可变抽样区间二元双抽样广义方差控制图

将可变抽样区间特性融入二元双抽样广义方差控制图,构建可变抽样区间二元双抽样广义方差控制图及其监控方法.采用马尔科夫链方法完成控制图调整的平均报警时间和平均报警时间性能指标的计算,进而建立用控制图参数设计的优化模型,并通过遗传算法完成模型求解.通过与可变抽样区间的二元广义方差合成控制图、二元双抽样广义方差控制图以及基于Cornish-Fisher修正的二元双抽样广义方差控制图的判异性能对比,验证该控制图的优势,并通过加工实例对性能优势进行进一步说明.     

Delay-dependent resilient-robust stabilisation of uncertain networked control systems with variable sampling intervals

This work is concerned with the robust resilient control problem for uncertain networked control systems (NCSs) with variable sampling intervals, variant-induced delays and possible data dropouts, which is seldom considered in current literature. It is mainly based on the continuous time-varying-delay system approach. Followed by the nominal case, delay-dependent resilient robust stabilising conditions for the closed-loop NCS against controller gain variations are derived by employing a novel Lyapunov–Krasovskii functional which makes good use of the information of both lower and upper bounds on the varying input delay, and the upper bound on the variable sampling interval as well. A feasible solution of the obtained criterion formulated as linear matrix inequalities can be gotten. A tighter bounding technique is presented for acquiring the time derivative of the functional so as to utilise many more useful elements, meanwhile neither slack variable nor correlated augmented item is introduced to reduce overall computational burden. Two examples are given to show the effectiveness of the proposed method.     

Economic design of variable sampling intervals T2 control charts using genetic algorithms

Control charting is a graphical expression and operation of statistical hypothesis testing. In this paper, we develop the economic design of the variable sampling intervals (VSI) T² control chart to determine the values of the five test parameters of the chart (i.e. the sample size, the long sampling interval, the short sampling interval, the warning limit, and the control limit) such that the expected total cost, associated with the test procedure, is minimized. The genetic algorithm (GA) is employed to search for the optimal values of the five test parameters of the VSI T² chart, and an example is provided to illustrate the solution procedure. Sensitivity analysis is then carried out to investigate the effects of model parameters on the solution of the economic design.     

Kernels for longitudinal data with variable sequence length and sampling intervals

We develop several kernel methods for classification of longitudinal data and apply them to detect cognitive decline in the elderly. We first develop mixed-effects models, a type of hierarchical empirical Bayes generative models, for the time series. After demonstrating their utility in likelihood ratio classifiers (and the improvement over standard regression models for such classifiers), we develop novel Fisher kernels based on mixture of mixed-effects models and use them in support vector machine classifiers. The hierarchical generative model allows us to handle variations in sequence length and sampling interval gracefully. We also give nonparametric kernels not based on generative models, but rather on the reproducing kernel Hilbert space. We apply the methods to detecting cognitive decline from longitudinal clinical data on motor and neuropsychological tests. The likelihood ratio classifiers based on the neuropsychological tests perform better than than classifiers based on the motor behavior. Discriminant classifiers performed better than likelihood ratio classifiers for the motor behavior tests.     

Cumulative conformance count charts with variable sampling intervals for correlated samples

The cumulative conformance count (CCC) control chart is often employed to monitor the fraction nonconforming of high-yield processes. Traditional CCC chart is used when the items from a process are inspected one-at-a-time following the production order. In recent years, the CCC chart has been generalized to accommodate some industrial practices where items from a process are inspected sample by sample and not according to the production order. In order to increase the sensitivity of the generalized CCC (GCCC) chart to changes in fraction nonconforming, the variable sampling interval (VSI) scheme is used in this study. The output characteristic within each sample is assumed with correlation. The statistical properties of the GCCC chart with the VSI scheme are deduced using the Markov chain method. In evaluating the usefulness of the VSI feature, GCCC charts with VSI and fixed sampling interval (FSI) schemes are compared in terms of their statistical properties. The comparison results show that using the VSI scheme can improve the speed of GCCC chart in detecting changes in fraction nonconforming. Finally, according to the comparison results, a design procedure is applied to an industrial example to validate its practicability.     

