基于互质因子摄动的反馈系统的鲁棒稳定性

考虑对象和控制器同时具有互质因子摄动时闭环系统的鲁棒稳定性问题,得到了闭环系统鲁棒稳定的充要条件并给出了鲁棒控制器的优化设计方法.     

具有未知干扰的互质因子摄动不确定性系统最优鲁棒调节: 优化设计及连续性

研究了含未知干扰的互质因子摄动离散时间系统达到最优性能鲁棒性的控制器设计及这种设计的连续性. 给出了含未知干扰的互质因子摄动离散时间系统最优鲁棒调节器的一种设计方法. 分析了含未知干扰互质因子摄动系统的性能鲁棒性优化设计的连续性, 证明了互质因子摄动系统性能鲁棒性的优化设计作为从设计模型到鲁棒控制器的映射是连续的, 并且, 若设计模型的参数集是紧的, 这种映射是一致连续的.     

递推l1鲁棒便是方法研究

研究了基于含ｌ＾１范数约束非结构不确定性因子的模型集描述的动态系统的ｌ＾１鲁棒辨识问题。首先在较缭的先验条件下导出了受系统参数集为Ｒ＾ｎ空间一凸多面体;然后通过求解该多面体的最小外框给出了名义时域模型的一次完成辨识方法和限定数据窗递推辨识方法,并给出了辨识误差界;最后对递推辨识算法进行了理论分析,算例仿真表明了算法的有效性。     

最坏情况下l^1鲁棒辨识方法研究

系统地讨论了ＳＩＳＯ、线性时不变、指数稳定系统在最坏情况下的ｌ＾１鲁棒辨识问题。提出了系统模型集合的最小外框概念，建立了两种任意非零信号作用下ｌ＾１鲁棒辨识算法；提出了任意非零信号作用下系统的可辨识条件；证明了算法的全局收敛性和最优性。     

基于互质因子摄动的系统非脆弱鲁棒解耦控制

研究对象和控制器存在互质因子不确定性时闭环系统鲁棒稳定和非脆弱鲁棒解耦控制问题,得到了闭环系统具有鲁棒稳定性和鲁棒解耦性能的充分条件,给出了鲁棒控制器的优化设计方法.     

互质因子摄动下的最优鲁棒稳态跟踪控制器设计

研究了存在未知外部干扰时的互质因子摄动系统的鲁棒稳态追踪问题.利用Youla参数化方法，提出了一个最坏情况稳态绝对误差的精确计算公式.该公式使得最优稳态跟踪控制器设计问题等价于保证系统鲁棒稳定时e1的最优鲁棒控制器设计问题.     

面向l1鲁棒控制的系统辨识 *

本文研究面向ｌ＾１控制的辨识方法，提出了有理分式名义模型及最坏情况下辨识误差的ｌ＾１范数界的两步估计方法。由该方法得到的辨识模型集可直接用于ｌ＾１控制顺设计。     

不确定性离散时间系统非保守自适应鲁棒控制

研究含未知干扰和互质因子摄动离散时间不确定性系统的自适应鲁棒控制问题，为非保守的自适应鲁棒镇定提出一种广义参数递推估计方法，基于确定性等价原理，并利用ι1设计方法提出一种自适应鲁棒控制策略，证明了自适应算法的全局收敛性，给出了一个可验算的鲁棒稳定性条件，证明了该鲁棒稳定性条件是非保守和最优的。     

离散系统回路成形设计与鲁棒稳定性

本文首先给出了离散系统鲁棒稳定的充要条件及规范化互质分解因子摄动的鲁棒镇定问题,在此基础上讨论了如何在回路成形设计问题中考虑鲁棒稳定性的要求.     

具有结构不确定性的输出反馈线性系统的鲁棒稳定性分析

基于向量的幂变换方法，对具有结构不确定性的输出反馈线性系统的鲁棒稳定性问题作了分析。单参数摄动时给出了闭环系统鲁棒稳定的充要条件，多参数摄动时得到了保证系统鲁棒稳定的充分条件，导出了闭环系统鲁棒稳定区域的一种代数表达形式。最后给出了实例。     

