带结构风险最小化的最优区间回归模型辨识

针对来自模型结构、参数以及测量数据的不确定性等因素,传统的辨识方法获取的是确定性数学模型的点输出,其鲁棒性差,易受外界干扰.因此,采用区间输出比点输出更易于实际问题的研究.基于复杂系统的不确定性测量数据以及系统参数的不确定性,提出了最优区间回归模型辨识的一种新方法,该方法将逼近误差的L∞范数思想与结构风险最小化理论相结合,建立求解区间模型的最优化问题,应用线性规划独立求解区间模型的上界和下界模型.该方法在保证模型辨识精度的同时,其泛化性能得到进一步提高.实验分析表明,提出的方法对来自噪声以及参数不确定性的数据,可以从区间模型的辨识精度和泛化性能之间取其平衡.     

基于l∞范数和l1范数最小化的二自由度最优鲁棒跟踪控制

研究了具有乘摄动模型不确定性并存在未知干扰系统的最优鲁棒跟踪控制问题,采用二自由度控制器结构Youla参数化方法将最优鲁棒跟踪控制问题转化为两个相互独立的优化问题,跟踪问题和鲁棒设计问题,跟踪问题以l∞范数为性能指标通过极小化跟踪误差的最大幅值实现最优跟踪控制;鲁棒性设计问题中,将模型不确定性视为一种外界干扰,通过极小化干扰到误差的灵敏度函数的l1范数使得干扰对跟踪误差的影响最小,通过截断处理,上述两种优化问题均可化为标准线性规划问题,给出了截断阶数与逼近误差之间的关系,仿真结果表明新方法的有效性。     

H∞ position transfer and regulation for floating offshore wind turbines

This paper proposes ${\rm H}_\infty$ controller design for platform position transfer and regulation of floating offshore wind turbines. The platform movability of floating wind turbines can be utilized in mitigating the wake effect in the wind farm, thereby maximizing the wind farm''s total power capture and efficiency. The controller is designed so that aerodynamic force is adjusted to meet the three objectives simultaneously, that is, 1) to generate the desired electrical power level, 2) to achieve the desired platform position, and 3) to suppress the platform oscillation. To acquire sufficient aerodynamic force to move the heavy platform, the pitch-to-stall blade pitching strategy is taken instead of the commonly-used pitch-to-feather strategy. The desired power level is attained by the standard constant-power strategy for the generator torque, while ${\rm H}_\infty$ state-feedback control of blade pitch and nacelle yaw angles is adopted for the position regulation and platform oscillation suppression. Weighting constants for the ${\rm H}_\infty$ controller design are adjusted to take the trade-off between the position regulation accuracy and the platform motion reduction. To demonstrate the efficiency of the proposed controller, a virtual 5-MW semi-submersible wind turbine is considered. Simulation results show that the designed ${\rm H}_\infty$ controller successfully accomplishes the platform position transfer and regulation as well as the platform oscillation reduction against wind and wave disturbances, and that it outperforms a previously-proposed linear quadratic controller with an integrator.     

基于l_∞范数和l_1范数最小化的二自由度最优鲁棒跟踪控制

研究了具有乘摄动模型不确定性并存在未知干扰系统的最优鲁棒跟踪控制问题 .采用二自由度控制器结构Youla参数化方法将最优鲁棒跟踪控制问题转化为两个相互独立的优化问题 :跟踪问题和鲁棒设计问题 .跟踪问题以l∞ 范数为性能指标通过极小化跟踪误差的最大幅值实现最优跟踪控制 ;鲁棒性设计问题中 ,将模型不确定性视为一种外界干扰 ,通过极小化干扰到误差的灵敏度函数的l1范数使得干扰对跟踪误差的影响最小 .通过截断处理 ,上述两种优化问题均可化为标准线性规划问题 .给出了截断阶数与逼近误差之间的关系 .仿真结果表明新方法的有效性 .     

