不确定随机线性重复过程的鲁棒L2-L∞滤波

研究了不确定随机线性重复过程的鲁棒L2-L∞滤波问题.通过设计一个全阶滤波器使不确定随机线性重复过程均方渐近稳定,给出鲁棒L2-L∞全阶滤波器存在的充分条件,并将滤波器的设计转化为一个凸优化的求解问题.所设计的滤波器能够保证相对于所有能量有界的外界扰动信号,重复过程的L2-L∞性能指标小于定值γ.仿真实例证实了该设计方法的有效性.     

时滞不确定离散系统的鲁棒l2-l∞滤波

将l2-l∞性能指标引入时滞不确定离散时间系统,研究基于这一指标的滤波器设计问题.所研究的对象是同时具有状态时滞和多面体不确定性的离散时间系统.采用线性矩阵不等式技术推导了此类不确定系统鲁棒l2-l∞滤波器存在的充分条件,并将滤波器的设计转化为一个凸优化的求解问题,可以采用较为有效的内点方法进行求解.所设计的滤波器能够保证相对于所有能量有界的外界扰动信号,滤波误差系统具有一定的l2-l∞扰动衰减水平.数值仿真验证了所提出算法的可行性.     

不确定连续系统的鲁棒H2/H∞滤波

研究凸多面体不确定连续系统的鲁棒H2／H∞滤波问题．为降低设计的保守性．提出一种新的具有参数依赖Lyapunov函数的鲁棒H2／H∞性能准则．基于该性能准则，推导出鲁棒H2／H∞滤波器存在的充分条件．并将滤波器设计问题转化为具有线性矩阵不等式（LMI）约束的参数优化问题．     

不确定时滞系统的鲁棒H∞滤波

对一类同时具有外界干扰和范数有界参数不确定性的时滞系统鲁棒H∞滤波问题进行了研究。对于所有容许的参数不确定性,利用Lyapunov方法,得到以线性矩阵不等式（linear matrix inequality,LMI）表示的鲁棒H∞滤波器设计方法。用该方法设计的滤波器使得滤波误差系统渐近稳定且满足一定的H∞性能指标。给出了滤波器存在的充分条件,并得到了设计滤波器的LMI方法。进而将最优鲁棒H∞滤波器存在的充分条件归结为一个具有线性矩阵不等式（LMI）约束的凸优化问题。最后,仿真结果很好地说明了本文方法的有效性。     

不确定跳变系统鲁棒L 2-L ∞滤波

针对既有连续时间演化,又含Markov事件驱动的一类混杂动态跳变系统,研究L2-L∞性能指标下的鲁棒滤波问题,只要系统遭受的外部干扰是能量有界的,便可保证一定的预设滤波误差峰值水平.在有限时间域内定义L2-L∞增益,利用随机稳定性分析获得了若干主要结果.鲁棒滤波器的分析和设计,不仅考虑了各模态下凸多面体形式描述的动态系统参数不确定性,更考虑了各模态间跳变转移概率的不确定性,使得滤波器对模态跳变机理具备了一定的鲁棒性.鲁棒滤波器的存在条件及设计方法可直接利用耦合线性矩阵不等式.最后用数值示例对结果进行了验证.     

随机时延网络化不确定系统的鲁棒H∞滤波

研究了一类具有随机时延的网络化不确定系统的 H∞ 滤波器设计问题. 这类时延的产生是由于传感器和滤波器通过有限带宽的网络连接而引起的系统测量数据的滞后, 而且它是随机发生的. 本文采用满足 Bernoulli 分布随机变量来描述测量数据的这种随机时延. 利用线性矩阵不等式, 给出了全阶和降阶的滤波器存在的充分条件. 所设计的滤波器使得滤波误差系统是均方指数稳定且具有给定的H∞ 性能. 数值仿真表明设计方法的有效性.     

线性连续重复过程的能量-峰值(L2-L∞)滤波

连续线性重复过程是一种特殊的二维(2-D)系统.两个坐标轴上一个是连续的,另一个是离散的,且其中连续坐标轴是时间有限的.针对这类特殊二维系统的L2-L∞滤波问题.设计了适合于该类系统的一类滤波器,并给出了滤波误差系统沿通道稳定且满足L2-L∞性能的充分条件,以及滤波器的求解条件.所得到的条件均为线性矩阵不等式的形式,便于计算求解.仿真实例证实了该设计方法的有效性.     

不确定时滞中立系统的鲁棒H∞滤波

研究一类噪声干扰信号和被估计信号带有参数不确定性时的时滞中立系统的鲁棒H∞滤波设计问题.假设参数不确定性具有线性分式形式,给出的滤波器使误差系统渐近稳定且达到指定的干扰抑制水平.该方法将系统中的范数有界不确定作外部干扰处理,将不确定中立系统的鲁棒H∞滤波问题转化为确定系统的H∞滤波设计.最后通过解线性矩阵不等式得到可采...     

