[06ac]Robust sampled-data control of linear singularly perturbed systems

异质多智能体系统在固定拓扑下的分组一致性闻国光;黄俊;于玉洁【摘要】研究了异质多智能体系统的分组一致性,针对固定通信拓扑情况,提出了一种基于邻接信息的分布式控制协议,并通过李雅普诺夫理论,推导出异质多智能体系统实现分组一致性的充分条件.最后利用数值仿真验证了理论分析的正确性.【期刊名称】《北京交通大学学报》【年(卷),期】2016(040)003【总页数】5页(P115-119)【关键词】异质多智能体系统;分组一致性;李雅普诺夫理论【作者】闻国光;黄俊;于玉洁【作者单位】北京交通大学理学院,北京100044;北京交通大学理学院,北京100044;北京交通大学理学院,北京100044【正文语种】中文【中图分类】O175近年来, 由于多智能体系统在生物系统、机器人编队、传感器网络、无人驾驶飞行器编队、水下行驶器和群体决策问题等方面的广泛应用，而越来越受到来自控制科学、数学、生物学、计算机科学等领域学者的关注.目前，在多智能体系统协调控制的研究中，一些研究课题已经取得了令人鼓舞的结果，例如：群集控制、蜂拥控制、一致性控制、聚集控制、区域覆盖、编队控制等.其中，多智能体系统的一致性控制问题可以描述为：通过对邻居智能体的局部信息设计分布式控制策略，使得智能体系统中某些重要的状态量随着时间的变化最终趋于一致.可以说一致性问题是多智能体系统协调控制一个重要的基本问题.2004年，Olfati-Saber和Murray[1]给出了研究连续时间一阶多智能体系统一致性控制问题的一个系统框架.在他们的模型中，每个智能体的动力学由一阶积分器来描述i(t)=ui(t),i=1,2,…,n，其中，xi和ui分别是智能体的位置和速度.2005年，Ren 等进一步研究了一阶连续和离散多智能体系统的一致性问题，证明了如果存在一系列有界的时间区间，使得在每个区间内智能体间的通信拓扑图包含有向生成树，那么一阶多智能体系统能够达到一致性.与此同时，Moreau等[2]考虑了一阶离散非线性多智能体系统的一致性控制问题，并得到了一系列实现一阶离散非线性多智能体系统一致性的条件.2007年，Lin等[3]对一阶连续非线性多智能体系统的一致性控制问题进行了研究.随着这个话题的发展，人们得到了大量新的关于带有不同模型和控制策略的一阶多智能体系统的结果.在现实中，考虑到许多系统的运动需要用位置和速度来共同刻画，二阶多智能体系统一致性控制被广泛研究.Ren等[4]以及Xie等[5]研究了二阶多智能体系统的一致性控制问题，并分别给出了一些具有固定通信拓扑和切换通信拓扑结构的多智能体系统实现一致性的条件.最近，在文献[6]中，作者研究了带有采样信息的二阶多智能体系统一致性问题.现有的关于多智能系统一致性的研究成果主要是基于同质多智能体系统建立的，即假设每个子系统具有相同的动力学.然而，在实际工程应用中，当不同种类的智能体分享共同目标时，由于外界影响或交流条件的限制，发生耦合的每个智能体的动力学系统可能不同.此时，采用异质动力学模型所模拟的带有不同特性和能力的多机器人网络在现实世界中具有更强的应用性.目前，异质多智能体系统一致性的研究文献，主要是考虑一阶系统和二阶系统的混合[7-8].文献[9]通过应用幂积分方法和李雅普诺夫理论，针对由一阶和二阶智能体组成的异质多智能体系统的一致性问题提出了两种有限时间一致性算法.在文献[10]中，Zheng等研究了由一阶和二阶智能体组成的混合系统的一致性问题.而以上有关异质多智能体系统的研究都是异一致性.随着研究的深入，我们发现分组一致性的适用范围更广，更具研究意义.对于由多个小组构成的复杂网络，分组一致性意味着每个组的智能体可以达到一致性，而不同组收敛到不同的值.事实上，分组一致性更加符合自然和人类社会，因为在一些现实的情况中，比如细菌克隆模式的构造、个人意见的簇状构造会根据环境、实际情况、协作任务甚至时间一直变化[11-12].在入度平衡的假设下，Yu等[13]利用线性矩阵不等式给出了在固定拓扑下保证一致性的充分条件.通过在分组协议中应用笛卡尔坐标，Xie等[14]对于时间连续的多智能体系统建立了实现分组一致性的充要条件.现有的关于多智能体系统的文献中，或者考虑异质多智能体系统一致性，或者考虑同质分组一致性.而关于异质多智能体系统分组一致性方面研究还较少.然而，研究具有异质多智能体系统的分组一致性控制具有现实意义和理论意义.因此，本文作者研究异质多智能体系统，针对固定通信拓扑的情况，提出了一种基于邻居信息的分组一致性控制协议，并通过稳定性理论等分析方法，推导出了系统实现分组一致性的充分条件，最后使用数值仿真验证了理论分析的正确性.该研究将会对异质多智能体系统分组一致性控制理论在多机器人合作控制、交通车辆控制、无人飞机编队，智能交通、现代医疗、勘探、营救、以及网络资源分配等实际问题的应用提供进一步的理论基础和技术支持.1.1 图论基础在一个多智能体网络中，假设每个多智能体是一个质点，由这些多智能体构成一个无向图，图中的每条边代表每个多智能体之间的信息交流.每个智能体根据从它的邻接智能体接受的信息来更新自身的状态.设G=(ε,V,A)是一个加权有向图，节点集合为V=(v1,v2,…,vn)，边集合ε⊆V×V，边eij=(vi,vj)∈ε意味着节点vj能从节点vi接收信息.A表示加权有向图的邻接矩阵.在A=[aij]n×n中，aij是大于等于0的常数，若eji∈ε则aij>0；若eji∉ε则aij=0.若在图G对应的邻接矩阵A中，有aij=aji则称这个图G是无向图.在有向图G中，若任意两个不同的节点之间都有一条路，则称这个有向图G是强连通的.在无向图G中，连通也叫做强连通.在有向图G中，如果存在一个节点和其它所有节点之间至少有一条路，则称这个有向图G 有有向生成树.对于给定的多智能体系统，若用有向图G来模拟所有智能体之间的信息交流，那么称G为多智能体系统的通信拓扑.用Ni={vj|eji∈ε}来表示节点vi 的邻接点集合.此外，定义图G的拉普拉斯矩阵为L=[lij]n×n，,n.结点i的度定义为.1.2 系统模型考虑由m个二阶智能体和n-m个一阶智能体构成的异质多智能体系统.二阶智能体系统表示如下：其中xi(t)∈Rn，vi(t)∈Rn，ui(t)∈Rn分别表示第i个智能体位置，速度，控制输入.此外，一阶智能体的动力学系统表示如下其中xi(t)∈Rn，ui(t)∈Rn分别表示第i个智能体位置，控制输入.设在一个由k组(k≥2)构成的多智能体系统中，如果智能体属于第t个组，则记σi=t，令xσi是对智能体进行分组的常数，且当σi=σj时，有xσi=xσj，当σi≠σj 时，有xσi≠xσj.定义1 对于任意的初始状态值xi(0)和vi(0)，若异质多智能体系统满足以下条件则称其逐渐达到k组一致性(k≥2).注1 为了方便起见，本文都是基于一维空间，即xi(t),vi(t),ui(t)∈R.然而，我们在一维空间里得到的所有结果都可以通过克罗内克积(Kronecker product)推广到n维空间.引理1[15] 考虑一个形式为的自治系统，f是连续的，令:Rn→R是一阶偏导连续的标量函数.假设1)当‖x‖→，2)∀令S是Rn中使=0的点的集合，M是S中最大的不变子集，则当t→时，Rn中所有解趋近于M.定义控制器如下：其中k1>0，k2>0是控制参数.在这部分中，我们将讨论异质多智能体系统在固定拓扑下的分组一致性.定理1 设智能体间的通信拓扑图是无向连通的，由式(1)和式(2)组成的异质多智能体系统在控制器(5)下可以达分组一致性.证明根据控制器(5)，可以将式(1)～(2)改写成：令ei=xi-xσi,i=1,2…,n，则有根据和 ei=xi-xσi，系统(6)可以写作选择李雅普诺夫函数如下：其中V是一个正函数.对V求导，得因为通信拓扑图是无向的，即邻接矩阵A=[aij]n×n是对称的，我们可以得到将式(9)和式(10)代入式(8)，得然后利用拉萨尔不变集原理.设，M是S中最大的不变子集.当=0时，有所以从式(7)和式(11)中可得M如下从式(12)中可得两式相加得基于之前提到的性质和式(15)，最大不变子集M化为如下形式将ei=xi-xσi代入式(16)，可以得到综上所述，定理1得证.在本节中，我们将通过仿真来验证其理论结果.考虑一个由3个具有二阶动力学的智能体(智能体1、2、3)和4个具有一阶动力学的智能体(智能体4、5、6、 7)构成的异质多智能体系统，它们的通信拓扑见图1.我们将智能体分为两组G1和G2，智能体1、2、4、5属于G1组，智能体3、6、7属于G2组.从图1可以看出通信拓扑是无向连通的.为了简化计算，我们假设邻接矩阵中的权重都为1.令k1=3，k2=1,记t为系统运行的时间，智能体的初始位置和速度是随机的，取系统初始速度为T，初始位移为xT(0)=[0,1.2,0.3,2,0.8,3.8,1.7]T，图2显示了所有智能体的状态轨迹，从图2的仿真结果中可以看出系统能够渐进收敛达到分组一致性，验证了定理的正确性.本文研究异质多智能体系统在固定无向通信拓扑下的分组一致性问题.文中提出一个基于邻居信息的分布式分组一致性的控制协议，并给出了使系统达到分组一致性的充分条件.最后用数值仿真验证了理论的正确性.未来，我们将进一步考虑在切换拓扑下，异质多智能体系统的分组一致性问题.【相关文献】[1] OLFATI-SABER R, MURRAY R M. 