Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

For the multivariate equation‐error moving average system, a multivariate maximum likelihood multi‐innovation extended stochastic gradient (M‐ML‐MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M‐ML‐MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.     

The innovation algorithms for multivariable state‐space models

This paper derives the input‐output representation of the dynamical system described by a linear multivariable state‐space model and the corresponding multivariate linear regressive model (ie, multivariate equation‐error model). A projection identification algorithm, a multivariate stochastic gradient identification algorithm, and a multi‐innovation stochastic gradient (MISG) identification algorithm are proposed for multivariate equation‐error systems by using the negative gradient search and the multi‐innovation identification theory. The convergence analysis of the MISG algorithm indicates that the parameter estimation errors converge to zero under the persistent excitation condition. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.     

基于动态衰减网络和算法的图像识别

针对在大样本数据集下,梯度下降法长期性存在着容易收敛到局部最优和收敛速度慢等问题,通过改变网络结构和梯 度下降过程,提出了一种动态衰减网络和动态衰减梯度下降算法。 在现有网络的基础上,层与层的每个神经元之间增加一条衰 减权重,同时在梯度下降过程中引入了衰减权重项。 衰减权重值随着迭代不断衰减,最终趋于 0。 由于衰减权重项的增加,可 以在梯度下降的前期加快梯度下降速度和收敛速度,同时可以避免越过最优解和在最优解附近振荡,提高了网络获得最优解的 概率。 通过 MNIST 、CIFAR-10 和 CIFAR-100 数据集的实验结果证实,所提出的动态衰减网络和算法,相比原始网络使用 Adam 和动量随机梯度下降法,测试准确度分别提高了 0. 2% ~ 1. 89%和 0. 75% ~ 2. 34%,同时具有更快的收敛速度。     

基于MISG算法的风力发电机液压变桨系统故障诊断

针对受状态延时影响的风机变桨系统故障诊断,提出了一种基于多新息随机梯度(MISG)的故障诊断方法。该方法将复杂系统转化为状态空间模型,并建立系统辨识模型。将新息标量扩展成新息向量改善算法精度,利用系统发生故障引起参数改变的特征,算法对风机状态延时变桨系统完成参数估计,将系统故障诊断问题转换为系统辨识问题。仿真所得结果验证该方法可以达到诊断风机状态延时变桨系统故障的目的。     

Parameter estimation for time-delay systems based on the frequency responses and harmonic balance methods

Two parameter estimation methods for linear time-delay systems are proposed based on the frequency responses and the harmonic balance methods. One is the stochastic gradient gradient-based iterative (SG-GI) algorithm and the other is the recursive least squares gradient-based iterative (RLS-GI) algorithm. These two methods can estimate the unknown parameters and the unknown time delays simultaneously by giving sinusoidal signals with different angular frequencies. Furthermore, a comparative study reveals that the RLS-GI algorithm is more effective than the SG-GI algorithm. The effectiveness of the proposed algorithms is illustrated by a numerical example.     

基于一致性原理和梯度下降法的微电网完全分布式优化调度

针对基于经典一致性算法的微电网优化调度中存在需要集中处理器对可控单元进行信息采集并做求和处理等问题,提出一种基于一致性原理和梯度下降法的完全分布式优化调度策略。该算法的一致性原理由一致项和调整项两部分组成。其中,一致项选择增量成本作为一致性算法的状态变量进行迭代计算,调整项根据功率偏差进行修正,使其满足约束条件。通过将梯度下降法和一致性原理相结合,实现微电网发电成本的优化并提高收敛速度。并且通过可控单元与相邻可控单元之间局部信息交互更新参数,应对拓扑结构的灵活变化。最后基于IEEE-14节点系统和IEEE-39节点系统仿真验证所提方法的有效性和可行性。     

Chaotic system identification using Wiener‐LSSVM model

In this paper, a new method is proposed for identifying chaotic system based on a Wiener‐least squares support vector machine (Wiener‐LSSVM) model. The model consists of a linear dynamic subsystem followed by a static nonlinear function, which is represented by LSSVM in this paper. The parameters of the linear dynamic part and those of LSSVM are estimated simultaneously by solving a set of linear equations using the least squares (LS) method. The proposed method incorporates partial structure information into the identification process and does not assume that the parameters of linear dynamic part are known. On the other hand, the LS algorithm is more efficient than gradient‐descendent‐based algorithms for estimating the parameters of Wiener‐LSSVM. Three identification examples are given to validate the effectiveness of the proposed method. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Modular design of adaptive robust controller for strict-feedback stochastic nonlinear systems