传感器饱和的非线性网络化系统模糊\begin{document}$H_{\infty}$\end{document}滤波

研究了基于T-S模糊模型描述的非线性网络化系统$H_{\infty}$滤波器设计问题.由于网络诱导时滞的存在, 使得一个采样周期内, 到达接收端的数据可能是一个或多个, 也可能没有任何数据.提出传感器冗余策略解决由于饱和而引起的传感器件失效的问题.为降低结果的保守性, 选择模糊规则依赖的Lyapunov函数对滤波误差系统进行稳定性分析, 给出使滤波误差系统均方渐近稳定且具有指定$H_{\infty}$性能的充分条件, 滤波器参数通过求解一组线性矩阵不等式(Linear matrix inequalities, LMIs)得到.仿真研究结果表明算法的有效性.     

存在匹配/非匹配不确定性的飞机舵面故障$L_1$容错控制

针对飞机舵面故障时产生的各种内部未建模动态、系统不确定参数、未知输入增益等问题,提出一种同时存在匹配/非匹配不确定性的多输入多输出飞机舵面故障$L_1$容错控制方法.首先,推导出等效线性参数时变模型;然后,基于投影算子提出$L_1$自适应容错控制方法,推导Lyapunov方程,并证明稳定性;最后,分析所提方法的瞬态和稳态性能.仿真结果验证了所提出方法良好的容错性、鲁棒性和稳定性,并保证了系统各参数的瞬态和稳态有界性.     

混合l2/l1/2范数的局部组稀疏表示方法

当前基于稀疏表示的行人再识别都是通过松弛l₀正则项为l₁正则项以达到逼近l₀范数稀疏性的目的.在满足有限等距性质(RIP)条件下,l₁和l₀具有等价性,然而在具有杂乱背景、物体遮挡等众多干扰因素的行人再识别任务中,却很难满足RIP条件.因此,文中提出混合l₂/l_1/2范数的组稀疏表示方法,通过将gallery集中同一行人图像序列视为一组,利用l₂范数约束组内结构,l_1/2范数约束组间结构,对遮挡和杂乱背景等干扰因素具有更高的鲁棒性.为了进一步增强模型的判别性,引入人体结构约束,将行人图像划分为若干近邻块区域,针对每一区域分别构造适应性的混合l₂/l_1/2范数的组稀疏模型,最终融合全部稀疏模型得出再识别结果.在当前具有挑战性的2个多行人图像序列数据集PRID 2011和iLIDS-VID上的实验验证文中方法的有效性.     

范数有界型参数不确定性系统的概率鲁棒H∞控制方法

利用确定性鲁棒控制方法对参数摄动的最坏情况进行研究,设计出的控制器具有较大的保守性和较高的控制成本.针对这一问题,建立范数有界型参数不确定性系统模型,分析系统性能的置信概率与参数不确定性随机向量的范数边界之间的关系,提出一种基于概率估计的鲁棒H∞控制方法,该方法能以有限的迭代步数给出一个与系统性能置信概率相关的鲁棒H∞控制器的可行解.最后通过仿真实例验证了所提出方法的有效性.     

两重稀疏约束的多标记社团分类算法

在多标记研究中,对于标记间相关性的利用已经越来越广泛,从而标记关系的展示就很有必要。相对以往的研究而言,由于多标记数据的高维特征,在训练过程中极为繁琐耗时,稀疏优化就尤为关键;同时标记相关性的内涵没有经过深入挖掘,因此如何更方便有效地进行多标记分类以及研究所有标记之间的相关性显得尤为必要。提出了一种基于两重稀疏约束的多标记社团分类算法,该算法首先将?_1/?_2正则化应用到多标记数据的稀疏表示过程,为后面的研究提供便利条件;其次在多标记关系基础上应用基于?_1范数正则化的社团发现算法,有效地对标记进行社团划分,直观展示出标记关系的内涵。实验证明该方法能够快速、准确地进行多标记分类,并且能够准确展示标记关系。     

