Hybrid control for speed sensorless induction motor drive

The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results     

Experimental sensorless control for IPMSM by using integral backstepping strategy and adaptive high gain observer

In this paper a robust sensorless control for an Interior Permanent Magnet Synchronous Motor (IPMSM) is designed. The proposed control strategy uses a backstepping controller, whose robustness is improved by using integral actions added at each step of the original algorithm, and by a Maximum-Torque-Per-Ampere strategy (MTPA) to improve its energy efficient operation. Furthermore, to implement this controller in the framework of the mechanical sensorless control from the only measurements of the currents and voltages, an adaptive interconnected high gain observer is developed for estimating the rotor speed, the position and the load torque. Moreover, sufficient conditions are given to ensure the practical stability of the Observer-Controller system even if bounded uncertainties occur. Finally, the performance and the effectiveness of the designed method are tested experimentally throw a significant benchmark including different speed references and with significant robustness tests. A comparative evaluation of the computational effort of our scheme with respect to classical motor control is given.     

多机传动机械系统的同步控制

本文提出通过磁场定向控制分别控制多个感应电动机跟踪同一个指令性速度的多机驱动机械系统的同步控制方案,通过建立全维转子状态估计器,辩识感应电动机的转子速度,定子和转子电阻,瞬时外负载,根据外负载辩识结果与速度ＰＩＤ控制器确定感应电动机的转矩电流,实现感应电动机的无速度传感器控制,计算机仿真结果表明了该控制方案的有效性。     

A Terminal Sliding Mode Observer Based Robust Backstepping Sensorless Speed Control for Interior Permanent Magnet Synchronous Motor

To achieve high-performance sensorless speed control for the interior permanent magnet synchronous motor (IPMSM) drive system, a terminal sliding mode observer based robust backstepping control is proposed in this paper. Firstly, an integral-type terminal sliding mode observer is designed to replace the real mechanical sensor to obtain the rotor position and speed information. Stability of the observer is guaranteed. Then, a robust backstepping controller with integral and sliding mode actions is designed to achieve speed regulation despite uncertainties and disturbances. The convergence for the backstepping control system is ensured. Finally, the sufficient conditions for input-to-state stability (ISS) property of the observer-controller closed-loop system are also analyzed. Simulation and comparison results have demonstrated the effectiveness of the proposed sensorless control scheme.     

Sensorless adaptive output feedback control of wind energy systems with PMS generators

This paper addresses the problem of controlling wind energy conversion (WEC) systems involving permanent magnet synchronous generator (PMSG) fed by IGBT-based buck-to-buck rectifier–inverter. The prime control objective is to maximize wind energy extraction which cannot be achieved without letting the wind turbine rotor operate in variable-speed mode. Interestingly, the present study features the achievement of the above energetic goal without resorting to sensors of wind velocity, PMSG speed and load torque. To this end, an adaptive output-feedback control strategy devoid of any mechanical sensor is developed (called sensorless), based on the nonlinear model of the whole controlled system and only using electrical variables measurements. This control strategy involves: (i) a sensorless online reference-speed optimizer designed using the turbine power characteristic to meet the maximum power point tracking (MPPT) requirement; (ii) a nonlinear speed regulator designed by using the backstepping technique; (iii) a sensorless interconnected adaptive state observer providing online estimates of the rotor position as well as speed and load/turbine torque. The proposed output-feedback control strategy is backed by a formal analysis showing that all control objectives are actually achieved. Several simulations show that the control strategy enjoys additional robustness properties.     

