及反馈PID控制回路中的速度,有效可行。     

Real-Time Speed and Flux Adaptive Control of Induction Motors Using Unknown Time-Varying Rotor Resistance and Load Torque

In this paper, an algorithm for direct speed and flux adaptive control of induction motors using unknown time-varying rotor resistance and load torque is described and validated with experimental results. This method is based on the variable structure theories and is potentially useful for adjusting online the induction motor controller unknown parameters (load torque and rotor resistance). The presented nonlinear compensator provides voltage inputs on the basis of rotor speed and stator current measurements, and generates estimates for both the unknown parameters and the nonmeasurable state variables (rotor flux and derivatives of the stator current and voltage) that converge to the corresponding true values. Experiments show that the proposed method achieved very good tracking performance within a wide range of the operation of the induction motor (with online variation of the rotor resistance: up to (87%). This high tracking performance of the rotor resistance variation demonstrates that the proposed adaptive control is beneficial for motor efficiency. The proposed algorithm also presented high decoupling performance and very interesting robustness properties with respect to the variation of the stator resistance (up to 100%), measurement noise, modeling errors, discretization effects, and parameter uncertainties (e.g., inaccuracies on motor inductance values). The other interesting feature of the proposed method is that it is simple and easily implementable in real time. Comparative results have shown that the proposed adaptive control decouples speed and flux tracking while standard field-oriented control does not.      

Adaptive fuzzy-neural-network control for induction spindle motor drive

An induction spindle motor drive using synchronous pulse-width modulation (PWM) and dead-time compensatory techniques with an adaptive fuzzy-neural-network controller (AFNNC) is proposed in this study for advanced spindle motor applications. First, the operating principles of a new synchronous PWM technique and the circuit of dead-time compensator are described in detail. Then, since the control characteristics and motor parameters for high-speed-operated induction spindle motor drive are time varying, an AFNNC is proposed to control the rotor speed of the induction spindle motor. In the proposed controller, the induction spindle motor-drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI online. Moreover, the effectiveness of the proposed induction spindle motor-drive system is demonstrated using some simulated and experimental results.     

游梁式抽油机异步电动机矢量空间节能研究

提出游梁式抽油机异步电动机矢量控制最小励磁电流控制规律，对取得定子电流最小值的方法进行了证明。该节能控制方案不增加系统硬件成本，根据电动机的负载状态对励磁电流进行调节，使定子电流达到最小值，从而达到电动机自身节能的目的。基于MATLAB／SIMULINK仿真软件进行仿真研究，结果表明本文控制方案不仅能使游梁式抽油机异步电动机轻载运行时节能，还使其电磁转矩振荡减小。该控制方法可以推广到一般中小容量异步电动机变频调速系统。     

A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives

Accurate knowledge of stator resistance is of utmost importance for correct operation of a number of speed sensorless induction motor control schemes in the low speed region. Since stator resistance inevitably varies with operating conditions, stable and accurate operation at near-zero speed requires an appropriate online identification algorithm for the stator resistance. The paper proposes such an identification algorithm, which is developed for the rotor flux-based model reference adaptive system (MRAS) type of the speed estimator in conjunction with a rotor flux oriented control scheme. In this speed estimation method, only one degree of freedom (out of the two available) is utilized for speed estimation. It is proposed to utilize the second available degree of freedom as a means for adapting the stator resistance online. The parallel stator resistance and rotor speed identification algorithm is developed in a systematic manner, using Popov's hyperstability theory. It increases the complexity of the overall control system insignificantly and enables correct speed estimation and stable drive operation at near-zero speeds. The proposed speed estimator with parallel stator resistance identification is at first verified by simulation. Extensive experimentation is conducted next at low speeds of rotation and successful stator resistance identification is achieved down to 0.5-Hz frequency of rotation.     