特征自表达和图正则化的鲁棒无监督特征选择

为了在揭示数据全局结构的同时保留其局部结构,本文将特征自表达和图正则化统一到同一框架中,给出了一种新的无监督特征选择(unsupervised feature selection,UFS)模型与方法。模型使用特征自表达,用其余特征线性表示每一个特征,以保持特征的局部结构;用基于 ${L_{2, 1}}$ 范数的图正则化项,在保留数据的局部几何结构的同时可以降低噪声数据对特征选择的影响;除此之外,在权重矩阵上施加了低秩约束,保留数据的全局结构。在6个不同的公开数据集上的实验表明,所给算法明显优于其他5个对比算法,表明了所提出的UFS框架的有效性。     

Distributed \begin{document}$H_{2}/H_\infty$\end{document} Filter Design for Discrete-Time Switched Systems

This paper addresses an infinite horizon distributed H2/H∞ filtering for discrete-time systems under conditions of bounded power and white stochastic signals. The filter algorithm is designed by computing a pair of gains namely the estimator and the coupling. Herein, we implement a filter to estimate unknown parameters such that the closed-loop multi-sensor accomplishes the desired performances of the proposed H2 and H∞ schemes over a finite horizon. A switched strategy is implemented to switch between the states once the operation conditions have changed due to disturbances. It is shown that the stability of the overall filtering-error system with H2/H∞ performance can be established if a piecewise-quadratic Lyapunov function is properly constructed. A simulation example is given to show the effectiveness of the proposed approach.     

L1 adaptive control with sliding-mode based adaptive law

This paper presents an adaptive control scheme with an integration of sliding mode control into the $\mathcal{L}_1$ adaptive control architecture, which provides good tracking performance as well as robustness against matched uncertainties. Sliding mode control is used as an adaptive law in the $\mathcal{L}_1$ adaptive control architecture, which is considered as a virtual control of error dynamics between estimated states and real states. Low-pass filtering mechanism in the control law design prevents a discontinuous signal in the adaptive law from appearing in actual control signal while maintaining control accuracy. By using sliding mode control as a virtual control of error dynamics and introducing the low-pass filtered control signal, the chattering effect is eliminated. The performance bounds between the close-loop adaptive system and the closed-loop reference system are characterized in this paper. Numerical simulation is provided to demonstrate the performance of the presented adaptive control scheme.     

A note on reverse scheduling with maximum lateness objective

The inverse and reverse counterparts of the single-machine scheduling problem $1||L_{\max }$ are studied in [2], in which the complexity classification is provided for various combinations of adjustable parameters (due dates and processing times) and for five different types of norm: $\ell _{1},\ell _{2},\ell _{\infty },\ell _{H}^{\Sigma } $ , and $\ell _{H}^{\max }$ . It appears that the $O(n^{2})$ -time algorithm for the reverse problem with adjustable due dates contains a flaw. In this note, we present the structural properties of the reverse model, establishing a link with the forward scheduling problem with due dates and deadlines. For the four norms $\ell _{1},\ell _{\infty },\ell _{H}^{\Sigma }$ , and $ \ell _{H}^{\max }$ , the complexity results are derived based on the properties of the corresponding forward problems, while the case of the norm $\ell _{2}$ is treated separately. As a by-product, we resolve an open question on the complexity of problem $1||\sum \alpha _{j}T_{j}^{2}$ .     

H∞ filtering for continuous fractional-order 2D Roesser model: The 0<α≤1 case

This paper investigates the $H_{\infty }$ filtering for the continuous fractional-order (FO) two-dimensional (2D) Roesser model with the FO between 0 and 1. Firstly, a sufficient condition to ensure the stability and bounded realness in the sense of $H_{\infty }$-norm for the continuous FO 2D Roesser model is given in the form of linear matrix inequalities (LMIs). Secondly, based on the bounded real lemmas proposed above, the $H_{\infty }$ filtering problem for continuous FO 2D Roesser model is addressed through some congruent transformation and matrix transformation. The results are given in LMI form, and the parameters of the continuous FO 2D filters can be achieved from the LMIs easily. In the end, the effectiveness of the proposed results is verified by two numerical examples.     

