Optimum H designs under sampled state measurements

We present the complete solution to the H^∞-optimal control problem when only sampled values of the state are available. For linear time-varying systems the optimum controller is characterized in terms of the solution of a particular generalized Riccati-differential equation, with the optimum performance determined by the conjugate point conditions associated with a family of generalized Riccati differential equations. For the infinite-horizon time-invariant problem, however, the optimum controller is characterized in terms of the solution of a particular generalized algebraic Riccati equation, and the performance is determined in terms of the conjugate-point conditions of a single generalized Riccati equation, defined on the longest sampling interval. If the distribution of the sampling times is also taken as part of the general design, uniform sampling turns out to be optimal for the infinite horizon case, while for the finite horizon problem a nonuniform sampling generally leads to a better performance.     

Impulse-doublet sampling of random waveforms

For discrete time systems, the sampling rate is an important design issue. On the one hand, a sampling rate below the Nyquist rate results in spectral aliasing, on the other hand, a sampling rate chosen higher than necessary increases the computational burden. We show in this paper that aliased spectra, arising from sampling a random process below the Nyquist rate, may be completely eliminated. We show that a deterministic or random waveform that is sampled at a rate less than the classical Nyquist rate may be successfully reconstructed if two arbitrarily closely spaced samples are retained each sampling instant. A convergence proof is given for the random waveform case. We suggest a diagonally loaded maximum likelihood estimator approach to reduce the reconstruction errors resulting from timing jitter between the pairs of impulse samples as an area of future research.     

Windows 2000/XP下通用实时采集的设计与实现

首先分析了Windows2000 /XP平台上几种实时采集方案,然后综合利用Windows2000 /XP为实时应用所提供的机制,提出了一种基于非专用采集卡的高精度实时采集方案。最后通过某振动信号采集系统的实现,给出该方案的详细设计思路和实验数据,证明了其正确性与可行性。     

Design of economically optimal acceptance sampling plans with inspection error

Models to determine economically optimal quality control systems have appeared in the literature for a number of years and involved a variety of approaches. This approach is unique because of the following reasons: (1) A continuous loss function is used to quantify deviations between a quality characteristic and its target level. (2) Inspector error is modeled explicitly as is the ability to influence this error with resources. (3) The models are used to construct graphs that allow practitioners to design near optimal inspection plans with a minimal understanding of the model details.Scope and purposeDesigning economically optimal acceptance sampling plans has not been widely addressed even though sampling remains a commonly used technique in certain quality engineering systems. In this research, we develop mathematical models that can be used to design both 100% inspection and single sampling plans. Since many actual implementations involve human inspectors, an important consideration that is frequently ignored is inspection error. In this research, inspection error is explicitly included in the model as is the ability to mitigate the consequences by expending resources.For the 100% inspection case, the design parameters are the inspection tolerance and resources expended to reduce inspection error. For single sampling, the appropriate model can be used to determine the optimal inspection tolerance and resource expenditure given a sampling plan or they can be solved for the sample size and inspection number given a prescribed inspection tolerance and resource expenditure. The paper illustrates all of these uses through simple examples that can easily be modified to specific situations.     

Optimal sampling-rates and tracking properties of digital LQ and LQG tracking controllers for systems with an exogenous component and costs associated to sampling

In this paper it is shown that if costs are associated to sampling operations which are added to a performance criterion, the minimization of this new performance criterion results in a controller operated at an optimal sampling rate. This, under the assumptions that the system is periodically sampled, the applied control is kept fixed between every two sampling instances and some technical conditions are met. In case the considered planning horizon in the performance criterion is finite an algorithm is devised which calculates in a finite number of steps the optimal sampling period. It is shown that the technical conditions mentioned above are satisfied by the finite planning horizon time-varying LQG tracking problem. Since stability is a major requirement in controller design we also consider the case of an infinite planning horizon. This analysis is focused on the time-invariant digital LQ tracking problem. Given some mild regularity conditions a numerical algorithm is presented that approximates the optimal solution within any prespecified error norm. It is shown that also in this case an optimal sampling-rate exists. The algorithm for determining the optimal sampling period if the planning horizon is finite is illustrated in an economic example.     

