感应电机调速系统的神经网络逆在线调整控制

以多变量、非线性、强耦合的感应电机调速系统为研究对象,在基于神经网络逆系统离线训练的基础上提出了在线调整的策略,通过静态神经网络加积分器来构造感应电机调速系统的逆模型,在实际运行中不断地修正神经网络权值,更精确地逼近其逆系统,实现了感应电机转速的高精度控制。仿真和实验结果表明系统具有优良的静态及动态性能,且对电机参数的变化与负载扰动具有较强的鲁棒性。     

基于模型参考自适应滑模观测器的DFIG无速度传感器控制

论文为双馈感应发电机提出一种基于模型参考自适应滑模观测器观测转子电流的无速度传感器控制策略.以电机本体作为参考模型,在双馈感应发电机d-q坐标模型的基础上构建转子电流估算模型.依据实转子实际电流和转子估算的电流之间的偏差,通过模型参考自适应滑模观测器来估计电机转子位置和转速.该控制策略对电机参数变化具有很强的鲁棒性和快速性.最后搭建了双馈风力发电仿真平台,对所提的控制策略进行验证.仿真结果验证了所提方案可行性和正确性.     

基于BAS算法优化的感应电机参数估计

为了提高感应电机系统参数估计与状态监测的准确性和效率,针对感应电机非线性、强耦合、参数易时变的特性,引入带外部输入的非线性自回归(NARX)神经网络时序预测模型。针对传统NARX神经网络初值依赖和收敛速度慢的问题,利用天牛须搜索算法(BAS)对神经网络预测模型进行参数寻优,提高神经网络的收敛速度和预测精度。实验结果表明,该方法能够以较简单的网络结构高效、准确、稳定地预测估计电机参数。     

无速度传感器感应电机控制系统转速辨识方法研究

无速度传感器感应电机控制技术已成为近年的研究热点,转速估计是无速度传感器感应电机控制技术的核心问题。在此对无速度传感器感应电机转速辨识技术进行了介绍,分析了几种比较典型的转速辨识方法的理论要点和优缺点,在直接转矩控制基础上设计了无速度传感器感应电机控制系统模型并进行仿真,给出了试验参数及仿真图形,并就今后的研究发展方向提出了看法。     

基于自适应模糊神经网络的感应电机矢量控制

感应电机在传统PI控制中,参数固定且容易超调。针对该问题,文中研究了一种基于自适应模糊神经网络PI控制与全阶自适应观测器的感应电机矢量控制方案。根据感应电机数学模型建立了全阶自适应观测器的模型,采用Lyapunov稳定性理论对其进行了稳定性分析设计,并推导了转速自适应律。电机速度外环PI由自适应模糊神经网络推理系统在线整定优化,与传统控制方案相比,该方法易于实现,能够有效提高控制精准性,抑制外部扰动,节省了传感器成本。MATLAB/Simulink仿真实验表明,所提方案不仅改善了无速度传感器感应电机矢量控制系统的动态性能,还减小了外部负载扰动等情况的影响,提高了系统的自适应性和鲁棒性。     

具有USB的PSoC在风扇控制中的应用

由于单相感应电机的转速控制功能不仅可改变转速，还可降低能耗和恼人的噪声，因而对大多数电机控制应用而言是理想的选择。     

基于模糊神经网络的永磁同步电机伺服系统研究

永磁同步电机具有非线性、强耦合的特性,常规的矢量控制方法难以对其进行精确控制。此外,电机系统易受负载扰动影响,从而产生转速和电磁转矩波动。针对转速环参数固定会导致系统响应速度慢、超调量大的问题,文中提出了一种模糊径向基神经网络PID控制策略,用以替代矢量控制系统中转速环PID控制。将神经网络和模糊控制相结合,基于增量式PID控制方式,利用梯度下降优化算法动态调整转速环中的PID参数。系统模型仿真结果表明,模糊神经网络PID控制的电机系统超调量较小,相较于常规PID控制,新模型在低速和高速运行的启动时间分别缩短了66.7%和75.9%,动态响应更快,具有更好的鲁棒性和抗干扰能力。利用DSP搭建了实验平台,实验结果也证明了该控制方法的有效性。     

基于MRAS的永磁同步电机直接转矩控制系统的研究

研究了一种基于模型参考自适应无速度传感器的永磁同步电机直接转矩控制系统:将永磁同步电机的磁链模型作为参考模型,估算的定子磁链模型作为可调模型,设计了自适应定律对电机的转速与定子电阻同时进行跟踪辨识,使用空间电压矢量调制技术组成了永磁同步电机无速度传感器直接转矩控制系统。仿真实验结果表明该系统获得了近似圆形的定子磁链,在转速与转矩变化时均能准确的估算出电机转速,具有良好的动、静态性能。     

船舶推进电机无速度传感器矢量控制仿真研究

船舶电力推进技术的核心是船舶推进电机控制技术。采用异步电机作为船舶推进电机, 以船-桨负载模型作为船舶推进交流异步电机的负载模型, 对船-桨负载模型进行仿真分析, 同时对船舶推进异步电机的实测转子转速和估算转子转速进行了对比。经验证, 改进后的模型参考自适应算法性能更优     

控制单相感应电机的多种方法

由于单相感应电机的转速控制功能不仅可改变转速,还可降低能耗和恼人的噪声,因而对大多数电机控制应用而言是理想的选择.     

Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive

This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial neural networks. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The rotor speed is synthesized from the induction motor state equations. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive     

Speed control of ultrasonic motors using neural network

The ultrasonic motor (USM) is a newly developed motor, and it has excellent performance and many useful features, therefore, it has been expected to be of practical use. However, the driving principle of USM is different from that of other electromagnetic-type motors, and the mathematical model is complex to apply to motor control. Furthermore, the speed characteristics of the motor have heavy nonlinearity and vary with driving conditions. Hence, the precise speed control of USM is generally difficult. This paper proposes a new speed-control scheme for USM using a neural network. The proposed controller can approximate the nonlinear input-output mappings of the motor using a neural network and can compensate the characteristic variations by on-line learning using the error backpropagation algorithm. Then, the trained network finally makes an inverse model of the motor. The usefulness and validity of the proposed control scheme are examined in experiments     

神经网络用于异步电机转速估计的仿真研究

在异步电机的无传感器矢量控制中,转速估计是至关重要的一环。理论上有多种估计方法,并且已有相关的产品面市。而神经网络控制作为一种新兴的控制模式,具有超强的自学习、自适应和泛化能力,理论上能逼近任意非线性函数,特别适用于异步电机这种多变量、强耦合的系统。文章将尝试建立神经网络模型来对异步电机转速进行估计,并分析不同的网络结构及训练方法对估计精度的影响。     

Robust control of induction motor with a neural-network load torqueestimator and a neural-network identification

This paper presents a control scheme for an induction motor drive which consists of a compensator, neural network identification (NNI), and neural network load torque estimator (NNLTE) based on the conventional proportional-integral controller. The NNI is a two-layer neural network which uses a projection algorithm to estimate the parameters of the induction motor and to regulate the gain of the compensator such that the response of the induction motor follows that of the nominal plant. The NNLTE is a two-layer neural network which uses the steepest descent algorithm to estimate the load disturbance and forward feed, resulting in equivalent control such that the speed response of the induction motor is robust against the load disturbance. Computer simulations and experimental results demonstrate that the proposed control scheme can obtain a robust speed control     

基于BP神经网络的电机软起动器控制策略研究

软起动器采用交流调压电路来控制电机的起动，已经广泛使用于各种电机，如风机、压缩机、水泵等的控制场合。本文提出了一种基于BP神经网络控制的软起动器控制策略，根据电机的转矩、速度以及负载来精确计算晶闸管触发角，通过建立模型、仿真，得出该控制策略的控制效果是稳定、有效而且非常有发展前景。     

Speed sensorless control of a bearingless induction motor with combined neural network and fractional sliding mode

To address the problem of speed and flux observation in sensorless control of a bearingless induction motor under the influence of parameter changes and external disturbances, a speed sensorless control strategy combining radial basis function (radial basis function, RBF) neural network and fractional sliding mode is proposed. According to the current error, fractional sliding mode control rate is designed to reduce the speed-observed chatter of the bearingless induction motor and its adverse effect on the rotor suspension stability. Then, combined with the theory of RBF neural network, the new optimal control rate is obtained by using its approximation ability. At the same time, the stability of two control rate is proved. Thus, the flux linkage and speed under normal operation, parameter change and external disturbance are observed and the new speed sensorless control is realized. The simulation and experimental results show that the proposed joint RBF neural network approximation algorithm and fractional sliding mode speed sensorless control system of the bearingless induction motor can not only effectively identify the flux and speed under three conditions of no-load, load disturbance and speed change, but also ensure the good suspension of the motor rotor in the x-axis and y-axis directions.     

粒子群优化BP-PID的矿井提升机调速系统

传统PID控制器在矿井提升机变频调速系统应用中,由于控制参数固定且不易整定,导致电机转速超调大、电磁转矩和转子磁链脉动大,进而出现矿井提升机调速系统控制效果差的问题。针对这一问题,文中提出一种改进粒子群优化BP神经网络PID控制器的算法。由于BP神经网络算法存在收敛速度慢和极易陷入局部最优的缺点,现将粒子群算法收敛速度快和全局最优特性与神经网络结合,并通过设计神经网络收敛系数进一步加快收敛速度。仿真结果表明,粒子群优化的神经网络控制效果比神经网络好,且效果明显优于传统PID控制器;相较于神经网络PID控制器,矿井提升机转速调节系统稳速调节速度明显提高;与传统PID控制器相比,电机电磁转矩和转子磁链脉动明显降低,具有较强的稳定性和鲁棒性。     

基于模糊神经网络的异步电机DTC研究

针对异步电机直接转矩控制在低速时脉动大、开关频率不固定等缺陷,提出了一种基于模糊神经网络的直接转矩控制方案,该方案采用模糊神经网络调节器分别对转矩和磁链进行控制。该控制方法综合了模糊控制和神经网络的优点,原理简单、无需大量专家经验、具有优良的非线性逼近和自适应能力。最后通过仿真实验证明,采用该控制器的异步电机系统动态性能良好、低速脉动小。     

An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model

This paper presents a new model reference adaptive system (MRAS) speed observer for high-performance field-oriented control induction motor drives based on adaptive linear neural networks. It is an evolution and an improvement of an MRAS observer presented in the literature. This new MRAS speed observer uses the current model as an adaptive model discretized with the modified Euler integration method. A linear neural network has been then designed and trained online by means of an ordinary least-squares (OLS) algorithm, differently from that in the literature which employs a nonlinear backpropagation network (BPN) algorithm. Moreover, the neural adaptive model is employed here in prediction mode, and not in simulation mode, as is usually the case in the literature, with a consequent quicker convergence of the speed estimation, no need of filtering the estimated speed, higher bandwidth of the speed loop, lower estimation errors both in transient and steady-state operation, better behavior in zero-speed operation at no load, and stable behavior in field weakening. A theoretical analysis of some stability issues of the proposed observer has also been developed. The OLS MRAS observer has been verified in numerical simulation and experimentally, and in comparison with the BPN MRAS one presented in the literature.     

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.