Observer-based path following control for autonomous vehicles with localization errors and tire slip effects

This paper investigates the problem of path following control for an autonomous vehicle subject to the localization errors and the tire slip effects. First, by analyzing the effects of localization errors, a loss-of-effectiveness actuator model is formulated, and a new chain form model is constructed for path following system of the vehicle. Then, the polytopic model is proposed to characterize the nonlinearity of the system, and an observer-based path following controller is designed by satisfying both the $H_{\infty }$ criterion and $L_1$ criterion. Finally, the path following controller design problem is converted into an optimization problem, which can be solved readily through convex optimization techniques. The effectiveness of the proposed control strategy is verified by simulation results.     

Toward structural sparsity: an explicit \ell _{2}/\ell _0 approach

As powerful tools, machine learning and data mining techniques have been widely applied in various areas. However, in many real-world applications, besides establishing accurate black box predictors, we are also interested in white box mechanisms, such as discovering predictive patterns in data that enhance our understanding of underlying physical, biological and other natural processes. For these purposes, sparse representation and its variations have been one of the focuses. More recently, structural sparsity has attracted increasing attentions. In previous research, structural sparsity was often achieved by imposing convex but non-smooth norms such as ${\ell _{2}/\ell _{1}}$ and group ${\ell _{2}/\ell _{1}}$ norms. In this paper, we present the explicit ${\ell _2/\ell _0}$ and group ${\ell _2/\ell _0}$ norm to directly approach the structural sparsity. To tackle the problem of intractable ${\ell _2/\ell _0}$ optimizations, we develop a general Lipschitz auxiliary function that leads to simple iterative algorithms. In each iteration, optimal solution is achieved for the induced subproblem and a guarantee of convergence is provided. Furthermore, the local convergent rate is also theoretically bounded. We test our optimization techniques in the multitask feature learning problem. Experimental results suggest that our approaches outperform other approaches in both synthetic and real-world data sets.     

Some Relations between Approximation Problems and PCPs over the Real Numbers

In [10] it was recently shown that that is the existence of transparent long proofs for was established. The latter denotes the class of real number decision problems verifiable in polynomial time as introduced by Blum et al. [6]. The present paper is devoted to the question what impact a potential full real number theorem would have on approximation issues in the BSS model of computation. We study two natural optimization problems in the BSS model. The first, denoted by MAX-QPS, is related to polynomial systems; the other, MAX-q-CAP, deals with algebraic circuits. Our main results combine the PCP framework over with approximation issues for these two problems. We also give a negative approximation result for a variant of the MAX-QPS problem.     

Interval estimation and fault detection for switched nonlinear systems based on zonotope method

This paper investigates the state interval estimation and fault detection (FD) problems for a Lipschitz nonlinear switched system based on the combination of the $H_{\infty }$ observer and the zonotope method. To begin with, an $H_{\infty }$ observer that is robust to the disturbance is designed, and the stability of the observer error dynamic system is analyzed under the average dwell time (ADT) concept. After this, the zonotope theory is applied on the $H_{\infty }$ observer error dynamic system such that the interval state estimation can be calculated iteratively when the system suffers from no fault. Furthermore, for FD purpose, a residual is constructed based on the $H_{\infty }$ observer. Because the constructed residual contains disturbance, it cannot be used for FD directly. To overcome this drawback, the interval estimation method of the residual is proposed by using the zonotope method, and a residual-based FD scheme is developed. Finally, a simulation example is given to verify the effectiveness of the proposed method.     

双论域上的广义直觉模糊概率粗糙集模型及其应用

已有的双论域直觉模糊概率粗糙集模型通过设置两个阈值${\lambda _1}$、${\lambda _2} $,讨论了经典集合在直觉模糊二元关系下的概率粗糙下上近似。该模型不能计算直觉模糊集合在直觉模糊二元关系下的概率粗糙下上近似,这在一定程度上限制了该模型的应用。首先给出了直觉模糊条件概率的定义。在直觉模糊概率空间下构造了双论域广义直觉模糊概率粗糙集模型,讨论了模型的主要性质。最后,将模型应用到临床诊断系统中。与其他模型相比,所提出的广义直觉模糊概率粗糙集模型进一步丰富了概率粗糙集理论,更适合于实际应用。