Adaptive sensorless robust control of AC drives based on sliding mode control theory

This paper focuses in the design of a new adaptive sensorless robust control to improve the trajectory tracking performance of induction motors. The proposed design employs the so‐called vector (or field oriented) control theory for the induction motor drives, being the designed control law based on an integral sliding‐mode algorithm that overcomes the system uncertainties. This sliding‐mode control law incorporates an adaptive switching gain in order to avoid the need of calculating an upper limit for the system uncertainties. The proposed design also includes a new method in order to estimate the rotor speed. In this method, the rotor speed estimation error is presented as a first‐order simple function based on the difference between the real stator currents and the estimated stator currents. The stability analysis of the proposed controller under parameter uncertainties and load disturbances is provided using the Lyapunov stability theory. The simulated results show, on the one hand that the proposed controller with the proposed rotor speed estimator provides high‐performance dynamic characteristics, and on the other hand that this scheme is robust with respect to plant parameter variations and external load disturbances. Finally, experimental results show the performance of the proposed control scheme. Copyright © 2006 John Wiley & Sons, Ltd.     

Specificity enhancement in classification of breast MRI lesion based on multi-classifier

In the conventional CMAC-based adaptive controller design, a switching compensator is designed to guarantee system stability in the Lyapunov stability sense but the undesirable chattering phenomenon occurs. This paper proposes a CMAC-based smooth adaptive neural control (CSANC) system that is composed of a neural controller and a saturation compensator. The neural controller uses a CMAC neural network to online mimic an ideal controller and the saturation compensator is designed to dispel the approximation error between the ideal controller and neural controller without any chattering phenomena. The parameter adaptive algorithms of the CSANC system are derived in the sense of Lyapunov stability, so the system stability can be guaranteed. Finally, the proposed CSANC system is applied to a Chua’s chaotic circuit and a DC motor driver. Simulation and experimental results show the CSANC system can achieve a favorable tracking performance. It should be emphasized that the development of the proposed CSANC system doesn’t need the knowledge of the system dynamics.     

A Robust Backstepping Sensorless Control for Interior Permanent Magnet Synchronous Motor Using a Super‐Twisting Based Torque Observer

In order to achieve high‐performance speed regulation for sensorless interior permanent magnet synchronous motors (IPMSMS), a robust backstepping sensorless control is presented in this paper. Firstly, instead of a real mechanical sensor, a robust terminal sliding mode observer is used to provide the rotor position. Then, a new super‐twisting algorithm (STA) based observer is designed to obtain estimates of load torque and speed. The proposed observer ensures finite‐time convergence, maintains robust to uncertainties, and eliminates the common assumption of constant or piece‐wise constant load torque. Finally, a sensorless scheme is designed to realize speed control despite parameter uncertainties, by combining the robust backstepping control with sliding mode actions and the presented sliding mode observers. The stability of the observer and controller are verified by using Lyapunov's second method to determine the design gains. Simulation results show the effectiveness of the proposed approach.     

Adaptive control strategy with flux reference optimization for sensorless induction motors

Avoiding mechanical (speed, torque) sensors in electric motor control entails cost reduction and reliability improvement. Furthermore, sensorless controllers (also referred to output-feedback) are useful, even in the presence of mechanical sensors, to implement fault tolerant control strategies. In this paper, we deal with the problem of output-feedback control for induction motors. The solutions proposed so far have been developed based on the assumption that the machine magnetic circuit characteristic is linear. Ignoring magnetic saturation makes it not possible to meet optimal operation conditions in the presence of wide range speed and load torque variations. Presently, an output-feedback control strategy is developed on the basis of a motor model that accounts for magnetic saturation. The control strategy includes an optimal flux reference generator, designed in order to optimize energy consumption, and an output-feedback designed using the backstepping technique to meet tight speed regulation in the presence of wide range changes in speed reference and load torque. The controller sensorless feature is achieved using an adaptive observer providing the controller with online estimates of the mechanical variables. Adaptation is resorted to cope with the system parameter uncertainty. The controller performances are theoretically analyzed and illustrated by simulation.     