随机时滞Lurie系统的鲁棒H∞和L2-L∞指数控制

针对一类具有范数有界不确定性和时变时滞的It^o型随机Lurie系统, 研究了鲁棒H_∞和L₂–L_∞指数控制问题. 利用Lyapunov-Krasovskii泛函方法和It^o微分公式, 得到了以线性矩阵不等式(LMIs)表示的控制器存在的充分条件. 对所有容许的参数不确定性, 设计的无记忆状态反馈控制器使闭环系统鲁棒指数均方稳定, 且具有给定的H_∞和L₂–L_∞干扰抑制度. 最后, 通过两个仿真例子说明了所提方法的有效性.     

随机不确定系统的鲁棒H∞滤波

研究在同时具有参数和随机不确定的情况下的鲁棒H∞估计问题.假设系统的方程由Ito随机微分方程描述,不确定的参数是范数有界的,外部干扰是随机不确定的.通过解一个线性矩阵不等式,可以设计鲁棒H∞滤波器,最后给出的一个例子对理论分析进行了阐述.     

A new design of robust filters for uncertain systems

In this paper, a structured polynomial parameter-dependent approach is proposed for robust H₂ filtering of linear uncertain systems. Given a stable system with parameter uncertainties residing in a polytope with s vertices, the focus is on designing a robust filter such that the filtering error system is robustly asymptotically stable and has a guaranteed estimation error variance for the entire uncertainty domain. A new polynomial parameter-dependent idea is introduced to solve the robust H₂ filtering problem, which is different from the quadratic framework that entails fixed matrices for the entire uncertainty domain, or the linearly parameter-dependent framework that uses linear convex combinations of s matrices. This idea is realized by carefully selecting the structure of the matrices involved in the products with system matrices. Linear matrix inequality (LMI) conditions are obtained for the existence of admissible filters and based on these, the filter design is cast into a convex optimization problem, which can be readily solved via standard numerical software. Both continuous and discrete-time cases are considered. The merit of the methods presented in this paper lies in their less conservatism than the existing robust filter design methods, as shown both theoretically and through extensive numerical examples.     

Some results on linear equality constrained state filtering

This paper addresses the linear equality constrained state filtering for linear dynamic systems from different perspectives. First, by integrating constraint information into the state equation to ensure that the estimates naturally satisfy the constraints, the constrained filtering problem can be transformed into an unconstrained one. Second, according to a linear transformation of the state vector and the linear relationship between different new state components, a reduced-order Kalman filter is developed. Third, adding a projection step after the one-step state prediction in the Kalman filtering algorithm, we present a state prediction projection method. These approaches are mutually equivalent, and the existing null space method proves to be a special case of them. Most of current methods and the proposed approaches can be summed up in a unified framework and boil down to three forms of the projection method. Finally, a vehicle tracking example is provided to compare the performance of the discussed constrained filters.     

基于降阶观测器的双线性系统H∞补偿器设计

讨论了多输入多输出双线性连续时间系统的基于降阶观测器的[H∞]补偿器设计问题。利用线性矩阵不等式和Lyapunov方程,得到了保证闭环系统全局渐近稳定且满足给定干扰抑制水平的bang-bang控制律的设计。仿真验证了所给理论结果的有效性。     

Study on robust H �� filtering in networked environments

 This paper is concerned with the robust H_∞ filter problem for networked environments, which are subject to both transmission delay and packet dropouts randomly. By employing random series which have Bernoulli distributions taking value of 0 or 1, the data transmission model is obtained. Based on state augmentation and stochastic theory, the sufficient condition for robust stability with H_∞ constraints is derived for the filtering error system. The robust filter is designed in terms of feasibility of one certain linear matrix inequality (LMI), which is formed by adopting matrix congruence transformations. A numerical example is given to show the effectiveness of the proposed filtering method.     

Resilient linear filtering of uncertain systems

The problem of resilient linear filtering for a class of linear continuous-time systems with norm-bounded uncertainties is investigated. We have considered additive filter gain variations to reflect the imprecision in filter implementation. The design problem of resilient linear filter is formulated as a convex optimization problem over linear matrix inequalities. As a limiting procedure, the case of resilient Kalman filter is derived. All the developed results are conveniently extended to the case of multiplicative filter gain variations. Simulation studies are carried out to support the theoretical findings.     

