Frequency domain identification of continuous-time output error models, Part II: Non-uniformly sampled data and B-spline output approximation

This paper treats several aspects relevant to the identification of continuous-time output error (OE) models based on non-uniformly sampled output data. The exact method for doing this is well known in the time domain, where the continuous-time system is discretized, simulated and the result is fitted in a mean square sense to measured data. The material presented here is based on a method proposed in a companion paper (Gillberg & Ljung, 2010) which deals with the same topic but for the case of uniformly sampled data. In this text it will be shown how that method suggests that the output should be reconstructed using a B-spline with uniformly distributed knots. This representation can then be used to directly identify the continuous-time system without proceeding via discretization. Only the relative degree of the model is used to choose the order of the spline.     

Sufficient conditions to stabilize time‐varying nonlinear sampled‐data systems via approximation

This paper investigates the problem of stabilizing a time‐varying nonlinear sampled‐data system based on its approximate models. Given a time‐varying nonlinear sampled‐data system, we make use of the approximate models of the system at each sampling instant to build a time‐varying approximate model with which an appropriate controller is designed. This paper provides some sufficient conditions under which a controller stabilizing the approximate models can also stabilize the exact sampled‐data system under any sufficiently small sampling period. With this method, we do not need to compute the complicated exact discrete‐time model of the concerned system and significantly simplify the controller design. The major contribution of this method lies in extending the available results regarding the time‐invariant sampled‐data systems to the time‐varying case. Moreover, this method enables us to analyze the influence of the precision of approximate models on the system performance. Copyright © 2016 John Wiley & Sons, Ltd.     

Identification of continuous-time AR processes from unevenly sampled data

When identifying a continuous-time AR process from discrete-time data, an obvious approach is to replace the derivative operator in the continuous-time model by an approximation. In some cases, a linear regression model can then be formulated. The well-known least-squares method would be very desirable to apply, since it enjoy good numerical properties and low computational complexity, in particular for fast or nonuniform sampling. The focus of this paper is the latter, i.e., nonuniform sampling. Two consistent least-squares schemes for the case of unevenly sampled data are presented. The precise choice of derivative approximation turns out to be crucial. The obtained results are compared to a prediction error method.     

Nearly-orthogonal sampling and neural network metamodel driven conceptual design of multistage space launch vehicle

This paper presents a new design methodology for efficient conceptual design of complex systems involving multidisciplinary and computationally intensive analysis with large number of design variables. A nearly-orthogonal sampling of design space is proposed with good space filling properties to extract maximum useful information about system behavior using much lower number of trial designs. This sampled data is then used as training data for artificial neural network, which will act as a metamodel to represent the time consuming disciplinary analyses. A stage-wise interconnection of separate neural networks is also proposed for trajectory metamodel to offset dimensionality curse of neural networks. Genetic Algorithm performs global optimization by utilizing this metamodel and subsequently sequential quadratic programming performs the local optimization utilizing exact analyses. The performance of proposed methodology is investigated in this paper for the conceptual design optimization of multistage solid fueled space launch vehicle. The results show excellent approximation of highly non-linear functions using proposed sampling and drastic reduction in overall design optimization time, due to greatly reduced number of exact disciplinary analyses.     

可编程控制器数据采样特殊性问题讨论

可编程控制器采样周期由扫描周期决定,在上位机和可编程控制器组成控制系统中,上位机以通讯方式获得可编程控制器的采样数据周期由查询采样数据的间隔时间所决定,主要从可编程控制器逻辑编程和上位机采样数据处理两个方面入手,提出解决上位机获得采样周期较短的实时采样数据一种方法。     

On the accuracy of a covariance matching method for continuous-time errors-in-variables identification

An analysis of a covariance matching method for continuous-time errors-in-variables system identification from discrete-time data is made. In the covariance matching method, the noise-free input signal is not explicitly modeled and only assumed to be a stationary process. The asymptotic normalized covariance matrix, valid for a large number of data and a small sampling interval, is derived. This involves the evaluation of a covariance matrix of estimated covariance elements and estimated derivatives of such elements, and large parts of the paper are devoted to this task. The latter covariance matrix consists of two parts, where the first part contains integrals that are approximations of Riemann sums, and the second part depends on the measurement noise variances.     