汽车车窗防夹数据的实验获取与分析

目前汽车上常用的防夹算法并没有考虑车辆使用时可能遇到的各种环境变化所带来的影响,导致车窗防夹功能在外界条件恶劣的情况下对障碍物误判或者感受不到障碍物,造成防夹功能失效.防夹算法发挥作用的前提是一个合理准确的防夹阈值,而防夹阈值的确定是以大量的实验研究为基础.针对以上问题,基于MaIlab/Simulink和dSPACE...     

Active learning for logistic regression: an evaluation

Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural first step in providing robust solutions for active learning across a wide variety of exponential models including maximum entropy, generalized linear, log-linear, and conditional random field models. For the logistic regression model we re-derive the variance reduction method known in experimental design circles as ‘A-optimality.’ We then run comparisons against different variations of the most widely used heuristic schemes: query by committee and uncertainty sampling, to discover which methods work best for different classes of problems and why. We find that among the strategies tested, the experimental design methods are most likely to match or beat a random sample baseline. The heuristic alternatives produced mixed results, with an uncertainty sampling variant called margin sampling and a derivative method called QBB-MM providing the most promising performance at very low computational cost. Computational running times of the experimental design methods were a bottleneck to the evaluations. Meanwhile, evaluation of the heuristic methods lead to an accumulation of negative results. We explore alternative evaluation design parameters to test whether these negative results are merely an artifact of settings where experimental design methods can be applied. The results demonstrate a need for improved active learning methods that will provide reliable performance at a reasonable computational cost.     

Land surface albedo retrieval via kernel-based BRDF modeling: II. An optimal design scheme for the angular sampling

In this paper, we address some fundamental issues regarding the strategy of inversion of the class of the bi-directional reflectance distribution function (BRDF) models known as kernel-based. We examine two main causes of uncertainty affecting the predicted reflectance values. They are a contribution of the random noise term in the measurements and the model residual after adjustment. For this purpose, we formulate notions of both the experimental design for angular sampling and the kernel response function for an arbitrary set of angular measurements. The Fisher informative matrix is considered to circumscribe the optimal design problem to search for the best angular sampling. Two approaches are presented and discussed: one relates to the random noise term of the BRDF model and is independent on the land cover type (LCT); the other is impacted by the residual term of the BRDF model and is determined by the reflectance peculiarities of the different LCTs. Additionally, we explore the residual terms as a function of the LCT and we analyze the sensitivity of the reflectance predicted by the various BRDF models to the kernel response function. This is achieved by segregating regions of the angular domain that are most responsive to the measurement noise. We further determined the optimal angular sampling distribution for ground-based multi-angular data sets over different LCT and known residual terms. Then, we extend it to the more general case, ignoring the residual term. It is shown that the functions that are associated with the chosen criteria saturate after the selection of five or six observing geometries. These inversion experiments in BRDF modeling confirm the efficiency of certain angular data sets as a solution to the optimal design problem in relation to the negative influence of the redundancy effect of observations. This effect arises from having an excessive number of angular measurements in relation to the number of retrieved parameters. Another manner to prove the optimal design validity is by statistical diagnosis. We carried out a diagnosis study based on the residual standard deviation, the Fisher and R-square statistics. A fast convergence rate is observed with angular measurements determined by the optimal design.     

Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis

For damage detection, a hybrid technique consisting of two strategies is proposed. First, grey relation analysis is used to exclude the impossible damage locations such that the number of design variables can be reduced. By using two simple rules proposed in this work, all actual damage locations are included as possible damage locations and the number of design variables is effectively reduced. Secondly, a real-parameter genetic algorithm is combined with simulated annealing and adaptive mechanisms for finding the actual damages. The proposed hybrid algorithm uses only vertical nodal displacements of static responses. In addition to the ideal error-free case in which there is no difference between the experimental data and the theoretical data, the case with 5% error, in which the experimental data is simulated by adding 5% error maximum to the corresponding theoretical data, is considered for measuring error and modeling error. From the results, it is demonstrated that the proposed technique is efficient in damage identification. For the error-free case, the predicted results agree with the true solutions exactly. For the case with 5% error, the prediction obtained is also reasonable.     

