Filtered gradient active fuzzy neural network noise control in an enclosure backed by a clamped plate

The performance of conventional linear algorithms in active noise control applications deteriorates facing nonlinearities in the system mainly because of loudspeakers. On the other hand, fuzzy logic and neural networks are good candidates to overcome this drawback. In this paper, the acoustic attenuation of noise in a rectangular enclosure with a flexible panel and five rigid walls is presented both theoretically and experimentally using filtered gradient fuzzy neural network (FGFNN) error back propagation algorithm in which the secondary path effect is implemented in derivation of updating rules. Considering this effect in updating rules leads to faster convergence and stability of the active noise control system. On the other hand, the primary path in the investigated system comprises an identified nonlinear model of loudspeaker inside the aforementioned box, parameters of which vary with the input current. The loudspeaker is identified using series‐parallel neural network model identification method. As a comparison, the performance of filtered‐x least mean squares and FGFNN algorithms are compared. It is observed that FGFNN controller exhibits far better results in the presence of loudspeakers with nonlinear behavior in primary path.Copyright © 2012 John Wiley & Sons, Ltd.     

Steiglitz–McBride adaptive notch filter based on a variable‐step‐size LMS algorithm and its application to active noise control

This paper proposes a new Steiglitz–McBride (SM) adaptive notch filter (SM‐ANF) based on a robust variable‐step‐size least‐mean‐square algorithm and its application to active noise control (ANC). The proposed SM‐ANF not only has fast convergence but also has small misadjustment. The variable‐step‐size algorithm uses the sum of the squared cross correlation between the error signal and the delayed inputs corresponding to the adaptive weights. The cross correlation provides robustness to the broadband signal, which plays the role of noise. The proposed SM‐ANF is computationally simpler than the existing Newton/recursive least‐squares‐type ANF. The frequency response of the new SM‐ANF has a notch depth of about ?25 dB (for each of the three frequencies considered) and has spectral flatness within 5 dB (peak to peak). This robust notch filter algorithm is used as an observation noise canceller for the secondary path estimation of an ANC system based on the SM method. The ANC with proposed SM‐ANF provides not only faster convergence but also an 11‐dB improvement in noise attenuation over the SM‐based ANC without such a SM‐ANF. Copyright © 2015 John Wiley & Sons, Ltd.     

A narrowband active noise control system with reference synthesis

Most active noise control (ANC) algorithms require a model of the secondary path (SP). If there are large changes in the SP, online SP modeling algorithms may not be able to follow the changes quickly enough to prevent divergence. In this work, the mirror‐MFxLMS (MMFxLMS) is adapted to narrowband ANC, and the resulting algorithm is analyzed. The MMFxLMS is an algorithm similar to the MFxLMS and FxLMS but has online SP modeling; it is stable for any SP estimate and does not require auxiliary noise. In addition, a technique to alternate between MFxLMS and MMFxLMS is proposed, resulting in the M³FxLMS algorithm, which does not sacrifice performance and assures stability. However, the M³FxLMS requires a reference signal that is not available in feedback ANC. Feedback ANC is important in many practical cases, for instance, in ANC headphones. To solve this problem, a technique is proposed to synthesize a reference formed by a sum of sinusoids using measurements from the error sensor signal. This technique uses the fast Fourier transform to obtain estimates of the harmonic frequencies of the periodic noise components, and then refines and tracks these using a phase‐locked loop (PLL). The resulting algorithm enables a stable ANC system, even under large and sudden changes in the SP. The proposed PLL is also new and has a lock‐in range equal to the full band of the system.     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

Frequency domain analysis of the mirror-modified filtered-x least mean squares algorithm with low ambient noise

The mirror-modified filtered-x least mean squares (MMFxLMS ) algorithm is a variation of the FxLMS algorithm with online secondary path modeling that cannot diverge due to secondary path modeling errors. However, problems may occur when the ambient noise is not limited due to insufficient modeling power. This work shows that under a frequency domain analysis without ambient noise, the MMFxLMS algorithm is always stable, and expressions for the maximum residual noise level at any given time are obtained. It is also shown that, under the same context, convergence to the minimum residual noise is guaranteed. Still, convergence can be much slower for high secondary path modeling errors than that of the LMS or MFxLMS algorithms. Simulations confirm these results.     