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Robust ControlRobust control is a field of engineering and control theory that focuses on designing systems that can perform effectively and reliably in the presence of uncertainties and variations. This is particularly important in applications where the environment or operating conditions are unpredictable, such as in aerospace, automotive, and manufacturing systems. The goal of robust control is to ensurethat the system can maintain stability and performance despite these uncertainties, providing a level of resilience that is essential for real-world applications. The development of robust control can be traced back to the early days of control theory, which emerged in the early 20th century with the work of engineers and mathematicians such as Bode, Nyquist, and others. The early focus of controltheory was on developing methods to analyze and design systems that could maintain stability and performance under ideal conditions. However, as control systems began to be applied to real-world problems, it became clear that these methods were not sufficient to address the uncertainties and variations that are inherentin practical systems. In response to this challenge, researchers began to develop new techniques and approaches to address robustness. One of the key developmentsin this area was the concept of H-infinity control, which emerged in the 1980s and provided a framework for designing controllers that could provide guaranteed performance in the presence of uncertainties. This marked a significant shift in the field of control theory, as it provided a rigorous mathematical foundation for addressing robustness in control systems. From a historical perspective, the development of robust control can be seen as a response to the limitations of traditional control methods in addressing real-world uncertainties. This has ledto a rich and diverse set of techniques and approaches for designing robustcontrol systems, ranging from classical methods such as loop shaping and robustPID control to modern approaches based on optimization and convex analysis. These developments have been driven by the need to address practical challenges in awide range of applications, leading to a field that is constantly evolving and adapting to new problems and opportunities. One of the key debates in the fieldof robust control is the trade-off between performance and robustness. Traditional control methods often focus on optimizing performance under ideal conditions,which can lead to designs that are sensitive to uncertainties and variations. In contrast, robust control methods prioritize resilience and stability, often at the expense of performance. This has led to a lively debate within the control community about the best approach to designing control systems, with some researchers advocating for a balanced approach that considers both performance and robustness, while others argue for the primacy of robustness in critical applications. To illustrate the importance of robust control, consider the example of an autonomous vehicle. In this application, the vehicle must be able to navigate safely and effectively in a wide range of conditions, including varying road surfaces, weather conditions, and traffic patterns. A control system that is not robust to these uncertainties could lead to unsafe or unreliable performance, potentially putting lives at risk. By contrast, a robust control system canprovide the level of resilience and stability that is essential for the safe operation of autonomous vehicles, highlighting the critical importance of robust control in modern engineering applications. Despite the clear benefits of robust control, there are also challenges and limitations associated with this approach. One of the key challenges is the complexity of designing robust control systems, which often requires sophisticated mathematical tools and techniques. This can make it difficult for practitioners to apply robust control methods in practice, particularly in applications with limited resources or expertise in control theory. Additionally, there is a trade-off between robustness and performance, as mentioned earlier, which can make it challenging to design control systems that achieve both goals simultaneously. Looking to the future, the field of robust control is likely to continue to evolve and adapt to new challenges and opportunities. With the increasing complexity and interconnectedness of modern engineering systems, the need for robust control will only grow in importance.This will require ongoing research and development to advance the state of the art in robust control methods, as well as efforts to make these techniques more accessible and practical for engineers and practitioners. By addressing these challenges, the field of robust control has the potential to make a significant impact on the safety, reliability, and performance of a wide range of engineeringsystems, ensuring that they can operate effectively in the face of uncertainties and variations.。