On the globally defined sensorless control of induction motors

The sensorless control problem of induction motors imposes a current challenge since the nonlinear model of this kind of machines does not exhibit global observability properties, i.e. there are some operation regimes for which speed observability is lost. One way for dealing with this unavoidable limitation, and at the same time provide globally defined controllers, is to consider that the rotor variables are estimated via an open‐loop observer. In this paper a globally defined passivity‐based speed controller that belongs to the aforementioned class is presented. It is shown that the structure of previously reported passivity‐based controllers, developed under the assumption that the mechanical variables are available for measurement, can be extended to operate under sensorless conditions if a speed observer is included in the control scheme. Since the controller design methodology leads to inherent drawbacks regarding robustness issues, to evaluate the usefulness of the proposed scheme a numerically based study is included that cover topics such as parameters and disturbance (load torque) uncertainty. The advantages and limitations of the proposed scheme are established with respect to other globally defined sensorless controllers. Copyright © 2008 John Wiley & Sons, Ltd.     

A speed-sensorless indirect field-oriented control for induction motors based on high gain speed estimation

The authors design a new speed sensorless output feedback control for the full-order model of induction motors with unknown constant load torque, which guarantees local asymptotic tracking of smooth speed and rotor flux modulus reference signals and local asymptotic field orientation, on the basis of stator current measurements only. The proposed nonlinear controller exploits the concept of indirect field orientation (no flux estimation is required) in combination with a new high-gain speed estimator based on the torque current tracking error. The estimates of unknown load torque and time-varying rotor speed converge to the corresponding true values under a persistency of excitation condition with a physically meaningful interpretation, basically equivalent to non-null synchronous frequency. Stability analysis of the overall dynamics has been performed exploiting the singular perturbation method. The proposed control algorithm is a “true” industrial sensorless solution since no simplifying assumptions (flux and load torque measurements) are required. Simulation and experimental tests show that the proposed controller is suitable for medium and high performance applications.     

改进型MRAS无速度传感器的无轴承异步电机矢量控制

无轴承异步电机(BIM)的转子磁链电压模型中含有纯积分环节,其积分初值和累计误差会影响磁链观测精度,进而使转速估计产生严重失真.为了实现BIM无速度传感器运行,本文借鉴模型参考自适应法(MRAS)基本结构,将改进二阶广义积分器与锁频环结合以代替原有纯积分器,提出了一种新的基于MRAS的BIM无速度传感器控制方法,构建了BIM转子磁链定向无速度传感器矢量控制系统.并且,基于MATLAB/Simulink的仿真验证和基于dSPACE的实验结果表明:与传统电压模型观测方法相比,所提出的转子磁链电压模型有效避免了纯积分环节带来的直流偏移和积分初值影响,有着更好的观测效果.同时,基于无轴承异步电机转子磁链定向无速度传感器矢量控制系统,电机能稳定悬浮运行,估算转速和实测转速具有很好的一致性.     

A direct adaptive neural command controller design for an unstable helicopter

This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).     

Neural speed estimator for line-connected induction motor embedded in a digital processor

Estimating the electrical and mechanical parameters involved in three-phase induction motors is frequently employed to avoid measuring every variable in the process. Among mechanical parameters, speed is an important variable: it is involved in control, diagnosis, condition monitoring, and can be measured or estimated by sensorless methods. These technologies offer advantages when compared with direct measurement, such as lower cost or more robust systems. This paper proposes the use of artificial neural networks to estimate rotor speed by using current sensors for balanced and unbalanced voltage sources with a wide mechanical load range in a line-connected induction motor. This paper also presents two case analyses: (i) a single current sensor; and (ii) a multiple currents sensors. Simulation and experimental results are presented to validate the proposed approach. A neural speed estimator embedded in a digital processor is also presented.     

Simplified EKF based sensorless direct torque control of permanent magnet brushless AC drives

A simplified extended Kalman filter (EKF) based sensorless direct torque control technique for a permanent magnet brushless AC drive is proposed. Its performance is compared with that obtained with other sensorless methods for estimating the rotor speed and position from a stator flux-linkage. Since the EKF has an inherently adaptive filtering capability and does not introduce phase delay, the technique provides better speed estimates. In addition, the technique is easy to implement and requires minimal computation.     