基于麦克斯韦应力法的双绕组无轴承开关磁阻电机新型数学模型

现有的双绕组无轴承开关磁阻电机麦克斯韦应力法数学模型忽略了相互垂直方向上径向悬浮力的耦合,但当转子偏离定转子齿极中心对齐位置较大时存在较强的耦合,故现有模型难以满足高精确度稳定悬浮控制要求。选择一种计及相互垂直方向悬浮力耦合的新积分路径,建立了基于麦克斯韦应力法的双绕组无轴承开关磁阻电机新型数学模型,同时为计及电机磁路的饱和特性,采用最小二乘法对铁心材料的非线性磁化曲线进行拟合,求解考虑磁饱和的各气隙磁密。通过与有限元仿真结果对比,验证了所建模型不仅揭示了相互垂直方向上径向悬浮力的耦合关系,考虑了磁饱和特性,且具有精确度良好、计算量小等优点,为实现高精度稳定悬浮控制奠定了基础。     

Development and convergence analysis of new least‐mean‐square‐based algorithm equipped with exponential‐decay step size and disturbance compensation for active noise control

In real‐world active noise control (ANC) applications, disturbance can be picked up by error sensors and significantly degrade the steady‐state ANC performance. This study proposes two techniques in combination with a least‐mean‐square (LMS) based ANC algorithm, named normalized filtered‐x LMS/commutation error (NFxLMS/CE) algorithm, to deal with the disturbance that is independent of a reference signal. A new stochastic method to analyze convergence properties of the NFxLMS/CE algorithm under influence of the disturbance is first established. Given that the reference signal is persistently exciting of sufficient order, exponential convergence of the algorithm is derived with a step‐size condition. An exponential‐decay step size (EDSS) is then proposed to obtain a new ANC algorithm referred to as EDSS‐NFxLMS/CE algorithm. In addition, a disturbance‐compensation (DC) technique is developed for the EDSS‐NFxLMS/CE algorithm to obtain an EDSS‐NFxLMS/CE_DC algorithm such that the influence of the disturbance can be reduced. It is shown that the EDSS‐NFxLMS/CE_DC algorithm is exponentially convergent. Moreover, computer simulations show that the EDSS‐NFxLMS/CE_DC algorithm can achieve a better ANC performance in terms of convergence rate and level of noise reduction as compared with that using the EDSS‐NFxLMS/CE algorithm without DC and that using NFxLMS/CE_DC algorithm of constant step sizes. These results support the effectiveness of the proposed techniques and EDSS‐NFxLMS/CE_DC algorithm. Copyright © 2012 John Wiley & Sons, Ltd.     

ANC系统次级通道背景噪声的处理方法

由于电力变压器ANC系统次级通道背景噪声的影响,使其辨识参数不能反映次级通道本身特性,影响整个降噪系统的降噪效果和收敛性能。论文首先以Fx-LMS算法为例,分析了辨识误差对算法收敛系数的影响。然后提出了一种次级通道背景噪声的去噪新方法,即将分时测量法与信号相关性分析结合,极大限度抑制了与激励信号频段相近的背景噪声影响。最后通过仿真计算证明去除背景噪声的效果明显,次级通道辨识精度显著提高。     

基于神经网络非线性模型的多级工作点阶跃响应扩展DMC预测控制

利用前馈神经网络建立对象的非线性预测模型,在不同工作点对阶跃响应,建立它的局部线性模型,利用隶属函数进行加权得全局线模型,利用全局线性模型进行滚动优化,利用非线性预测模型校正线模型,实现非线性预测控制,仿真表明,文中提出了方法控制效果良好,满足实时要求。     

考虑磁饱和的感应电机MTPA转矩控制

感应电机的最大转矩电流比(MTPA)控制算法根据需求转矩调节磁链幅值,以达到转矩与定子电流比值的最大化。因磁链随转矩的变化而变化,不再像传统控制算法保持磁场幅值恒定,磁场非线性饱和效应会对控制系统产生影响。针对此问题,该文提出考虑磁饱和特性的MTPA转矩控制系统。根据需求转矩以及饱和模型,通过线性搜索寻找最优磁链目标值,并在计算励磁电流时考虑d轴转子电流的动态特性,用降阶观测器观测转子磁链,并在设计滑模磁链控制器以及反步法设计转矩控制器时考虑观测误差,以确保控制器参数的设置能达到全局稳定。仿真和实验结果证明了所设计控制器的有效性。     