Dynamic stereo with self-calibration

An approach for incremental refinement of disparity maps obtained from a dynamic stereo sequence of a static scene is presented. The approach has been implemented using a binocular stereo vision system mounted on a mobile robot. A robust least median of squares based algorithm is given for recovering the camera motion between successive viewpoints, which provides a self-calibration mechanism. The recovered motion is utilized for recursive disparity prediction and refinement using a robust Kalman filter model     

l_1系统辨识中的代数算法及其Worst－case误差

讨论单输入单输出、离散时不变因果系统的ｌ１系统辨识问题。首先提出基于代数方法的代数算法，并分析了该算法的特点；然后估计其Ｗｏｒｓｔ－ｃａｓｅ误差，并证明了该算法的收敛性；最后讨论了在某些特殊情况下该算法的相应形式。所给结果是面向鲁棒控制的。     

l~1鲁棒辨识收敛性态的研究

研究ｌ~１鲁棒辨识的渐近收敛性，提出一种有效的辨识算法，并分析了该算法的可行性和收敛性。     

Analysis of sparse representation and blind source separation

In this letter, we analyze a two-stage cluster-then-l(1)-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l(1) norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l(1)-norm solution and the l(0)-norm solution is also analyzed according to a probabilistic framework. If the obtained l(1)-norm solution is sufficiently sparse, then it is equal to the l(0)-norm solution with a high probability. Furthermore, the l(1)- norm solution is robust to noise, but the l(0)-norm solution is not, showing that the l(1)-norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed domain and with the case in which the source number is unknown. It is also robust to additive noise and estimation error in the mixing matrix. Finally, four simulation examples and an EEG data analysis example are presented to illustrate the algorithm's utility and demonstrate its performance.     

基于压缩传感的手写字符识别方法

基于新出现的压缩传感理论,提出了一种鲁棒的手写字符识别方法,能很好地对含有噪声的字符进行识别.该方法通过对测试字符进行稀疏表示,采用l1范数最小化算法求得最稀疏的系数解,所获得的系数具有明显的类别信息,从而易于对测试字符进行分类.实验结果表明,该方法具有很好的噪声鲁棒性.     

不确定广义大系统有限时间鲁棒分散控制

研究不确定连续广义大系统的有限时间鲁棒分散控制问题,设计系统的有限时间鲁棒分散状态反馈控制器.首先应用广义Lyapunov 函数法,给出不确定广义大系统有限时间鲁棒稳定的充分条件;其次,给出不确定广义大系统应用分散状态反馈控制器鲁棒镇定的充分条件和有限时间鲁棒分散控制器的设计方法;最后,通过仿真例子验证所提出方法的有效性.     

线性不确定系统经动态输出反馈的分层次控制策略

利用李雅谱诺夫稳定理论及线性矩阵不等式(LMI)方法, 研究了具有非结构摄动的不确定连续系统经动态输出反馈的二次鲁棒能稳定问题. 推导出这类控制器存在的充分条件, 即一个拟线性矩阵不等式(Q-LMI)的形式, 给出了Q-LMI问题的基于LMI方法的求解步骤. 为了使得Q-LMI问题有解, 可引入一些LMI约束, 提高了Q-LMI问题的可解性. 基于Q-LMI条件, 揭示了系统不确定参数界与系统控制规模的关系, 提出了“不同的摄动实施不同规模的控制”的鲁棒能稳定的分层次控制策略. 最后, 通过一个例子说明了     

An LMI approach to robust H-infinity control for uncertain singular time-delay systems

The problem of robust H-infinity control for a class of uncertain singular time-delay systems is studied in this paper. A new approach is proposed to describe the relationship between slow and fast subsystems of singular time- delay systems, based on which, a sufficient condition is presented for a singular time-delay system to be regular, impulse free and stable with an H-infinity performance. The robust H-infinity control problem is solved and an explicit expression of the desired state-feedback control law is also given. The obtained results are formulated in terms of strict linear matrix inequalities (LMIs) involving no decomposition of system matrices. A numerical example is given to show the effectiveness of the proposed method.     

An LMI approach to robust H-infinity control for uncertain singular time-delay systems

The problem of robust H-infinity control for a class of uncertain singular time-delay systems is studied in this paper. A new approach is proposed to describe the relationship between slow and fast subsystems of singular time- delay systems, based on which, a sufficient condition is presented for a singular time-delay system to be regular, impulse free and stable with an H-infinity performance. The robust H-infinity control problem is solved and an explicit expression of the desired state-feedback control law is also given. The obtained results are formulated in terms of strict linear matrix inequalities （LMIs） involving no decomposition of system matrices. A numerical example is given to show the effectiveness of the proposed method.     

基于观测器的l~1鲁棒故障检测方法

基于参数化的控制系统输出观测器 ,将鲁棒控制理论中的l1最优化方法用于故障检测技术 ,提出一种新的控制系统鲁棒故障检测方法 .通过求解一个混合 0 1型整数线性规划问题 ,可得出l1优化残差函数 .仿真结果验证了所提方法的有效性