Robust Control Synthesis of Polynomial Nonlinear Systems Using Sum of Squares Technique

在这篇论文，广场(求救) 加技术被用来与多项式向量领域为不明确的仿射的非线性的系统的一个类分析柔韧的州的反馈合成问题。没有或与保证费用或 Hperformance 目的，足够的条件被给获得答案到上述控制问题。而且，如此的可解决的条件能以能被 SOS 技术直接处理的州的依赖线性矩阵不平等(LMI ) 作为求救编程问题被提出。而且，一个想法被提供由介绍一些额外的多项式描述多项式或甚至合理的矩阵的逆。一个数字例子被举说明途径的有效性。

Robust least squares projection twin SVM and its sparse solution

Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

Robust On-Line Fault Diagnosis for Nonlinear Difference-Algebraic Systems Using Least Squares Estimate

A robust on-line fault diagnosis methor based on least squares estimate for nonlinear difference-algebraic systems (DAS) with uncertainties is proposed. Based on the known nominal model of the DAS, this method firstly constructs an auxiliary system consisting of a difference equation and an algebraic equation, then, based on the relationship between the state deviation and the faults in the difference equation and the relationship between the algebraic variable deviation and the faults in algebraic equation, it identifies the faults on-line through least squares estimate. This method can not only detect, isolate and identify faults for DAS, but also give the upper bound of the error of fault identification. The simulation results indicate that it can give satisfactory diagnostic results for both abrupt and incipient faults.

基于Sum of Squares分解技术的电力系统鲁棒综合控制方法

针对具有不确定干扰的汽轮发电机励磁与汽阀综合控制系统,建立鲁棒综合控制模型。运用基于Sum of Squares(SOS)分解技术的鲁棒控制方法(SOSRCA),设计电力系统鲁棒综合控制方法。该方法充分考虑了综合系统中存在的不确定参数及干扰,使发电机组具有较好的鲁棒性能。控制方法的求解过程是算法化、程序化的,避免了繁琐的递归设计和参数估计过程。最后,在三机电力系统仿真中,对基于SOSRCA所得出的鲁棒综合控制律进行仿真分析与讨论,验证其有效性及优越性。

Low-cost adaptive square-root cubature Kalman filter forsystems with process model uncertainty

A novel low-cost adaptive square-root cubature Kalmanfilter （LCASCKF） is proposed to enhance the robustness of processmodels while only increasing the computational load slightly.It is well-known that the Kalman filter cannot handle uncertainties ina process model, such as initial state estimation errors, parametermismatch and abrupt state changes. These uncertainties severelyaffect filter performance and may even provoke divergence. Astrong tracking filter （STF）, which utilizes a suboptimal fading factor,is an adaptive approach that is commonly adopted to solvethis problem. However, if the strong tracking SCKF （STSCKF）uses the same method as the extended Kalman filter （EKF） tointroduce the suboptimal fading factor, it greatly increases thecomputational load. To avoid this problem, a low-cost introductorymethod is proposed and a hypothesis testing theory is applied todetect uncertainties. The computational load analysis is performedby counting the total number of floating-point operations and it isfound that the computational load of LCASCKF is close to that ofSCKF. Experimental results prove that the LCASCKF performs aswell as STSCKF, while the increase in computational load is muchlower than STSCKF.

Outliers, inliers and the generalized least trinuned squares estimator in system identification

The least trimmed squares estimator (LTS) is a well known robust estimator in terms of protecting the estimate from the outliers. Its high computational complexity is however a problem in practice. We show that the LTS estimate can be obtained by a simple algorithm with the complexity 0( N In N) for large N, where N is the number of measurements. We also show that though the LTS is robust in terms of the outliers, it is sensitive to the inliers. The concept of the inliers is introduced. Moreover, the Generalized Least Trimmed Squares estimator (GLTS) together with its solution are presented that reduces the effect of both the outliers and the inliers. Keywords Least squares - Least trimmed squares - Outliers - System identification - Parameter estimation - Robust parameter estimation This work was supported in part by NSF ECS — 9710297 and ECS — 0098181.

Robust Regression Analysis with LR-Type Fuzzy Input Variables and Fuzzy Output Variable

In this paper, we propose a fuzzy linear regression model with LR-type fuzzy input variables and fuzzy output variable, the fuzzy extent of which may be different. Then we give the iterative solution of the proposed model based on the Weighted Least Squares estimation procedure. Some properties of the estimates are proved. We also define suitable goodness of fit index and its adjusted version useful to evaluate the performances of the proposed model. Based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure, we give robust estimation steps for the proposed model. Compared with the well-known fuzzy Least Squares method, the effectiveness of our model on reducing the outliers influence is shown by using two examples.