永磁直线同步电动机改进全局滑模变结构控制研究

针对滑模控制永磁直线同步电动机（PMLSM）不能实现有限时间控制以及全局滑模控制（GSMC）不能实现快速收敛的问题,提出了一种改进型GSMC算法来设计直线伺服速度控制器。该算法采用了由非线性项、线性项相结合的动态滑模面以及等速趋近律,非线性滑模面衰减函数由3个指数函数项组成一阶可导函数, 并能在有限时间内衰减为零。仿真结果表明,改进GSMC不仅使系统具有全局鲁棒性,且能在保证高跟踪精度的同时加快响应速度和抗干扰能力。     

Stator-flux-based vector control of induction machines in magneticsaturation

In many variable-torque applications of induction machines, it is desirable to operate the machine at high flux levels, thus allowing the machine to produce higher torques. This can lead to saturation of the main flux path, introducing cross-coupling effects which can severely disrupt the performance of controllers dependent on knowledge of the machine's magnetic parameters. Stator flux-oriented torque-control schemes need not depend on the magnetic parameters of the machine and, hence, are potentially more robust and easier to implement in magnetic saturation than rotor-flux-oriented control. In this paper, the authors present and analyze a stator-flux-oriented torque-control scheme. This controller only requires knowledge of the stator voltage, stator current, and stator resistance. An analytical expression for the maximum achievable torque output of the machine using a linear magnetics model is compared with values calculated using a nonlinear magnetics model incorporating saturation of the main flux path and is shown to be a good approximation at high flux levels, when the main flux path is heavily saturated. Experiments carried out on a 3 hp 1800 RPM wound-rotor induction machine show smooth operation of the control scheme at torque levels up to at least four times rated torque     

一种基于在线建模的自适应有源噪声控制快速算法

针对次级通路时变条件下的快速算法展开研究.首先分析了已有的基于次级通路在线建模的FxLMS算法,同时给出了带策略选择的权系数局布迭代算法;在此基础上,提出了一种针对次级通路在线建模的带策略选择的局布迭代有源控制快速算法.给出了该快速算法的实现步骤;比较了不同算法的运算量;就次级通路为时变与非时变两种情况下对算法进行了计算机仿真.结果表明,本文提出的快速算法能有效地降低原算法的运算量,提高原算法次级通路在线建模部分的性能,并可在稳态时取得较原算法更优的降噪量.     

卡尔曼滤波回归统计模型及工程应用分析

将统计模型的回归系数看做状态向量，统计模型视为观测方程，利用卡尔曼滤波算法实现了统计模型的建模。该建模方法不仅便于实时处理、计算机实现、节省机时，而且由于卡尔曼滤波属于线性最小方差估计，所以相比最小二乘建模方法可望获得更高的建模精度。工程实践也验证了卡尔曼滤波回归统计模型的上述特性。     

Extended fuzzy function model with stable learning methods for online system identification

The aim of the online nonlinear system identification is the accurate modeling of the current local input‐output behavior of the plant without using any prior knowledge and offline modeling phase. It is a challenging task for many intelligent systems when used for real‐time control applications. In this paper, we propose a novel computationally efficient extended fuzzy functions (EFF) model for system identification of unknown nonlinear discrete‐time systems. The main contributions are to introduce an effective quasi‐nonlinear model (EFF) and propose adaptive learning rates (ALR) for recursive least squares (RLS) and gradient‐descent (GD) methods. The asymptotic convergence of the modeling errors and boundedness of the parameters are proved by using the input‐to‐state stability (ISS) approach. Numerical simulations are performed for Box–Jenkins gas furnace system and a nonlinear dynamic system. The benefits of its accuracy, stability and simple implementation in practice indicate that EFF model is a promising technique for online identification of nonlinear systems. Copyright © 2010 John Wiley & Sons, Ltd.     

