A Robust Controller for A 3‐DOF Flexible Robot with a Time Variant Payload

This article describes a new control scheme designed for a three degree of freedom (3‐DOF) flexible robot. The control scheme consists of two multi variable control loops. The inner loop is the motor's position control system, while the outer loop controls the robot tip's position, thus canceling vibrations which are originated by the structural flexibility of the manipulator during movement. As it will be shown, the outer control loop is robust to payload variations. The outer loop performance is based on a perfect cancelation of the inner loop dynamics. The effects of not achieving such perfect cancelation are also studied, and rules for designing a robust controller in this case are developed. Simulations assuming different payloads have been carried out with successful results for trajectory tracking. Trajectory tracking with a variable payload is also achieved.     

Bounded Consensus Algorithms for Multi‐Agent Systems in Directed Networks

In this paper, the consensus problem is investigated via bounded controls for the multi‐agent systems with or without communication. Based on the nested saturation method, the saturated control laws are designed to solve the consensus problem. Under the designed saturated control laws, the transient performance of the closed‐loop system can be improved by tuning the saturation level. First of all, asymptotical consensus algorithms with bounded control inputs are proposed for the multi‐agent systems with or without communication delays. Under these consensus algorithms, the states’ consensus can be achieved asymptotically. Then, based on a kind of novel nonlinear saturation functions, bounded finite‐time consensus algorithms are further developed. It is shown that the states’ consensus can be achieved in finite time. Finally, two examples are given to verify the efficiency of the proposed methods.     

基于特征参数归一化的鲁棒语音识别方法综述

目前,自动语音识别系统往往会因为环境中复杂因素的影响,造成训练环境和测试环境存在不匹配现象,使得识别系统性能大幅度下降,极大地限制了语音识别技术的应用范围。近年来,很多鲁棒语音识别技术成功地被提出,这些技术的目标都是相同的,主要是提高系统的鲁棒性,进而提高识别率。其中,基于特征的归一化技术简单而有效,常常被作为鲁棒语音识别的首选方法,它主要是通过对特征向量的统计属性、累积密度函数或功率谱的归一化来补偿环境不匹配产生的影响。该文主要对目前主流的归一化方法进行介绍,其中包括倒谱矩归一化方法、直方图均衡化方法以及调频谱归一化方法等。     

一种新的具有鲁棒性动态二进制时隙防碰撞算法

在射频识别系统中,由于信道共享的原因,会发生标签碰撞现象,从而导致阅读器的漏读,因此防碰撞算法在射频识别系统的设计中占有极其重要的地位。现有传统的动态二进制时隙防碰撞算法具有对标签要求不高的优点,非常适用于采用被动标签的RFID系统,但实际使用过程中,由于距离等外界因素会出现弱标签现象,现有算法对此不具有鲁棒性。因此,本文提出一种新的具有鲁棒性动态二进制时隙防碰撞算法,该算法在传统的动态二进制时隙防碰撞算法的基础上,增加一个未确定的标签队列,大大提高了系统的鲁棒性。实验显示该算法比传统BSA识别次数减少55%,同时保证了较好的鲁棒性。     

人脸特征的SIFT保护算法

人脸识别技术在门禁、视频监控等公共安全领域中的应用日益广泛,人脸特征数据的安全性和隐私性问题成为备受关注的焦点。提出一种基于SIFT的人脸特征安全保护新算法,首次将随机投影应用到对人脸特征数据的保护中。该算法首先利用SIFT特征对旋转、尺度缩放、光照变化等具有较好稳定性的特点,提取有较好鲁棒性的人脸数据;然后根据用户密钥对SIFT特征进行不可逆变换,生成具有可重建性的人脸特征模板数据,从而实现对人脸特征数据的保护。实验表明,该算法在CMU、AR和Feret人脸数据库中均取得较高的识别率,不仅对人脸特征数据具有保护作用,并且对姿势、遮挡和表情的变化具有较高的鲁棒性。     

Robust fuzzy stabilization of dithered chaotic systems using island-based random optimization algorithm

Applying dither to highly nonlinear systems may suppress chaotic phenomena, but dynamic performance, such as convergence rate and disturbance attenuation, is usually not guaranteed. This paper presents a dithered H_∞ robust fuzzy control scheme to stabilize chaotic systems that ensures disturbance attenuation bounds. In the proposed scheme, Takagi-Sugeno (T-S) fuzzy linear models are used to describe the relaxed models of the dithered chaotic system, and fuzzy controllers are designed based on an extension to the concept of parallel distributed compensation (PDC). Sufficient condition for the existence of the H_∞ robust fuzzy controllers is presented in terms of a novel linear matrix inequalities (LMI) form which takes full consideration of modeling error and disturbances, but cannot be solved by the standard procedures. In order to solve the LMI problem and to identify the chaotic systems as T-S fuzzy modes, we propose a compound optimization strategy called the island-based random-walk algorithm (IRA). The algorithm is composed of a set of communicating random-walk optimization procedures concatenated with the down-hill simplex method. The design procedure and validity of the proposed scheme is demonstrated via numerical simulation of the dithered fuzzy control of a chaotic system.     

不确定随机时滞系统的鲁棒H∞保性能控制

讨论了一类具有时变时滞的不确定It类型随机系统的鲁棒H∞保性能控制问题. 运用Lyapunov-Krasovskii泛函方法, 设计使得闭环系统鲁棒随机指数均方稳定, 且具有给定H∞干扰抑制度 γ 的状态反馈保性能控制器. 控制器存在的充分条件以线性矩阵不等式(LMIs)的形式表示. 进一步, 通过求解具有LMIs约束的凸优化问题, 给出了不确定随机时滞系统的最优保性能控制器的设计方法. 最后通过一个数值例子验证了所提方法的有效性.     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.     

具有圆盘极点约束的不确定切换系统的鲁棒控制

本文研究了一类具有圆盘极点约束的不确定线性切换系统的鲁棒镇定问题．对此类系统设计了状态反馈控制器,使得闭环系统的所有极点均位于一个给定的圆盘中,并得到了一个基于LMI的充分条件．通过求解线性矩阵不等式,可以构造一个具有较小反馈增益参数的状态反馈控制器．最后的仿真算例证明了本文结果的有效性。