一类关联时滞组合系统的H_∞滤波

针对当输入噪声为有限能量信号时的渐近稳定的线性滤波器的设计问题,基于Lyapunov稳定性理论,结合线性矩阵不等式技术,提出了一类关联时滞组合系统的H_∞滤波器的设计方案,并利用有界实引理给出了滤波器存在的一个充分条件。为了使得滤波器具有良好的稳态性能,考虑了LMI的优化问题。并将滤波器的设计问题转化为具有线性矩阵不等式约束的凸优化求解问题。通过求解一组LMI,可以得到最优滤波器。     

Robust H-infinity filter for a class of polytopic Delta operator systems

This study deals with the robust H-infinity filtering for a class of Delta operator systems with polytopic uncertainties. By the aid of introducing two slack matrices to eliminate the coupling between systems matrices and Lyapunov matrices, an improved version of the bounded real lemma is given via linear matrix inequality formulation, which shows a close correspondence between the continuous-and discrete-time H-infinity performance criterion. Based on it, the existence condition of the desired filter is obtained such that the corresponding filtering error system is asymptotically stable with a guaranteed performance index. A numerical example is employed to illustrate the feasibility and advantages of the proposed design.     

An LMI approach to robust reduced-order H ∞ filter design for polytopic uncertain systems

This paper proposes a method for robust reduced-order H _∞ filter design for polytopic uncertain systems, using linear matrix inequalities (LMIs). Sufficient LMI conditions for both robust full- and reduced-order H _∞ filter design are derived. Convex optimization problems are formulated and solved to obtain optimal H _∞ filters by using the resulting LMI conditions. The resulting conditions do not involve any non-convex rank constraints, and thus the proposed method for H _∞ filter design guarantees global optimum solutions. Numerical examples are presented to show the effectiveness of the proposed method. Recommended by Editorial Board member Huanshui Zhang under the direction of Editor Young Il Lee. This work was supported by the Brain Korea 21 Project and the Basic Research Program of the Korea Science and Engineering Foundation under grant R01-2006-000-11373-0. Hyoun-Chul Choi received the B.S., M.S., and Ph.D. degrees in Control and Instrumentation Engineering from Ajou University, Suwon, Korea, in 1995, 1997, and 2006, respectively. He was a Visiting Researcher at Griffith University, Brisbane, Australia, from 2001 to 2002, and a Postdoctoral researcher at Ajou University, Suwon, Korea, from 2006 to 2007. Since 2008, he has been with ASRI, School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea, where he is currently a Postdoctoral Researcher. His research interests include LMI-based control, optimal and robust control, network-based control, and mechatronics. Dongkyoung Chwa received the B.S. and M.S. degrees from the Department of Control and Instrumentation Engineering in 1995 and 1997, respectively, and the Ph.D. degree from the School of Electrical and Computer Engineering in 2001, all from Seoul National University, Seoul, Korea. From 2001 to 2003, he was a Postdoctoral Researcher with Seoul National University. In 2003, he was a Visiting Research Fellow at The University of New South Wales, Australian Defence Force Academy, and was the Honorary Visiting Academic at the University of Melbourne, Melbourne, Australia. In 2004, he was a BK21 Assistant Professor with Seoul National University. Since 2005, he has been an Assistant Professor with the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea. His research interests are nonlinear, robust, and adaptive control theories and their applications to the robotics, underactuated systems including wheeled mobile robots, underactuated ships, cranes, and guidance and control of flight systems. Suk-Kyo Hong received the B.S., M.S., and Ph.D. degrees in Electrical Engineering from Seoul National University, Seoul, Korea, in 1971, 1973, and 1981, respectively. His major graduate research works were centered on speed control of induction motors. He was an Exchange Professor at Rensselaer Polytechnic Institute, Troy, NY, from 1982 to 1983, and at the Institut National de Recherche en Informatique et en Automatique, France, from 1988 to 1989. He has been with the faculty of the Department of Electrical and Computer Engineering, Ajou University, Suwon, Korea, since 1976, and was a Visiting Professor at Griffith University, Australia, in 2001 and 2002. His current research interests include robust robot control, microprocessor applications, factory automation, and computer integrated manufacturing.     

LMI-based minimax estimation and filtering under unknown covariances

In this paper, we consider a minimax approach to the estimation and filtering problems in the stochastic framework, where covariances of the random factors are completely unknown. The term ‘random factors’ refers either to unknown parameters and measurement noise in the estimation problem or to disturbance process and the initial state of a linear discrete-time dynamic system in the filtering problem. We introduce a notion of the attenuation level of random factors as a performance measure for both a linear unbiased estimate and a filter. This is the worst-case variance of the estimation error normalised by the sum of variances of all random factors over all nonzero covariance matrices. It is shown that this performance measure is equal to the spectral norm of the ‘transfer matrix’ and therefore the minimax estimate and filter can be computed in terms of linear matrix inequalities (LMIs). Moreover, the explicit formulae for both the minimax estimate and the minimal value of the attenuation level are presented in the estimation problem. It turns out that the above attenuation level of random factors coincides with the attenuation level of deterministic factors that is the worst-case normalised squared Euclidian norm of the estimation error over all nonzero sample values of random factors. In addition, we demonstrate that the LMI technique can be applied to derive the optimal robust estimator and filter, when there is a priori information about convex polyhedral sets which unknown covariance matrices of random factors belong to. Two illustrative examples show advantages of the minimax approach proposed.