Speed sensorless estimation of AC induction motors using the fast orthogonal search algorithm

This paper presents a method of estimating the speed of an induction motor using a measurement of the stator current. Speed-induced current harmonics are identified in the stator current using the fast orthogonal search (FOS) algorithm. The frequencies of these estimated harmonics are in turn used to estimate the speed of the motor given the number of rotor slots in the motor. Several optimizations of the FOS algorithm are presented to allow for real-time performance on an embedded digital signal processor. Experimental results of speed estimates on a 1/4 horsepower motor are presented to verify this approach.     

Pattern recognition-a technique for induction machines rotor brokenbar detection

A pattern recognition technique based on Bayes minimum error classifier is developed to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. Initially, rotor speed is estimated from the stator currents, then appropriate features are extracted. The produced feature vector is normalized and fed to the trained classifier to see if the motor is healthy or has broken bar faults. Only the number of poles and rotor slots are needed as pre-knowledge information. A theoretical approach together with experimental results derived from a 3 hp AC induction motor show the strength of the proposed method. In order to cover many different motor load conditions, data are obtained from 10% to 130% of the rated load for both a healthy induction motor and an induction motor with a rotor having 4 broken bars     

Tracking control technique for induction motors

A novel application is presented of the tracking control technique to induction motor drive systems. By this technique, the position or the speed of the rotor can follow a preselected track (a time history of rotor position or velocity). An algorithm for the design of the tracking controller is developed. The induction motor model and the controller are modified to allow the inclusion of the nonlinear modes in the system without excessive computations. A simple and realistic criterion for selecting the proper reference tracks during starting, speed control and braking is proposed. The controller developed, is tested on a full-size nonlinear analog simulator. All test results show the effectiveness of the scheme in position-tracking applications such as robotics and manipulators     

A method for transient torque response improvement in optimum efficiency induction motor drives

Optimal efficiency control of induction motor drives implies operation at reduced flux levels with light loads. Two problems in light load operation are a large speed drop after sudden load torque increase and slow acceleration. In order to improve response in these transients, an algorithm for optimum dynamic distribution of the available maximum inverter current into the flux-producing and the torque-producing stator current components is developed in this paper. The proposed algorithm accounts for the main flux saturation effect in the machine and the dynamics of the flux variation. Its performance is illustrated by means of simulation and experimental results. Superiority of the developed algorithm over some of the existing methods is proved by comparing the speed drops, which result after sudden load torque increase during operation at light load, and by examining an acceleration transient under light load condition.     

Sensorless scalar-controlled induction motor drives with modified flux observer

This paper presents a simple sensorless scalar control algorithm to control the speed of an induction motor. First, a modified flux observer was employed to estimate the stator flux with the voltage command and the feedback current. Then, based on the mathematical model of the induction motor, the slip frequency was calculated, and the frequency of the voltage command was compensated. An auto-boost controller was designed to overcome the decrease in voltages of the stator resistance and to maintain constant stator flux amplitude. To improve the pure integration problem, a highpass filter was installed in the stator flux observer. In this filter, the cut-off frequency is proportional to the voltage frequency; therefore, the phase shift and amplitude degradation of the estimated flux can be corrected easily. Finally, to demonstrate the proposed control algorithm, a PC-based experimental system was constructed in a 1-hp induction motor. Experimental results are presented to validate the effectiveness of our design.     

An improved energy saving v/f control technique for solar powered single-phase induction motor

With the increased energy demand and fast decaying of fossil fuels, renewable energy has remarkably gained importance in recent years. Hence a novel speed control method for wide speed operation of single-phase induction motor utilizing the non-conventional energy such as solar power is proposed in this paper. The proposed speed control method consists of two-stages of power conversion; the DC–DC boost converter with Maximum Power Point Tracking (MPPT) forms the first stage and the second stage is made up of parallel connected inverter fed single-phase induction motor load. Wide speed operation with proposed method is accomplished by volt/hertz (v/f) control technique. The performance characteristics of the motor load with the proposed method are determined for different operating conditions. The results obtained are compared with existing method of speed control. Simulated and experimental results are substantiated to validate the superiority of the proposed system. Significant energy saving together with better efficiency is achieved.