Asymptotic recovery for discrete-time systems

An asymptotic recovery design procedure is proposed for square, discrete-time, linear, time-invariant multivariable systems, which allows a state-feedback design to be approximately recovered by a dynamic output feedback scheme. Both the case of negligible processing time (compared to the sampling interval) and of significant processing time are discussed. In the former case, it is possible to obtain perfect recovery if the plant is minimum-phase and has the smallest possible number of zeros at infinity. In other cases good recovery is frequently possible. New conditions are found which ensure that the return-ratio being recovered exhibits good robustness properties.     

基于虚拟仪器的多通道声发射检测系统设计与开发

针对目前商用声发射检测系统的不足,基于虚拟仪器技术设计并开发了一套多通道声发射检测系统。该系统采用通用高速同步数据采集卡实现对四通道声发射信号的采集,单通道采集频率可调且可达125MHz,采样长度可调。以LabVIEW为平台开发配套的软件,实现对数据采集过程的控制、采集数据的预处理、波形显示和数据保存等功能。利用该系统开展了一维直线和二维平面断铅实验,通过对实验数据进行处理实现了对断铅位置的定位,并从多角度分析了定位误差的来源,依据行业标准对系统的性能实施了定性与定量评价。断铅实验结果表明声发射源定位误差低于传感器阵列中最大传感器间距的5%,验证了所开发系统的可用性,其硬件条件、软件功能以及定位精确度满足行业标准。所提出的声发射检测系统设计方案、实验方法以及数据处理方法将为工业现场声发射检测系统的研发提供有益的借鉴。     

Adaptive observers for a class of uniformly observable systems with nonlinear parametrization and sampled outputs

In this paper, we propose an adaptive observer for a class of uniformly observable nonlinear systems with nonlinear parametrization and sampled outputs. A high gain adaptive observer is first designed under the assumption that the output is continuously measured and its exponential convergence is investigated, thanks to a well defined persistent excitation condition. Then, we address the case where the output is available only at (non uniformly spaced) sampling instants. To this end, the continuous-time output observer is redesigned leading to an impulsive observer with a corrective term involving instantaneous state impulses corresponding to the measured samples and their estimates. Moreover, it is shown that the proposed impulsive observer can be put under the form of a hybrid system composed of a continuous-time observer coupled with an inter-sample output predictor. Two design features are worth to be emphasized. Firstly, the observer calibration is achieved through the tuning of a scalar design parameter. Secondly, the exponential convergence to zero of the observation and parameter estimation errors is established under a well defined condition on the maximum value of the sampling partition diameter. More specifically, the observer design is firstly carried out in the case of linear parametrization before being extended to the nonlinear one. The theoretical results are corroborated through simulation results involving a typical bioreactor.     

Verification of Similar FSMs by Mixing Incremental Re-encoding, Reachability Analysis, and Combinational Checks

State space exploration is often used to prove properties about sequential behavior of Finite State Machines (FSMs). For example, equivalence of two machines is proved by analyzing the reachable state set of their product machine. Nevertheless, reachability analysis is infeasible on large practical examples. Combinational verification is far less expensive, but on the other hand its application is limited to combinational circuits, or particular design schemes. Finally, approximate techniques imply sufficient, not strictly necessary conditions.The purpose of this paper is to extend the applicability of purely combinational checks. This is generally achieved through state minimization, partitioning, and re-encoding the FSMs to factor out their differences. We focus on re-encoding. In particular, we present an incremental approach to re-encoding for verification that transforms the product machine traversal into a combinational verification in the best case, and into a computationally simpler product machine traversal in the general case.Experimental results demonstrate the effectiveness of this technique on medium-large circuits where other techniques may fail.     