Enhancing the performance of CUSUM scale chart

Control charts act as the most important statistical process monitoring tool, widely used for the purpose of identifying unusual variations in process parameters. Researchers have implemented different rules to increase the sensitivity of Shewhart, CUSUM and EWMA control charts for the detection of small shifts in process location. However, for the monitoring of process scale, the use of such rules has been limited to Shewhart charts. This study proposes the implementation of sensitizing rules in CUSUM scale charts to enhance their ability to detect smaller changes in process variability. The performance of the proposed schemes is evaluated and compared with the simple scale CUSUM scheme, the EWMS chart, the M-EWMS chart and the COMB chart, in terms of run length characteristics such as average run length (ARL) and standard deviation of the run length distribution (SDRL). Control chart coefficients to set the ARL at the desired level are also provided. Two numerical examples are given to illustrate the application of the proposed schemes on practical data sets.     

Synthetic double sampling np control chart for attributes

The quality of a product, based on the number of non-conforming items can be controlled using the np chart. This paper proposes a synthetic double sampling (DS) np chart which comprises two sub-charts, i.e. the DS np and conforming run length (CRL) sub-charts. For the zero-state case, the synthetic DS np chart surpasses its standard counterpart, i.e. the synthetic np and the basic DS np chart, and other np type charts like the standard np, combined synthetic and np (Syn-np), variable sample size (VSS) np, exponentially weighted moving average (EWMA) np and cumulative sum (CUSUM) np charts, for detecting increases in the fraction of non-conforming items p, for most shift sizes. The synthetic DS np chart also performs reasonably well in the steady-state case in comparison with other charts mentioned above. Thus, among the competing charts, the synthetic DS np chart stands out as one of the best charts.     

Adaptive CUSUM procedures with Markovian mean estimation

This paper proposes using a Markovian-type mean estimating procedure in the conventional cumulative sum (CUSUM) control scheme to update its reference value in an adaptive way. This generalizes a class of Markovian adaptive CUSUM (ACUSUM) schemes to achieve the aim of providing an overall good performance over a range of future expected but unknown mean shifts. A two-dimensional Markov chain model is developed to analyze the run length performance of the new scheme. A comparison of run length performance of the proposed ACUSUM scheme and other control charts is shown favorable to the former.     

Output-feedback control for sampled-data systems with variable sampling rate

In this paper, we propose design method of controller for sampled-data systems with variable sampling rate. First, we give design method for both H₂ and H_∞ controller. For H₂ control, performance of the system is introduced according to a standard sampled-data setting. A discrete-time H₂ control problem is employed for solving the original problem. Its solvability condition is then established as a parameter-dependent linear matrix inequality. A probabilistic approach is taken for coping with the parameter-dependency. H_∞ controller is designed by almost the same manner. Applying both results, we have design method for multi-objective control.     

A multivariate synthetic double sampling T2 control chart

In this article, we propose a multivariate synthetic double sampling T² chart to monitor the mean vector of a multivariate process. The proposed chart combines the double sampling (DS) T² chart and the conforming run length (CRL) chart. On the whole, the proposed chart performs better than its standard counterparts, namely, the Hotelling’s T², DS T², and synthetic T² charts, in terms of the average run length (ARL) and average number of observations to sample (ANOS). The proposed chart also outperforms the multivariate exponentially weighted moving average (MEWMA) chart for moderate and large shifts but the latter is more sensitive than the former towards small shifts. For a variable sample size chart, like the synthetic DS T² chart, ANOS is a more meaningful performance measure than ARL. ANOS relates to the actual number of observations sampled but ARL merely deals with the number of sampling stages taken. Interpretation based on ARL is more complicated as either n₁ or n₁ + n₂ observations are taken in each sampling stage.     

An exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles

This paper proposes an exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles. A computer program in Fortran is available to assist in the design of the control chart and the algorithm of the Fortran program is also given. Some useful guidelines are also provided to aid users in choosing parameters for a particular application. Simulation results on the detection performance of the proposed control chart, compared with some other competing methods show that it provides quite robust and satisfactory performance in various cases, including intercept shifts, slope shifts and standard deviation shifts. A real data example from an optical imaging system is employed to illustrate the implementation and the use of the proposed control scheme.     

An exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles

This paper proposes an exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles. A computer program in Fortran is available to assist in the design of the control chart and the algorithm of the Fortran program is also given. Some useful guidelines are also provided to aid users in choosing parameters for a particular application. Simulation results on the detection performance of the proposed control chart, compared with some other competing methods show that it provides quite robust and satisfactory performance in various cases, including intercept shifts, slope shifts and standard deviation shifts. A real data example from an optical imaging system is employed to illustrate the implementation and the use of the proposed control scheme.     

