Fastest recovery after feedback disruption

The problem of quickly reducing operating errors during recovery from a feedback disruption is considered. The objective is to design controllers that reduce operating errors as quickly as possible, once feedback has been restored. It is shown that robust optimal feedback controllers that achieve this objective do exist. Furthermore, it is shown that the performance of optimal controllers can be approximated as closely as desired by controllers that generate bang–bang input signals for the controlled system. Controllers that generate bang–bang signals are relatively easy to derive and implement, since bang–bang signals are characterised by a finite list of scalars – their switching times.     

Fastest recovery from feedback loss: Bounded overshoot

The problem of designing robust optimal controllers to reduce in minimal time operating errors that had accumulated during a period of feedback loss is revisited, with the objective of imposing a constraint on the maximal overshoot of the controlled system. It is shown that robust optimal controllers that satisfy this constraint exist under rather broad conditions. It is also shown that optimal performance can be closely approximated by bang–bang controllers – controllers that are relatively easy to design and implement.     

Spatially nested sampling schemes for spatial variance components: Scope for their optimization

Efficient designs for nested sampling are needed in many areas of science. In the geosciences they are used to discover the important spatial scales on which properties vary. However, while the practical advantages and disadvantages of various nested designs have been discussed, no attempt has been made to optimize nested sampling schemes. This paper shows how an optimal nested sampling design can be found by a method of numerical combinatorial optimization: simulated annealing. The sample design is optimized over a space of possible designs for a fixed sample size and predetermined levels (spatial scales). The objective function for optimization is based on the expected covariance matrix for errors in the estimates of variance components, and so depends on what those components are. By simulation it was shown that optimized sampling schemes can detect scale-dependent variance components with common requirements for statistical power on smaller total sample sizes than are required with commonly used spatially nested sample designs such as the balanced design. Although the optimized design depends on the underlying covariance structure, sampling designs can be identified that perform better than the commonly used ones over a wide range of conditions.     

Insensitive output feedback H ∞ control of delta operator systems with insensitivity to sampling time jitter

The insensitive multi‐objective H _∞ control synthesis problem via dynamic output feedback for linear delta operator systems with insensitivity to sampling time jitter is investigated in the case of small sampling times. The delta‐domain model instead of the standard shift‐domain model is used to avoid the inherent numerical ill‐condition resulting from using the latter model at high sampling rates. Parameter sensitivity function of the transfer function with respect to sampling time is defined to mitigate the effect of sampling time jitter because it may cause significant degradation of the overall system performance. It is worth pointing out that a novel bounded real lemma for delta operator allowing extra degree of freedom for multi‐objective control design is presented by using the well‐known projection lemma. Then, from this new lemma, a two‐step design procedure based on LMI is presented to design insensitive dynamic output feedback controllers such that the resulting closed‐loop system is asymptotically stable and meets the requirement of sensitivity specification. A numerical example is also presented to show the effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

Systematic sampling is a minimum support design

In order to select a sample in a finite population of N units with given inclusion probabilities, it is possible to define a sampling design on at most N samples that have a positive probability of being selected. Designs defined on minimal sets of samples are called minimum support designs. It is shown that, for any vector of inclusion probabilities, systematic sampling always provides a minimum support design. This property makes it possible to extensively compute the sampling design and the joint inclusion probabilities. Random systematic sampling can be viewed as the random choice of a minimum support design. However, even if the population is randomly sorted, a simple example shows that some joint inclusion probabilities can be equal to zero. Another way of randomly selecting a minimum support design is proposed, in such a way that all the samples have a positive probability of being selected, and all the joint inclusion probabilities are positive.     

A random sampling approach to worst-case design of structures

A random sampling approach is presented for worst-case design of structures. Uncertainties are considered in structural and material parameters, which are assumed to exist in intervals with prescribed upper and lower bounds. Constraints are given for the worst responses that are found by solving anti-optimization problems. Optimal cross-sections are then selected from the list of available sections. The regions of uncertainty of parameters are discretized into integer values to formulate the hybrid problem of optimization and anti-optimization as an integer programming problem. The accuracy of solution is defined based on the order of the objective value; hence, a random sampling approach is successfully applied to obtain optimal and anti-optimal solutions within the prescribed accuracy. It is shown in the numerical examples that a good approximate optimal solution is found by random sampling with small number of analyses.     

网络自适应公平分组抽样算法研究

针对SGS(sketch guided sampling)的缺陷,提出了一种网络自适应公平抽样算法.根据抽样分组估计出值流量大小,并依据该值调整抽样比,使之适应于流量变化,从而达到对各种流的公平抽样的效果.对算法的相关性质进行了证明与分析,基于实际互联网数据进行了实验比较,实验结果表明,该算法具有准确性、自适应性、易于工程实现等优点.     

Parametrization of all ripple-free deadbeat controllers

This paper is concerned with deadbeat control in sampled-data systems. Deadbeat control achieves finite-time settling (deadbeat settling) at sampling instants, but there may exist error called ripple “between” sampling instants even after the response is settled “at” sampling instants. The objective of this paper is to give a parametrization of all ripple-free deadbeat controllers (controllers which achieve deadbeat settling without ripple) in sampled-data systems. It is also shown that the following holds in general: minimum-time deadbeat control causes ripple when the pulse transfer function to be controlled has stable zeros.     