基于广义遗传算法的自适应重要抽样理论

1 引言随着计算技术和软件工具的迅猛发展,具有良好普适性的数值模拟方法在许多领域开始受到了广泛的重视。由于Harbitz,Melchers,Wu和本文作者的一系列工作,基于重要抽样理论的非线性系统可靠性分析技术取得了很大的进展。尽管如此,基于重要抽样理论的非线性系统可靠性分析技术中一些带有根本性的理论问题并没有彻底解决。突出表现是,对于广义多设计点问题、寻找所有设计点的求解算法不仅没有提出,甚至没有提出过合理的求解思路。造成这一局面的原因是,多设计点问题的求解需以非连通域非线性系统全局优化问题的解决为前提,而后一问题在     

Economic model predictive control with triggered evaluations: State and output feedback

In this work, we focus on the computation load reduction in the optimization of economic model predictive control (EMPC) for nonlinear systems. Specifically, event-based triggering approach is adopted to significantly reduce the number of evaluations of the EMPC. First, we consider the case that state feedback is available and design a triggering condition based on the difference between the actual system state and its predicted value. At a sampling time, if the triggering condition is satisfied, the EMPC is re-evaluated. Subsequently, we consider the case that only output feedback is available. In this case, a robust moving horizon estimator is used to reconstruct the state information from output measurements and the corresponding triggering condition is based on the difference between the measured and predicted output as well as its time derivatives. For both cases, the EMPC is redesigned to account for potential open-loop operations. Sufficient conditions that ensure the closed-loop stability are provided for both cases. A chemical process is used to illustrate the effectiveness of the proposed designs.     

Consensus of multi-agent systems based on sampled-data control

This article studies the consensus problem in directed networks, assuming that each agent is with double-integrator dynamics and only obtains the measurements of its positions relative to its neighbours at sampling instants. We propose a protocol based on sampled-data control and derive an equivalent characterisation of the solvability of the consensus problem under this protocol. In virtue of this equivalent characterisation, we further consider two cases: fixed topology and switching topology. For the first case, we present a set of sampling periods and feedback coefficients which ensure that the protocol can solve a consensus problem. For the second case, we derive sufficient conditions for the protocol to solve a consensus problem under arbitrary switching signals and under a class of switching signals, respectively. Finally, simulations are provided to illustrate the effectiveness of the theoretical results.     

脱机手写汉字识别的最优采样特征新方法

在脱机手写汉字识别中，笔画形变是造成识别率下降的主要原因，减少笔画形变的影响是提高脱机手写汉字识别率的关键。针对上述问题，提出了最优采样特征。该特征以目前被广泛应用的方向线素特征为基础，在一定的约束条件下，通过移动采样点的位置，可以适应笔画的形变。从而减少特征的类内方差，提高特征的可分性，改进了识别性能。通过在THCHR样本集上进行实验，并对最优采样特征和方向线素特征的实验结果进行比较，验证了最优采样特征的识别率优于方向线索特征。     

A comparison of experimental designs in the development of a neural network simulation metamodel

In this case study, we investigate the effects of experimental design on the development of artificial neural networks as simulation metamodels. A simple, deterministic combat model developed within the paradigm of system dynamics provides the underlying simulation. The neural network metamodels are developed using six different experimental design approaches. These include a traditional full factorial design, a random sampling design, a central composite design, a modified Latin Hypercube design and designs supplemented with domain knowledge. The results from this case study show how much impact the experimental design chosen for the neural network training set can have on the predictive accuracy achieved by the metamodel. We compare the networks in terms of various performance measures. The neural network developed from the modified Latin Hypercube design supplemented with domain knowledge produces the best performance, outperforming networks developed from other designs of the same size.     

The linear periodic output regulation problem

The problem of asymptotic output regulation for linear systems driven by time-varying, T-periodic exosystems is considered in this paper. Necessary and sufficient condition for its solvability based on the existence of periodic solutions of differential Sylvester equations are derived. These conditions constitute a generalization to the periodic case of the celebrated algebraic regulator equations of Francis. A general algorithm for the synthesis of an error-feedback regulator is given. For the case of minimum-phase systems, it is shown that the regulator design can be carried out without the knowledge of the Floquet decomposition of the exosystem, thus extending significantly the applicability of the general result. The more challenging issue of robust regulation by error feedback is also addressed, and solved under a stronger observability condition.     

Characterization of symmetric send-on-delta PI controllers

A new event-based proportional–integral controller, based on a specific send-on-delta sampling strategy, is analyzed in this paper. In particular, necessary and sufficient conditions on the controller parameters for the existence of equilibrium points without limit cycles are given for a first-order-plus-dead-time process. These conditions can be usefully exploited for the tuning of the controller, thus making the overall design easier. Practical issues related to the controller implementation are also addressed. Simulation and experimental results are provided as illustrative examples.