系数为LR-型模糊数的模糊线性最小二乘回归

针对输入、输出以及系数为LR-型模糊数的情况,建立模糊线性回归模型,提出该模型的最小二乘估计以及模型性能评价方法。当输入、输出以及系数都退化为精确值时,该估计退化为经典的最小二乘估计。该方法不仅适用于三角模糊数,也适用于其它LR-型模糊数(如指数型模糊数)。数值模拟表明,该方法的拟合效果较好。

Robust inversion analysis of local gravity anomalies caused by geological dislocation model of faults

Theoretical analysis and practical observations show that fault dislocations can change the gravity field around the fault. Gravity changes which were caused by the repeated dislocations over a long period of time were superimposed on the Bougeur gravity anomalies. These anomalies became the evidence of historical movement of fault as well as provide a way for the study of paleo earthquakes. This paper investigates inversion methods for the geological dislocation modeling of faults using the local Bouguer's gravity anomalies. To remove the effects of the irrelevant part of gravity anomalies to fault movements, we propose the robust nonlinear inversion method and set up the corresponding algorithm. Modeling examples indicate that the Marquardt's and Baye's least squares solutions depart from the true solution due to the attraction of gross errors in the data. The more seriously the data is contaminated, the more seriously the solutions are biased. In contrast, the proposed robust Marquardt's and Baye's inversion solutions can still maintain consistency with the solution without gross errors, even though 50 percent of the data is contaminated. This indicates that the proposed robust methods are effective. Using the proposed methods, we invert the geological dislocation models of the faults around the Erhai Lake in West Yunnan. The results show that the Northern Cangdong fault and the Erhai fault are normal dip slip faults with about 4 to 5 km dislocations; and that the Southern Cangdong fault has a less dip slip compared with the former two. A satisfactory fitting between the theoretical values of the inversion solution and the actual local gravity field is achievable.

基于具有LR型模糊输出回归模型的保费预测

在保费预测研究中,提出了一种基于模糊回归模型的预测方法.采用模糊最小二乘法,针对清晰输入和LR型模糊输出,在考虑输出量隶属函数类型存在差异问题基础之上,得到模型回归系数的迭代解.通过最小二乘估计的定性分析,给出检验模型拟合度的指标.结合保费数据的预测结果表明模型可行且具有较强的解释能力.

Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

Once the spoofer has controlled the navigation sys-tem of unmanned aerial vehicle(UAV),it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error.Aiming at this problem,the influence of the spoofer’s state estimation error on spoofing effect and error convergence conditions is theoretically analyzed,and an improved adaptively robust estimation algo-rithm suitable for steady-state linear quadratic estimator is pro-posed.It enables the spoofer’s estimator to reliably estimate UAV status in real time,improves the robustness of the estima-tor in responding to observation errors,and accelerates the con-vergence time of error control.Simulation experiments show that the mean value of normalized innovation squared(NIS)is reduced by 88.5%,and the convergence time of NIS value is reduced by 76.3%,the convergence time of true trajectory error of UAV is reduced by 42.3%,the convergence time of estimated trajectory error of UAV is reduced by 67.4%,the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%,and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8%when the improved algorithm is used.The improved algorithm can make UAV deviate from pre-set trajectory to spoofing trajectory more effectively and more subtly.

Robust and Non-fragile H_∞ Control for a Class of Uncertain Jump Linear Systems

This paper describes the synthesis of robust and non-fragile H∞ state feedback controllers for a class of uncertain jump linear systems with Markovian jumping parameters and state multiplicative noises. Under the assumption of a complete access to the norm-bounds of the system uncertainties and controller gain variations, sufficient conditions on the existence of robust stochastic stability and γ-disturbance attenuation H∞ property are presented. A key feature of this scheme is that the gain matrices of controller are only based on It, the observed projection of the current regime rt.

Robust stability analysis for a cruise missile

A global controller design methodology for a flight stage of the cruise missile is proposed. This methodology is based on the method of least squares, To prove robust stability in the full airspace with parameter disturbances, the concepts of convex polytopic models and quadratic stability are introduced, The effect of aerodynamic parameters on system performance is analyzed. The designed controller is applied to track the overloading signal of the cruise segment of the cruise missile, avoiding system disturbance owing to controller switching, Simulation results demonstrate the validity of the proposed method.