Runtime data‐based characterization and torque estimation of switched reluctance motors

High‐performance control of a switched reluctance (SR) motor strictly requires an accurate nonlinear motor model as well as an appropriate control strategy. Since recent modeling methods are either complex or not accurate enough, this paper focuses on overcoming such drawdacks. An experimental method is proposed to collect useful data for modeling a 6/4 SR motor at running condition. Nonlinear modeling is done by an algorithm, which also enables simple static torque computation. The algorithm presents not only the static torque data but also an estimator model for instantaneous torque estimation during real‐time operation, which is very important for most torque control strategies. The nonlinear model is experimantally tested, and its accuracy verified. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于低分辨率传感器的高性能道闸控制算法研究

针对采用低分辨率位置传感器道闸方波控制算法运行过程中出现的抖振、砸杆与回落现象,提出了一种基于三闭环矢量控制的高性能道闸控制算法。采用基于转子数学模型(SERF)的速度估测器获取转子速度信息,从而得到转子位置信息;对道闸系统精确建模计算确定最优的三环PID控制参数,从而实现道闸伺服系统三闭环矢量控制算法。实验结果表明,控制算法能够有效地解决道闸运行过程中的砸杆、抖振等问题,提高了道闸系统的控制性能。     

电力负荷模型结构的样条函数描述

提出了用一系列简单函数分段或分区描述大幅度电压变化情况下负荷、变结构负荷和复杂非线性负荷特性的样条函数模型。介绍了一元样条函数模型参数辨识的样条最小二乘法,提出了确定样条函数模型中多项式系数和内节点数值的参数辨识的2层优化算法,其中内层优化问题为内节点数量和数值确定情况下的多项式系数的辨识,外层优化问题为内节点数值的辨识,问题求解采用步长加速法。通过改变样条函数模型中多项式的阶次、内节点数目和内节点数值,可用样条函数模型灵活方便地描述许多复杂的电力负荷特性,并且具有传统数学模型的简单结构和形式,为模型结构或形式未知情况下的负荷建模提供了新的途径。仿真建模实例证明了所提模型与算法的有效性。     

内置式永磁同步电机精确参数的MTPA控制

内置式永磁同步电机（IPMSM）由于转子磁路不对称,最大转矩电流比（MTPA）控制可充分利用该特性提高电机带载能力。在实际控制中,IPMSM的电气参数会因磁饱和、温度变化而发生波动,导致实际MTPA控制偏离预定轨迹,无法实现精确控制。针对参数变化,在电动汽车中MTPA控制多采用查表法,但制作查询表格过程复杂且耗时。针对上述问题,文中采用遗忘因子递推最小二乘法对电机参数进行在线辨识,并将得到的实时参数用于MTPA控制,提高了鲁棒性和准确性;运用MTPA控制下定子电流与交、直轴电流的关系得到交、直轴电流给定值,该控制策略原理简单,易于实现。通过MATLAB/Simulink搭建控制系统模型进行了仿真研究,其结果验证了所提方法的准确性和有效性。     

基于RBFN-AFS的开关磁阻电机非线性模型与动态仿真

针对开关磁阻电机(SRM)电磁特性存在饱和非线性、多变量、强耦合的特点,提出了一种基于径向基函数网络的自适应模糊系统(RBFN-AFS)建立SRM模型并进行动态仿真的新方法.该方法在实测SRM磁链和转矩特性的基础上,采用递阶自组织学习(HSOL)算法对RBFN-AFS网络进行学习训练,使网络从样本数据中估计出未知的模糊规则,并在学习训练过程中不断更新和修正网络隐层节点参数矢量和连接权值,最终实现磁链与转矩对转子位置角和相电流的非线性映射关系,与其他建模方法相比,该模型具有更快的计算速度和更好的泛化能力.将基于RBFN-AFS网络的电流-磁链和转矩模型应用于SRM调速系统的动态仿真分析中,通过仿真与实验比较,此方法能够很好地预测SRM的动态和稳态运行特性.这种基于RBFN-AFS的建模方法为实现SRM的各项性能分析和各种实时控制提供了一种新的思路.