非均匀采样信号的滑动滤波方法

针对实际采样过程中出现的采样非均匀性,提出了基于连续傅里叶变换的非均匀采样信号频谱分析方法和非均匀采样信号的滑动滤波方法。将非均匀采样信号描述为不均匀采样时刻冲激函数代数和的形式,利用连续傅里叶变换得到非均匀采样信号的频谱特性,根据不同采样间隔,得到非均匀采样信号的滑动滤波方法。通过MATLAB仿真软件验证了这种非均匀采样信号分析与处理方法的正确性,将这一研究成果应用到机械抖动激光陀螺输出信号处理中,与常规的平均滤波方法相比,激光陀螺的零偏误差减少了10.1%。     

面向非匀点云拟合的RSR移动最小二乘法

针对传统的移动最小二乘法在非均匀分布的采样点集拟合中的不足,提出了影响域半径动态调整的移动最小二乘法（RSRMLS）。在传统移动最小二乘法（MLS）的基础上,根据拟合子区域采样点数据稀疏情况,该方法可自动调整MLS的半径区域大小。通过对相同数据点集的拟合比较,提出的RSRMLS拟合效果明显优于传统MLS。     

Sampled‐data output feedback stabilization for a class of switched stochastic nonlinear systems

This paper investigates a global sampled‐data output feedback stabilization problem for a class of switched stochastic nonlinear systems whose output and system mode are available only at the sampling instants. An observer is designed to estimate the unmeasurable state and thus a sampled‐data controller is constructed with the sampled estimated state. As a distinctive feature, a merging virtual switching signal is introduced to describe the asynchronous switching generated by detecting the system mode via a sampler. By choosing an appropriate piecewise Lyapunov function, it is proved that the proposed sampled‐data controller with allowable sampling period can stabilize the considered switched stochastic nonlinear systems under an average dwell‐time condition. Finally, two simulation results are presented to illustrate the effectiveness of the proposed method.     

Identification of continuous-time errors-in-variables models

A novel direct approach for identifying continuous-time linear dynamic errors-in-variables models is presented in this paper. The effects of the noise on the state-variable filter outputs are analyzed. Subsequently, a few algorithms to obtain consistent continuous-time parameter estimates in the errors-in-variables framework are derived. It is also possible to design search-free algorithms within our framework. The algorithms can be used for non-uniformly sampled data. The asymptotic distributions of the estimates are derived. The performances of the proposed algorithms are illustrated with some numerical simulation examples.     

A function space approach to sampled data control systems andtracking problems

This paper presents a new framework for hybrid sampled data control systems. Instead of considering the state only at sampling instants, this paper introduces a function piece during the sampling period as the state and gives an infinite-dimensional model with such a state space. This gives the advantage that sampled data systems with built-in intersample behavior can be regarded as linear, time-invariant, discrete-time systems. As a result, the approach makes it possible to introduce such time-invariant concepts as transfer functions, poles, and zeros to the sampled data systems even with the presence of the intersample behavior. In particular, tracking problems can be studied in this setting in a simple and unified way, and ripples are completely characterized as a mismatch between the intersample reference signal and transmission zero directions. This leads to the internal model principle for sampled data systems     

Spectral analysis of nonuniformly sampled data – a review

In this paper, we present a comprehensive review of methods for spectral analysis of nonuniformly sampled data. For a given finite set of nonuniformly sampled data, a reasonable way to choose the Nyquist frequency and the resampling time are discussed. The various existing methods for spectral analysis of nonuniform data are grouped and described under four broad categories: methods based on least squares; methods based on interpolation techniques; methods based on slotted resampling; methods based on continuous time models. The performance of the methods under each category is evaluated on simulated data sets. The methods are then classified according to their capabilities to handle different types of spectrum, signal models and sampling patterns. Finally the performance of the different methods is evaluated on two real life nonuniform data sets. Apart from the spectral analysis methods, methods for exact signal reconstruction from nonuniform data are also reviewed.     

基于自回归技术的自适应服务质量测量方法

SLA管理已经成为服务商和用户关心的重要内容。在进行服务质量管理时，服务质量的测量是其他管理功能的基础。本文从采样的角度分析了服务质量测量和传统网络测量的异同点，给出了服务质量采样频率的理论下限。在讨论已有方法不足的基础上，提出了基于预测的自适应采样算法，该算法在采样区间内随机确定采样的具体时间点，并结合自回归预测方法对即将采样的结果进行预测，在此基础上以一定的概率接受预测结果，放弃对准确预测时间点的采样，从而减少了采样次数，降低了对服务的影响。最后的实验结果说明了算法的有效性。     

Sampled‐data control of a class of uncertain switched nonlinear systems in nonstrict‐feedback form

This paper investigates the stabilization problem of sampled‐data output feedback for a class of uncertain switched nonlinear systems in nonstrict‐feedback form. An observer is designed to estimate the unmeasured states, and a sampled‐data controller is obtained by discretizing the virtual controller that is constructed via the dynamic surface control method. It is proved that the designed sampled‐data controller can render all states of the resulting closed‐loop system to converge to a neighborhood of the origin for the arbitrary switching signal, and an allowable sampling period is also given. Finally, 2 examples are presented to illustrate the effectiveness of the proposed method.     