A modular approach of the estimation-based design in adaptive linear control systems has been extended to the adaptive robust control of strict-feedback stochastic nonlinear systems with additive standard Wiener noises and constant unknown parameters. By using It?’s differentiation rule, nonlinear damping and adaptive Backstepping procedure, the input-to-state stable controller of global stabilization in probability is developed, which guarantees that system states are bounded and the system has a robust stabilization. According to Swapping technique, we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed. Transient performance of the system is estimated by the norm of error. Results of simulation show the effectiveness of the control algorithms. The modular design, which has a concise hierarchy, is more flexible and versatile than a Lyapunov-based algorithm. __________ Translated from Journal of University of Science and Technology of China, 2004, 34(4): 495–503 (in Chinese)     

永磁交流伺服系统的抗扰动自适应控制

在某些高性能的驱动场合,惯量扰动的存在是造成伺服系统性能变差的重要原因。现提出一种具有抗扰动作用的自适应控制方法,采用梯度算法对系统的惯量进行在线辨识,并依据极点配置原则对速度控制器参数(速度环的比例系数Kpω和积分系数Kiω)进行实时调整,文中给出了理论分析。以一台面贴式永磁同步电机为对象进行了惯量辨识和自适应性能的仿真分析和实验验证,结果表明本文提出的基于梯度算法进行在线惯量辨识及调整的自适应算法是有效的。     

二维云模型在线优化永磁同步电机无传感器电压模型控制

提出一种基于二维云模型在线优化智能PID的永磁同步电机无传感器电压模型控制算法。在虚拟γ-δ旋转坐标系下利用实际电压值与理想电压值之差对电机转速误差进行实时校正,获取到相对准确的电机转速以及转子位置估计。同时,设计了一种具有参数动态实时自整定功能的智能PID对上述电压模型控制中的转速、电流进行实时调节。通过引入混沌粒子群算法离线优化控制参数,使整个控制系统处于优化状态,再利用二维云模型在线调节模块对其实时整定。实验结果表明,该控制算法能够准确估计出电机转速以及转子位置,对电机内部参数摄动以及外部负载扰动具有较强的鲁棒性。     

基于小波神经网络PID的永磁同步电机转速控制

提出了基于小波神经网络PID的永磁同步电机(PMSM)转速控制策略。根据系统运行参数的变化,采用三层前馈式人工神经网络,基于梯度下降纠正误差法在线训练实时更新PID参数值。采用小波神经网络和增量式PID共同构成转速环控制器。建立PMSM数学模型,设计PMSM速度环控制器,构建S函数,对控制算法进行仿真试验,验证了该控制算法的先进性。试验结果表明,所提控制策略比传统PID转速控制具有更好的动态性能和抗干扰能力。     

基于观测器的异步电机随机系统模糊反步位置跟踪控制

提出了一种基于观测器的异步电机随机系统模糊反步位置跟踪控制方法:通过构造降维观测器估计转子角速度;采用模糊逻辑系统逼近系统模型中的未知随机非线性函数。利用动态面控制技术解决传统反步设计中存在的"计算爆炸"问题。仿真结果表明:所提出的控制方法可以克服随机扰动的影响,并且确保跟踪误差收敛到足够小的原点邻域内。     

A continuous-time least mean-phase adaptive filter for power system frequency estimation

The mean squared error criterion is widely used in the literature. However, there are applications where the squared error is not the primary parameter affecting the performance of a system. In this paper, we introduce a cost function that is based on the phase error. The criterion is useful for applications where the performance depends primarily on the phase of the estimated (recovered) signal. A continuous-time adaptive filter is then developed using the proposed criterion in stochastic differential equation formalism. The proposed adaptive filter is used to estimate power system frequency where the formulated structure is very simple. The three-phase voltages are converted to a complex form for processing by the proposed algorithm. The performance of the new algorithm is studied through simulations at different situations of the power system.     