A Robust Algorithm for Estimating Motion Parameters Based on the Modified LMedS

We put forward a robust method for estimating motion parameters from the 3-D space position vectors of feature points on the basis of the modified least median of squares (LMedS) regression estimator. First, initial values of motion parameters are estimated by the primary LMedS, then the motion parameters with an iterative reweighted estimator are re-estimated, in which a hybrid weight function of Huber weight and Tukey weight takes place of the dichotomy weight in the primary LMedS, The algorithm alleviates the difficulty of deleting some outliers while SNR is too low, so we can get a more accurate estimation. Computer simulations show that its performance is satisfactory.

ESL-Based Robust Estimation for Mean-Covariance Regression with Longitudinal Data

When longitudinal data contains outliers, the classical least-squares approach is known to be not robust. To solve this issue, the exponential squared loss (ESL) function with a tuning parameter has been investigated for longitudinal data. However, to our knowledge, there is no paper to investigate the robust estimation procedure against outliers within the framework of mean-covariance regression analysis for longitudinal data using the ESL function. In this paper, we propose a robust estimation approach for the model parameters of the mean and generalized autoregressive parameters with longitudinal data based on the ESL function. The proposed estimators can be shown to be asymptotically normal under certain conditions. Moreover, we develop an iteratively reweighted least squares (IRLS) algorithm to calculate the parameter estimates, and the balance between the robustness and efficiency can be achieved by choosing appropriate data adaptive tuning parameters. Simulation studies and real data analysis are carried out to illustrate the finite sample performance of the proposed approach.

A Robust Decision Feedback Equalizer for ATSC DTV Receivers

Least mean square (LMS) decision feedback equalizer (DFE) is preferred as an effective solution to coping with inter-symbol interference (ISI) for ATSC digital television (DTV) receivers. In DTV transmission environment, echo delay often covers several hundreds symbols, which leads to very large-scale equalizer. One consequence of the large-scale equalizer is the very slow convergence, which combined with error propagation, inherent drawback of DFE, seriously deteriorates the performance of the receivers, especially in severe channels More working modes and corresponding robust control mechanism were given to help the equalizer converge to the stable state smoothly. Simulation results show that the improved equalizer can perform better, especially in the severe channels.

A Forecasting Method of Pollution Flashover Voltage of Insulators Based on Robust Regression

In view of the limitations in the prediction of pollution flashover voltage by least squares regression, a method to predict pollution flashover voltage by robust regression is proposed. According to testing voltage and the data of salt deposit density (ρSDD ) and non-soluble deposit density (ρNSDD ), the regression coefficient is solved by a complex weighting least square iteration algorithm. In iterative calculations, the weight function is adopted, in which the weight coefficient is the function of the residual error of last iteration to weaken the influence of singular values on the regression coefficient. The characteristic exponent denoting ρSDD influence and characteristic exponent denoting ρNSDD influence are mapped by the regression coefficient, and thus the pollution flashover voltage of insulators can be predicted. Through the comparison of test results, robust regression results and least squares regression results, the effectiveness of the proposed robust regression-based forecasting method is verified.

Robust Palmprint Recognition Base on Touch-Less Color Palmprint Images Acquired

In order to make the environment of palmprint recognition more flexible and improve the accuracy of touchless palmprint recognition. This paper proposes a robust, touchless, palmprint recognition system which is based on color palmprint images. This system uses skin-color thresholding and hand valley detection algorithm for extracting palmprint. Then, the local binary pattern (LBP) is applied to the palmprint in order to extract the palmprint features. Finally, chi square statistic is used for classification. The experimental results present the equal error rate of 3.7668% and correct recognition rate of 97.0142%. Therefore the results show that this approach is robust and efficient in color palmprint images which are acquired in lighting changes and cluttered background for touch-less palmprint recognition system.

GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System, GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System, SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS/SINS/视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS/SINS/视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

非视距环境下核密度估计的全球卫星导航系统鲁棒定位方法

非视距(NLOS)传输会引起全球卫星导航系统(GNSS)接收机的伪距测量误差,最终导致定位解出现较大误差,这一问题在城市峡谷等复杂环境下尤为突出。针对该问题,该文提出核密度估计的鲁棒定位方法,其核心思想是在定位解算中引入鲁棒估计来缓解NLOS的影响。考虑到NLOS引起的伪距观测误差偏离高斯分布,所提方法首先利用核密度估计(KDE)方法估计伪距观测误差的概率密度函数,并利用该概率密度函数来构造鲁棒代价函数用于导航解算,从而缓解NLOS引起的定位误差。实验结果表明所提方法在卫星存在NLOS传输时能够较好地减小GNSS的定位误差。