Robust ControlRobust control is a field of engineering and mathematics that deals with the design and implementation of control systems that can effectively operate in the presence of uncertainties and variations in the system. This is an important area of study because in real-world applications, it is often difficult to accurately model and predict all the variables and disturbances that can affect a system. Robust control techniques aim to ensure that the system remains stable and performs as expected, even in the presence of these uncertainties. One of the key challenges in robust control is dealing with parametric uncertainties, which refer to variations in the parameters of the system model. For example, in a mechanical system, the mass or friction coefficients of the system components may not be known precisely, and they can vary over time due to factors such as wear and tear. Robust control techniques address these uncertainties by designing controllersthat can accommodate a range of possible parameter values, ensuring that the system remains stable and performs well under all these conditions. Another important aspect of robust control is handling unmodeled dynamics, which are aspects of the system behavior that are not captured in the system model. This can include phenomena such as unmodeled friction, disturbances, or external forcesthat are not accounted for in the control design. Robust control techniques aim to design controllers that can effectively reject these unmodeled dynamics and maintain the desired system performance. From a practical perspective, robust control is crucial in many engineering applications. For example, in aerospace engineering, it is essential to design aircraft control systems that can handle variations in the aircraft's aerodynamic properties, as well as disturbances such as gusts of wind. Similarly, in automotive engineering, robust control is important for designing vehicle control systems that can accommodate variations in road conditions and vehicle dynamics. From a theoretical perspective, robust control is a rich and challenging area of research, with connections to various branches of mathematics and engineering. It draws on concepts from control theory, optimization, and system identification, and it requires a deep understanding of mathematical tools such as linear matrix inequalities and convex optimization. Researchers in robust control are constantly developing new techniques andalgorithms to address the complexities of real-world systems and to push the boundaries of what is possible in terms of system performance and robustness. In conclusion, robust control is a critical area of study in engineering and mathematics, with both practical and theoretical significance. It addresses the challenges of designing control systems that can effectively operate in the presence of uncertainties and variations, and it has wide-ranging applications in fields such as aerospace, automotive, and robotics. From a theoretical perspective, robust control is a challenging and rich area of research that requires a deep understanding of mathematical concepts and tools. Overall, robust control plays a crucial role in ensuring the stability and performance of complex engineering systems in the face of real-world uncertainties.。