On multistage ranked set sampling for distribution and median estimation

A variation of ranked set sampling (RSS), multistage RSS (MSRSS), is investigated for the estimation of the distribution function and some of its quantiles, in particular the median. It is shown that this method is significantly more efficient than simple random sampling (SRS). The method becomes more and more effective as the number of stages r increases. Two estimators of the median based on MSRSS are proposed and compared to the sample median obtained by SRS.     

A relation between overshoot and sampling period in sampled data feedback control systems

This correspondence shows that the overshoot of response of a sampled data feedback control system sometimes tends to infinity as the sampling period tends to zero provided that the assigned poles are constant. This suggests that an optimal sampling period exists in designing, deadbeat controllers.     

Computer control under time-varying sampling period: An LMI gridding approach

This paper addresses computer control under time-varying sampling period and delayed actuation. The proposed approach uses time-varying observers and state-feedback controllers designed by means of linear matrix inequalities (LMI) and quadratic Lyapunov functions. The use of non-stationary Kalman filters is also discussed. A separation principle applies in some cases. A DC motor control setup shows the applicability of the approach in a real implementation.     

Coupled design of test signals, sampling intervals, and filters for system identification

This paper discusses the problem of optimal design of experimental conditions for linear system identification. It is demonstrated that, in general, to achieve maximal return from an experiment, coupled design of all the experimental conditions, namely the test signal, sampling intervals and filters, should be carried out simultaneously. For the case of uniform sampling it is shown that joint design of the presampling filter, sampling rate and input can be carded out in the frequency domain. For the case of nonuniform sampling a sequential design procedure is developed which optimizes the information increment between samples.     

Indirect sampled-data control with sampling period adaptation

It is known that if a continuous-time feedback system is exponentially stable, then the corresponding sampled-data system obtained by sample-hold discretisation with constant sampling period is also exponentially stable, provided that the sampling period τ?>?0 is sufficiently small. In general, it is difficult to estimate how small the sampling period has to be in order to achieve the stability of the sampled-data system. In this article, we present an adaptive mechanism for adjusting the sampling period. This mechanism has the properties that, for every initial state, (i) the adaptation of the sampling period terminates after finitely many time steps and (ii) the state of the adaptive sampled-data system is integrable and converges to zero as time goes to infinity.     

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.     

Control of constrained linear systems using fast sampling rates

This paper addresses the problem of optimal control of constrained linear systems when fast sampling rates are utilised. We show that there exists a well-defined limit as the sampling rate increases. An immediate consequence of this result is the existence of a finite sampling period such that the achieved performance is arbitrarily close to the limiting performance.     

深度图像的分块自适应压缩感知

针对基于空域上下采样的深度编码框架中,由边缘信息损失带来的视点绘制质量下降的问题,提出了一种面向视点绘制质量的深度图像分块自适应压缩采样方法。在基于分块压缩感知和光滑Landweber投影重构的BCS_SPL框架下,利用图像块的方差表征其边缘信息,并据此进行自适应采样,以提高深度图像重构和视点合成质量。结果表明,在相同的采样率下,相比上下采样和BCS_SPL方法,本文提出的分块自适应压缩感知方法在绘制视点的PSNR和主观质量上都有提高。     

Stochastic optimal control and analysis of stability of networked control systems with long delay

In this paper, stochastic optimal controllers of networked control systems whose network-induced delay is longer than a sampling period are designed for the two cases of the system with either full state information or partial state information. The controllers are shown to render corresponding networked control systems exponentially mean square stable. The optimal estimator of the system state is presented when the system has partial state information and network-induced delay is longer than a sampling period. The separation theorem is proved to still hold in such networked control systems.     

Fastest recovery after feedback disruption: nonlinear delay-differential systems

The problem of reducing operating errors after a feedback disruption is considered for a class of nonlinear delay-differential systems. It is shown that there are optimal controllers that reduce such errors in minimal time, once feedback has been restored. It is further shown that optimal performance can be approximated by bang-bang controllers – controllers that are easy to design and implement.     

Rational approximation of L1 optimalcontrollers for SISO systems

The L₁ optimal control problem with rational controllers for continuous-time systems is considered in which it is shown that the optimal L₁ performance index with rational controllers is equal to that of irrational controllers. A sequence of rational controllers that approximates the optimal index is constructed. Convergence properties of such a sequence are studied. That the corresponding sequence of objective transfer functions is shown to converge in weak-* topology in BV(R₊) in the time domain and uniformly in a wider sense in the frequency domain     

基于均匀抽样的二阶差分聚类数确定方法

目标函数二阶差分方法利用目标函数值随类别数的梯度变化作为判定准则,直接利用目标函数值与聚类数的关系,实现在不同数据集上自动得到正确的聚类数,但计算最佳聚类数会占用一定时间。当样本总数较大时,采用该方法得到最佳聚类数计算量非常大。针对此问题,本文提出基于均匀抽样的二阶差分聚类数确定方法,首先采用改进的均匀抽样设计,然后在所得到的数据子集上进行二阶差分设计。实验结果表明,该方法在减少计算量的同时达到了期望的正确判断。