基于互信息的医学图像配准中改进的采样方法

研究了以互信息为相似性测度的医学图像配准方法,在互信息计算过程中,对图像数据的采样提出了一种基于信息熵的采样方法。这种方法是将图像分成一定数量的小方块,计算每一小方块的熵,根据熵值的大小对方块进行分类,不同的类设置不同的采样因子:熵值大的方块对应的采样因子大,熵值小的方块对应的采样因子小。通过实验证明,该方法能够折中配准的精度和速度,适用于医学图像配准的实时处理。     

Multirate sampled‐data stabilization for a class of low‐order lower‐triangular nonlinear systems

This paper investigates the problem of sampled‐data controller design for a class of lower‐triangular systems in the p‐normal form (0<p<1). A multirate digital feedback control scheme is proposed to achieve the global strong stabilization of the sampled‐data closed‐loop system under some assumptions. In the design of the controller, the input‐Lyapunov matching strategy and multirate control approach are combined to obtain better stabilizing performance. Unlike the design method based on the approximate discrete‐time model, our controller is obtained from the exact discrete‐time equivalent model, which does not need to be computed completely. The approximate multirate digital controllers are proved to be effective in the practical implementation. It is shown that, compared with the emulated control scheme, our controller may provide faster decrease of Lyapunov function for each subsystem. This will lead to allow large sampling periods. An illustrative example is provided to verify the effectiveness of the proposed control scheme.     

Augmented TP model transformation‐based parallel distributed compensation control design

The usual sampled hyper grid points of uniform sampling method are distributed equally in the TP model transformation, thus, the sampling results often omit the local extrema when the sampling step is not fine‐tuned. Then the resultant tensor which is used for controller design can not fully cover the state space, although the gain which is the feasible solution of the linear matrix inequalities. In this paper, we proposed a non‐uniform sampling method for tensor product model transformation, local extrema are considered in the sampling step, while the sampling step can vary dynamically for different function entries. In this paper, TP model transformation‐based parallel distributed compensation (PDC) controller is extended in three folds: (i) The existing TP model transformation‐based PDC controller with uniform sampling method is extended to TP model transformation‐based PDC tracking controller with the uniform sampling method and an extended signal. (ii) A new TP model transformation‐based PDC tracking controller is proposed based on a new sampling method, that is, the Hammersley sampling method. (iii) TP model transformation‐based PDC tracking controller is also proposed based on the non‐uniform sampling method. The proposed adaptive TP model transformation‐based PDC tracking controller is able to enhance the performance of the TP model transformation‐based PDC controller, and the adaptive TP model transformation‐based PDC controller obtains the best results due to the nearly exact sampling of the system.     

DoS攻击下基于多率采样的多智能体系统安全一致性

本 文 研 究 了 一 类 带 有 多 率 采 样 的 线 性 多 智 能 体 系 统(Multiagent Systems, MASs)在 拒 绝 服 务(Denial-of-Service, DoS)攻击下的安全一致性控制问题, 其中DoS攻击通常阻断智能体之间的信息传输. 本文将多 率采样在网络化控制系统中的结果推广到了多智能体系统, 并考虑了非理想通信网络环境. 首先, 通过引入一个匹 配机制来同步由多率采样引起的智能体不同状态分量的采样数据. 然后, 在DoS攻击下, 针对带有多率采样的线 性MAS提出了一个基于多率采样的安全一致性控制器. 通过使用李雅普诺夫稳定性理论和切换系统方法, 获得了 包含DoS 攻击持续时间以及攻击频率的安全一致性充分条件. 最后, 给出了一个仿真例子来验证所提方法的有效 性, 并给出了多率采样与单率采样机制的性能对比分析.     

Synchronization of Switched Neural Networks with Time‐varying Delays via Sampled‐data Control

This paper investigates sampled‐data synchronization control of switched neural networks with time‐varying delays under average dwell time. Based on the delay system method, the sampled‐data synchronization system is proposed with time‐varying delays and input delays in the unified framework for switched neural networks. By constructing a suitable Lyapunov‐Krasovskii functional and free‐weighting matrix, the relationship between the average dwell time and the maximum sampling interval is revealed to form delay‐dependent exponentially synchronization criteria. The desired mode‐dependent controller under the maximum sampling interval and decay rate is designed. Finally, two numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed techniques.     

基于主成分分析的MWC采样数据压缩方法

调制带宽转换器(Modulated Wideband Converter,MWC)是针对稀疏多频带信号提出的一种有效的亚奈奎斯特采样方法。实际的MWC设计过程中需要考虑硬件实现、重构精度等问题,不可避免地会导致采样数据中存在一定的冗余。该文首次对MWC系统产生采样冗余的原因以及采样数据特点进行深入分析,并提出了一种基于主成分分析(principal component analysis, PCA)的MWC采样数据压缩方法。该方法首先对MWC采样数据进行PCA变换,将其中的大部分能量集中在少量的主成分中,进而通过保留少量主成分,并对其进行进一步量化编码的方式来实现对MWC采样数据的压缩。实验结果表明,PCA变换对MWC采样数据的能量集中效果优于小波变换和离散余弦变换,且该压缩方法能够在保证重构精度高于90%的前提下将MWC系统的采样数据量压缩至原来的1/8以下。