Plasma lens experiment at the final focus test beam

The proposed plasma lens experiment at the Final focus Test Beam (FFTB) facility of the Stanford Linear Accelerator Center has been approved by the adminstration. The experiment would allow the examination of plasma focusing devices for particle beams in the parameter regime of interest to future high-energy colliders. It is expected to lead to compact plasma lens designs capable of focusing the beam to unprecedented small spot sizes.     

Input-output finite-time stability of time-varying linear singular systems

This paper studies the input-output finite-time stabilization problem for time-varying linear singular systems. The output and the input refer to the controlled output and the disturbance input,respectively.Two classes of disturbance inputs are considered,which belong to L-two and L-infinity.Sufficient conditions are firstly provided which guarantee the input-output finite-time stability.Based on this,state feedback controllers are designed such that the resultant closed-loop systems are input-output finite-time stable.The conditions are presented in terms of differential linear matrix inequalities.Finally,an example is presented to show the validity of the proposed results.     

gain bounds for systems with sector bounded and slope‐restricted nonlinearities

This paper proposes a new method for calculating a bound on the gain of a system comprising a linear time invariant part and a static nonlinear part, which is odd, bounded, zero at zero and has a restriction on its slope. The nonlinear part is also assumed to be sector bounded, with the sector bound being (possibly) different from that implied by the slope restriction. The computation of the gain bound is found by solving a set of linear matrix inequalities, which arise from an integral quadratic constraint formulation of a multiplier problem involving both Zames‐Falb and Popov multipliers. Examples illustrate the effectiveness of the results, and comparisons are made against the state‐of‐the‐art. Copyright © 2011 John Wiley & Sons, Ltd.     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

具有随机丢包的网络化多自主体系统的一致性

研究具有随机丢包的网络化多自主体系统的均方一致性问题,其中不同自主体间的通信通道具有相同的丢包情况并且均服从马尔可夫(Markov)分布。首先,利用系统变换和迭代方法,得到了系统达到均方一致的一个初等充要条件。然后,利用矩阵理论和图论知识,如果拓扑图含有有向生成树,则可以将系统的均方一致性转化成一个线性Markov跳变系统的均方稳定性,并且可以建立线性矩阵不等式形式的均方可镇定准则。最后,通过相应的仿真实例说明了所得结论的可行性。     

不确定离散系统的鲁棒Gl2滤波器设计

对于凸多面体不确定离散系统,研究了满足误差估计系统鲁棒稳定且满足一定Gl₂性能指标的线性全阶稳定滤波器设计方法.分别给出了确定性和不确定性离散系统具有Gl₂性能的线性矩阵不等式（linear matrix inequality, LMI)充要条件和充分条件.在此基础上,通过使用滤波器参数化方法,分别推导出了以LMI描述的确定性和不确定性系统的Gl₂滤波器存在的充要条件和充分条件,进而可以使用LMI凸优化程序求解滤波器.通过对算例的计算,结果表明,在噪声信号为独立范数有界情况下,鲁棒Gl₂滤波比H_∞滤波具有更少的保守性.     

非线性滤波在射线图像降噪增强中的应用

射线数字成像是当前射线检测与诊断技术发展的方向，图像降噪增强是提高射线成像检测灵敏度和分辨力的关键。重点探讨以排序滤波为代表的非线性滤波图像降噪增强方法，对射线图像的处理表明，线性滤波在降噪的同时会引起较大的边级模糊，而排序滤波不仅降低噪声且能保持图像细节，提高图像质量效果明显。     

Static output feedback control via PDE boundary and ODE measurements in linear cascaded ODE–beam systems

This paper addresses the problem of feedback control design for a class of linear cascaded ordinary differential equation (ODE)–partial differential equation (PDE) systems via a boundary interconnection, where the ODE system is linear time-invariant and the PDE system is described by an Euler–Bernoulli beam (EBB) equation with variable coefficients. The objective of this paper is to design a static output feedback (SOF) controller via EBB boundary and ODE measurements such that the resulting closed-loop cascaded system is exponentially stable. The Lyapunov’s direct method is employed to derive the stabilization condition for the cascaded ODE–beam system, which is provided in terms of a set of bilinear matrix inequalities (BMIs). Furthermore, in order to compute the gain matrices of SOF controllers, a two-step procedure is presented to solve the BMI feasibility problem via the existing linear matrix inequality (LMI) optimization techniques. Finally, the numerical simulation is given to illustrate the effectiveness of the proposed design method.     

Decentralized observers with consensus filters for distributed discrete-time linear systems

This paper presents a decentralized observer with a consensus filter for the state observation of discrete-time linear distributed systems. Each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors’ estimates. It is proven that the state estimates of the proposed observer exponentially converge to the actual plant states under arbitrarily changing, but connected, communication and pseudo-connected sensing graph topologies. Except these connectivity properties, full knowledge of the sensing and communication graphs is not needed at the design time. As a byproduct, we obtained a result on the location of eigenvalues, i.e., the spectrum, of the Laplacian for a family of graphs with self-loops.     

Variance analysis of identified linear MISO models having spatially correlated inputs,with application to parallel Hammerstein models

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.