Robust Control and Estimation Robust control and estimation are two critical aspects of control engineering that have been extensively studied over the years. These two concepts areessential in ensuring that a system can operate optimally in the presence of uncertainties and disturbances. In this response, we will explore the concept of robust control and estimation from multiple perspectives, including its importance, key principles, and applications. From a general perspective, robust control and estimation are essential in ensuring that a system can operate optimally in the presence of uncertainties and disturbances. In many engineering applications, itis often challenging to predict the exact behavior of a system due to external factors such as noise, disturbances, and other uncertainties. As such, robust control and estimation techniques are used to design control systems that can operate efficiently under these conditions. By incorporating robust control and estimation principles into a system's design, engineers can ensure that the system can maintain its performance and stability even when subjected to disturbances and uncertainties. One of the key principles of robust control is the use of feedback control. Feedback control is a control technique that uses measurements of a system's output to adjust its input in real-time. By using feedback control, engineers can design control systems that are more robust to disturbances and uncertainties. For example, in a temperature control system, feedback control can be used to adjust the heater's output based on the temperature measurements taken from the system. This allows the system to maintain its temperature setpoint even when subjected to external disturbances such as changes in ambient temperature. Another important principle of robust control is the use of model-based control. Model-based control involves using a mathematical model of the system being controlled to design a control system that can operate optimally under different conditions. By using a model-based approach, engineers can design control systems that are more robust to uncertainties and disturbances. For example, in a robotic arm control system, a mathematical model of the arm's dynamics can be used to design a control system that can maintain the arm's position and orientation even when subjected to external disturbances. Robust estimation is another critical aspect of control engineering that is closely related to robust control. Robustestimation involves using statistical techniques to estimate a system's state or parameters in the presence of uncertainties and disturbances. By using robust estimation techniques, engineers can ensure that a system can accurately estimate its state or parameters even when subjected to external disturbances. For example, in a navigation system, robust estimation techniques can be used to estimate the position and velocity of a vehicle even when subjected to external disturbances such as wind and road conditions. The applications of robust control and estimation are diverse, ranging from aerospace and automotive systems toindustrial automation and robotics. In aerospace applications, robust control and estimation are used to design control systems for aircraft that can maintain their stability and performance even when subjected to external disturbances such as turbulence and wind gusts. In automotive applications, robust control and estimation are used to design control systems for vehicles that can maintain their stability and performance even when subjected to external disturbances such as road conditions and wind gusts. In industrial automation and robotics, robust control and estimation are used to design control systems for machines and robots that can operate optimally under different conditions. For example, in a manufacturing plant, robust control and estimation can be used to design control systems for machines that can maintain their performance and stability even when subjected to external disturbances such as changes in material properties and environmental conditions. In robotics, robust control and estimation can be used to design control systems for robots that can maintain their position and orientation even when subjected to external disturbances such as collisions and changes in payload. In conclusion, robust control and estimation are two critical aspects of control engineering that have been extensively studied over the years. By incorporating robust control and estimation principles into a system's design, engineers can ensure that the system can maintain its performance and stability even when subjected to disturbances and uncertainties. The principles of feedback control and model-based control are essential in designing robust control systems, while robust estimation techniques are essential in accurately estimating a system's state or parameters in the presence of uncertainties and disturbances.The applications of robust control and estimation are diverse, ranging from aerospace and automotive systems to industrial automation and robotics.。