Sensorless Inverter‐Fed Compressor Drive System Using Back‐EMF Estimator with PIDNN Torque Observer

A saliency back‐EMF estimator with a proportional–integral–derivative neural network (PIDNN) torque observer is proposed in this study to improve the speed estimating performance of a sensorless interior permanent magnet synchronous motor (IPMSM) drive system for an inverter‐fed compressor. The PIDNN torque observer is proposed to replace the conventional proportional–integral–derivative (PID) torque observer in a saliency back‐EMF estimator to improve the estimating performance of the rotor flux angle and speed. The proposed sensorless control scheme use square‐wave type voltage injection method as the start‐up strategy to achieve sinusoidal starting. When the motor speed gradually increases to a preset speed, the sensorless drive will switch to the conventional saliency back‐EMF estimator using the PID observer or the proposed saliency back‐EMF estimator using the PIDNN observer for medium and high speed control. The theories of the proposed saliency back‐EMF rotor flux angle and speed estimation method are introduced in detail. Moreover, the network structure, the online learning algorithms and the convergence analyses of the PIDNN are discussed. Furthermore, a DSP‐based control system is developed to implement the sensorless inverter‐fed compressor drive system. Finally, some experimental results are given to verify the feasibility of the proposed estimator.     

基于重置算法的感应电机转速自适应观测器

常规并联双模型转速自适应观测器在电机启动、加速或突加负载时存在观测转速滞后性大、精度低的问题, 使得感应电机无速度传感器控制系统的调速性能变差. 针对上述问题, 提出一种重置自适应转速观测器对转子磁链进行观测, 同时通过自适应机构得到电机转速, 并利用Lyapunov 稳定性定理证明了系统的稳定性. 仿真和实验结果表明, 所提出的重置自适应观测器的观测误差小、稳定性好, 改善了在电机启动、加速或突加负载时的转速观测性能.

     

A rotor speed estimation algorithm in variable speed permanent magnet synchronous generator wind energy conversion system

A rotor speed estimation algorithm in a direct vector controlled permanent magnet synchronous generator wind energy conversion system is proposed. The proposed method is based on a simple equation obtained from the flux model of the machine and contains only stator flux and current. Constant gain recursive least squares estimator is used for implementing the speed estimation algorithm. Rotor position information used for coordinate transformation is computed using the estimated speed. Stator flux information required by the speed estimator is obtained using the stator voltage equation by implementing a programmable low pass filter. The estimated speed is used as the feedback signal for the speed control loop of the vector controlled machine side converter control system whose command speed is obtained from a wind speed sensorless maximum power point tracking controller, thus, we obtain a complete rotor speed and wind speed sensorless permanent magnet synchronous generator wind energy conversion system. Simulation is carried out to validate the performance of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

基于DSP的自适应速度辨识直接转矩控制系统研究

异步电机直接转矩控制能产生快速且良好的鲁棒性响应,采用自适应磁链观测器,取代传统的积分器,构造了新型的速度估计器,并结合模糊控制器,实现对定子磁链准确观测和系统无速度传感器运行状态。基于DSP(TMS320LF2407A)核心芯片建立数字化控制系统。仿真与实验表明,该系统对电机定子磁链的观测精度高,转速估算准确,尤其在低速下能保持很高的性能。     

基于EKF和SMC的永磁同步电机无传感器矢量控制

采用扩展卡尔曼滤波(EKF)算法估计永磁同步电机(PMSM)转速和转子位置,构成转速、电流双闭环的无传感器矢量控制系统.针对扩展卡尔曼滤波为有偏估计、对模型误差鲁棒性差等问题,提出了基于指数趋近律的滑模转速控制器.为提高转速环抗负载转矩扰动能力,设计负载转矩观测器并将观测结果引入到电流控制器的输入端,作为速度控制器前馈补偿的控制输入.仿真实验结果表明,与传统采用PI(proportional-integral)转速控制器的系统相比,文中所提控制策略具有转速跟踪误差小、响应快、无超调、抗负载扰动能力强等优点.