双连杆柔性机械手负载自适应模糊滑模控制

讨论了柔性机械手末端负载变化时的控制问题。应用奇异摄动将双连杆柔性机械手系统分解为慢变、快变两个子系统。提出一种慢变子系统采用自适应模糊滑模控制、快变子系统采用最优控制的混合控制方法。仿真结果表明,该方法不仅能实现柔性机械手轨迹的快速、准确跟踪,有效的抑制弹性振动,并且对负载的变化具有强的鲁棒性。     

Robust Control of Uncertain Markov Jump Singularly Perturbed Systems

In this paper,we study the robust control for uncertain Markov jump linear singularly perturbed systems(MJLSPS),whose transition probability matrix is unknown.An improved heuris- tic algorithm is proposed to solve the nonlinear matrix inequalities.The results of this paper can apply not only to standard,but also to nonstandard MJLSPS.Moreover,the proposed approach is independent of the perturbation parameter and therefore avoids the ill-conditioned numerical prob- lems.     

时域推进算法中TPM模型的奇异性研究

三角贴片模型(TPM)常用于研究电磁散射的时域推进算法中。但在计算矢量位函数和标量位函数的微分过程中,TPM模型将产生奇异点。本文详细分析了TPM模型奇异性问题的产生,并提出了处理奇异点的方法,给出的算例证明了该处理方法的有效性。     

Singular value decomposition based data distortion strategy for privacy protection

Privacy-preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications, such as in data mining-based terrorist analysis systems. We propose a sparsified Singular Value Decomposition (SVD) method for data distortion. We also put forth a few metrics to measure the difference between the distorted dataset and the original dataset and the degree of the privacy protection. Our experimental results using synthetic and real world datasets show that the sparsified SVD method works well in preserving privacy as well as maintaining utility of the datasets. Shuting Xu received her PhD in Computer Science from the University of Kentucky in 2005. Dr. Xu is presently an Assistant Professor in the Department of Computer Information Systems at the Virginia State University. Her research interests include data mining and information retrieval, database systems, parallel, and distributed computing. Jun Zhang received a PhD from The George Washington University in 1997. He is an Associate Professor of Computer Science and Director of the Laboratory for High Performance Scientific Computing & Computer Simulation and Laboratory for Computational Medical Imaging & Data Analysis at the University of Kentucky. His research interests include computational neuroinformatics, data miningand information retrieval, large scale parallel and scientific computing, numerical simulation, iterative and preconditioning techniques for large scale matrix computation. Dr. Zhang is associate editor and on the editorial boards of four international journals in computer simulation andcomputational mathematics, and is on the program committees of a few international conferences. His research work has been funded by the U.S. National Science Foundation and the Department of Energy. He is recipient of the U.S. National Science Foundation CAREER Award and several other awards. Dianwei Han received an M.E. degree from Beijing Institute of Technology, Beijing, China, in 1995. From 1995to 1998, he worked in a Hitachi company(BHH) in Beijing, China. He received an MS degree from Lamar University, USA, in 2003. He is currently a PhD student in the Department of Computer Science, University of Kentucky, USA. His research interests include data mining and information retrieval, computational medical imaging analysis, and artificial intelligence. Jie Wang received the masters degree in Industrial Automation from Beijing University of Chemical Technology in 1996. She is currently a PhD student and a member of the Laboratory for High Performance Computing and Computer Simulation in the Department of Computer Science at the University of Kentucky, USA. Her research interests include data mining and knowledge discovery, information filtering and retrieval, inter-organizational collaboration mechanism, and intelligent e-Technology.     

Continuous stabilization controllers for singular bilinear systems: The state feedback case

Global asymptotic stabilization for a class of singular bilinear systems is first studied in this paper. New approaches are developed by means of the LaSalle invariant principle for nonlinear systems. A new set of sufficient condition is first derived via the continuous static state feedback, the feedback not only guarantees the existence of solution but also the global asymptotical stabilization for the closed loop system.     

A Trajectory-Based Approach for the Stability Robustness of Nonlinear Systems with Inputs

 We show, for two different definitions of semiglobal practical external stability, that the stability property holds on semi-infinite time intervals if and only if it holds on arbitrarily long but finite time intervals. These results have immediate applications in the analysis of the stability properties of highly oscillatory systems with inputs using averaging or for systems with inputs that are slowly varying. Results are stated for general flows and the stability is given with respect to arbitrary (not necessarily compact) sets. Date received: May 11, 2001. Date revised: March 14, 2002.     

The infinite time near optimal decentralized regulator problem for singularly perturbed systems: a convex optimization approach

This paper presents LQ decentralized pole location for singularly perturbed systems. The poles are located in a sector included in the left-half complex plane. The singular perturbation method is used to define reduced and well-behaved problems. It is shown that the LQ control problem with pole location in a sector can be solved using the LMI tool. The associated parametrical optimization problem involves a linear cost objective under LMI constraints. The decentralized control problem is then solved in the reduced slow system by just introducing structure constraints on the matrix variables, constraints that do not destroy the linearity and then the convexity of the problem.     

Multi-resolution form of SVD for text-independent speaker recognition

A text-independent speaker recognition system based on multi-resolution singular value decomposition (MSVD) is proposed. The MSVD is applied to the speaker data compression and feature extraction not at the square matrix. Our results have shown that this MSVD introduced better performance than the other Karhunen-Loeve transform with respect to the percentages of recognition.     

On the decay rate of Hankel singular values and related issues

This paper investigates the decay rate of the Hankel singular values of linear dynamical systems. This issue is of considerable interest in model reduction by means of balanced truncation, for instance, since the sum of the neglected singular values provides an upper bound for an appropriate norm of the approximation error. The decay rate involves a new set of invariants associated with a linear system, which are obtained by evaluating a modified transfer function at the poles of the system. These considerations are equivalent to studying the decay rate of the eigenvalues of the product of the solutions of two Lyapunov equations. The related problem of determining the decay rate of the eigenvalues of the solution to one Lyapunov equation will also be addressed. Very often these eigenvalues, like the Hankel singular values, are rapidly decaying. This fact has motivated the development of several algorithms for computing low-rank approximate solutions to Lyapunov equations. However, until now, conditions assuring rapid decay have not been well understood. Such conditions are derived here by relating the solution to a numerically low-rank Cauchy matrix determined by the poles of the system. Bounds explaining rapid decay rates are obtained under some mild conditions.     

低浓度三分子模型的分支理论

本文讨论了低浓度三分子模型的奇点性态，证明了中心焦点是稳定的。并且应用分支理论对该模型进行了定性分析，给出极限环趋于奇点时的刻划。