电力工业的非参数随机前沿面分析

随机前沿面模型是评价决策单元投入产出效率的重要方法之一,但模型中参数的求解和误差项的分解比较困难,寻求简洁有效算法是研究的重点。对随机前沿面模型构造过程中误差项的处理进行了研究,采用非参数方法对随机前沿面模型的误差项进行处理,然后利用最小二乘法进行回归分析并建立随机前沿面模型,有效地解决了随机前沿面模型中误差项分解和计算的困难。电力工业的实证研究表明,基于非参数处理的随机前沿面模型给出了具体函数形式,模型求解变得直观和容易,而且效率评价值靠前的企业是那些成功建立现代企业制度的企业,说明了方法的合理性。     

利用扩张状态观测器的交流永磁同步电机控制

为了提高交流永磁同步电机控制系统的控制品质,保证系统在内外各种扰动的影响下仍能保持快速、无超调、高精度的控制品质,提出了一种改进的控制方式。首先,根据电机模型的特点,分析了影响系统控制品质的因素,包括电流环的动态耦合、速度环的未知扰动等。然后,在介绍扩张状态观测器的原理及功能的基础上,设计了三个扩张状态观测器分别估计速度环和电流环的总扰动,进而设计出具有扰动前馈补偿和误差反馈律相结合的复合控制器。最后仿真和实验均证明了改进的控制策略的有效性。实验结果表明,使用同一组控制器参数,改进的控制策略能够同时实现高低速的高品质动态响应。设定速度为600 r/min时,与传统PI控制器相比,调节时间从0.54 s缩短为0.09 s,速度稳态误差RMS值从2.2 r/min降为1.6 r/min;设定速度为0.01 r/min时,改进的控制策略仍能够保持电机平稳运行,且位置误差在±1码。该控制策略能够满足交流永磁同步电机高精度控制的要求。     

基于磁链与负载观测器的异步电机反步滑模控制

针对参数摄动及负载转矩未知且变化会影响异步电机速度控制的问题,设计了一种改进型指数趋近律的滑模转子磁链观测器,用于估算电机转子磁通值,同时将磁通观测值用于负载转矩的估计。引入磁链误差、转速误差,通过反步滑模控制算法,将转子磁通与负载转矩的观测值用于异步电机控制。在转子电阻摄动与负载转矩未知且变化的情况下,将该控制策略与自适应反步控制方法进行实验结果对比。实验结果表明,该控制策略的响应速度快且跟踪精度高,提高了异步电机速度控制系统的抗干扰性能。最后,提出一种转速软给定方法,实验表明该方法能有效抑制转速变化时刻定子电流与电磁转矩的急剧增加,进一步改善了电机系统的性能。     

Iterative parameter and order identification for fractional-order nonlinear finite impulse response systems using the key term separation

This article considers the parameter estimation for a fractional-order nonlinear finite impulse response system with colored noise. For the fractional-order systems, the challenge and difficulty are to identify the order and parameters of the systems simultaneously under colored noise disturbances. In order to reduce the problem of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the separation of the key term separation. A key term separation auxiliary model gradient-based iterative algorithm is derived by using the negative gradient search. Meanwhile, to achieve the higher estimation accuracy, we propose a key term separation auxiliary model multiinnovation gradient-based iterative algorithm by utilizing the multiinnovation theory. Finally, the simulation results test the effectiveness of the proposed algorithms.     

Real‐time identification of nonlinear multiple‐input–multiple‐output systems with unknown input time delay using Wiener model with Neuro‐Laguerre structure

In this article, a real‐time block‐oriented identification method for nonlinear multiple‐input–multiple‐output systems with input time delay is proposed. The proposed method uses the Wiener structure, which consists of a linear dynamic block (LDB) followed by a nonlinear static block (NSB). The LDB is described by the Laguerre filter lattice, whereas the NSB is characterized using the neural networks. Due to the online adaptation of the parameters, the proposed method can cope with the changes in the system parameters. Moreover, the convergence and bounded modeling error are shown using the Lyapunov direct method. Four practical case studies show the effectiveness of the proposed algorithm in the open‐loop and closed‐loop identification scenarios. The proposed method is compared with the recently published methods in the literature in terms of the modeling accuracy, parameter initialization, and required information from the system.