一种自适应非参量CUSUM控制图算法

针对CUSUM控制图中存在的固定检测门限和对异常终止反应迟钝的缺点,提出了一种自适应的非参量CUSUM控制图算法.该算法首先利用固定门限剔除野值,同时简化了对显著异常的检测过程.然后,采用简单滑动平均算法对非野值数据进行平滑,并基于切比雪夫不等式理论对平滑后的数据进行转换,使之满足非参量CUSUM算法的使用条件.最后,由算法根据数据转换结果自适应地设置CUSUM算法中的检测门限,并在发出异常告警后实施异常终止监控.在针对SYN洪流攻击的仿真检测试验中,利用该算法能够在检测时延不超过7个采样周期且攻击持续期间不发生漏警的要求下,准确地检测出最低攻击流量仅为正常业务流量20%的攻击行为.     

Adaptive sliding mode control for variable structure systems with constraint control input

In this paper, the problems in sliding mode control systems with constraint control input are described, and then we utilize selecting a switching surface with an integrator to solve this problem and to derive a bound for the existence of sliding motion. Additionally, we propose an adaptive sliding mode controller in order to reduce settling time and also maintain the sliding motion in a larger bound. Finally, an illustrative example is given to demonstrate the effect of proposed controller.     

变异区间自适应调整的遗传算法

在对遗传算法进化概率进行深入分析的基础上,提出了变异区间随演化进程自适应调整的遗传算法,旨在通过变异区间的自适应调整提高遗传算法的性能。实验结果表明变异区间自适应调整的遗传算法不仅比基本遗传算法有更快的收敛速度,而且有更高的求解精度。     

Adaptive variable structure set-point control of underactuatedrobots

Control of underactuated mechanical systems (robots) represents an important class of control problem. In this correspondence, a model-based adaptive variable structure control scheme is introduced, where the uncertainty bounds only depend on the inertia parameters of the system. Global asymptotic stability is established in the Lyapunov sense. Numerical simulations are conducted to validate the theoretical analysis     

Integral control by variable sampling based on steady-state data

In this paper we consider integral control algorithms with convergent adaptive sampling for multivariable infinite-dimensional systems. Steady-state gain information is used in choosing suitable integrator gains and we also consider robustness with respect to error in measuring the steady-state gain.     

Adaptive radial basis function neural network control with variable variance parameters

The paper presents a direct adaptive control architecture for a class of nonlinear dynamic systems, which are either ill defined or rather complex. The direct adaptive architecture employs radial basis function (RBF) neural network (NN) systems to reconstruct the ideal feedback linearization control. With the modified adaptation algorithm proposed herein, the on-line function approximation capability of the RBF NN a system is enhanced to remove the auxiliary control term and switching element in a conventional RBF-NN-based controller; simultaneously, the tracking performance is upgraded. Global asymptotic stability of the on-line algorithm is established in the Lyapunov sense to guarantee that the tracking error can converge to a small neighbourhood of the origin. Simulation validations for an inverted pendulum system are finally performed to verify the effectiveness of the proposed controller and the theoretical discussion.     

牵引车–飞机系统的自适应滑模变结构控制

将自动转向技术应用于牵引车–飞机系统, 并以侧偏位移和相对横摆角作为反馈, 提出一种牵引车四轮主动转向控制策略. 重点考虑牵引车和飞机的侧向和横摆运动, 建立含铰接角在内的牵引车–飞机系统非线性动力学模型. 将牵引车和飞机的轮胎侧偏刚度视为有界的不确定性参数, 将侧向风等因素视为未知的外在扰动, 采用自适应滑模变结构控制方法设计牵引车转向角控制器. 仿真结果表明, 设计出的前、后轮转向控制器能使控制系统同时获得很好的轨迹跟踪性和操纵稳定性, 并且能够有效的克服参数摄动和外界干扰对系统操作性的影响.     

Adaptive backstepping control of variable speed wind turbines

Variable speed wind turbines maximize the energy capture by operating the turbine at the peak of the power coefficient, however parametric uncertainties in mechanical and electrical dynamics of the system may limit the efficiency of the turbine. In this study, we present an adaptive backstepping approach for the variable speed control of wind turbines. Specifically, to overcome the undesirable effects of parametric uncertainties, a desired compensation adaptation law (DCAL) based controller has been proposed. The proposed method achieves global asymptotic rotor speed tracking, despite the parametric uncertainty on both mechanical and electrical subsystems. Extensive simulation studies are presented to illustrate the feasibility and efficiency of the method proposed.