JUNE 2014 « IEEE CONTROL SYSTEMS MAGAZINE 105(Registration cochair), Sandra Hirche (Student Activities chair), Pradeep Misra (Electronic Services coordina-tor), Felice Andrea Pellegrino (Public-ity chair), Ann Rundell (Registration cochair), and Andrea Serrani (Publi-cation chair). We also wish to express our sincere gratitude to the sponsors for their generous support. L ast, but not least, we would like to thank Ros-sella Spangaro and “The Office” team for their superb management of the financial and logistic details of the conference and the many students and volunteers of the Universities of Benevento, Firenze, and Trieste for their enthusiastic support during the event.CONCLudING REMARkSQuoting Jay Farrell’s concluding remarks in his CDC 2012 report, “serv-ing as general chair of an IEEE CDC is a once-in-a-lifetime responsibil-ity and opportunity,” and we cannot agree more. This personal journey for us started in an airport lounge on the way back to Italy after the 2008 CDC. During these five years of service, we always felt the support and trust of the CSS as a professional society and of many colleagues and friends. Nodoubt that all our efforts have been motivated by trying to make the first CDC in Italy a memorable event.It is impossible to relate in these short remarks all our memories about this fantastic adventure. So we want to share with the readership two pic-tures (Figure 2). The first was taken on Friday, December 6. We sat at the registration desk exhausted in front of the empty conference center. Look-ing at our faces, it is evident that we could not believe some 1600 attend-ees could be accommodated with the high technical and social standards of our flagship conference. Our faces in the second picture show that, by themiddle of the conference, we started relaxing because the atmosphere was great, the sessions were well attended, and our colleagues and friends enjoyed one of the most beautiful cit-ies in the world. This is an unforget-table reward for us. We are looking forward to the 2014 CDC in Los Ange-les to share some good memories of this fantastic experience that touched our lives.Thomas Parisini andRoberto Tempo General Cochairs,CDC 2013(a)(b)FIGURE 2 (a) Thomas Parisini, Roberto Tempo, Rossella Spangaro, Laura Appiani, and Veronica Simeone before the conference started. (b) Thomas Parisini and Roberto Tem-po during one of the social events.The 25th Chinese Control and decision ConferenceDigital Object Identifier 10.1109/MCS.2014.2308731Date of publication: 12 May 2014The 25th Chinese Control and De-cision Conference (2013 CCDC) was held in Guiyang, China, May 25–27, 2013. The 2013 CCDC was co-organized by Northeastern University, China, the Industrial Electronics (IE) Chapter of the IEEE Singapore Sec-tion, and Harbin Section Chapter of the IEEE Control Systems Society (CSS). The local organizer was Guizhou Uni-versity, China. The conference was technically cosponsored by the CSS, the Systems Engineering Society of China, the Chinese Association for Ar-tificial Intelligence, and the Technical Committee on Control Theory of Chi-nese Association of Automation.TECHNICAL PROGRAMThe 2013 CCDC received 1513 full paper submissions from authors from 26 countries and regions. After going through a rigorous review process—during which all the members in the Technical Program Committee worked professionally, responsibly, and dili-gently—998 papers were included in the technical program for presentation in 99 oral sessions and nine interactive sessions. The conference was attended by 650 delegates.In addition to the regular technical sessions, the technical program also included five keynote addresses and seven distinguished lectures, covering the state of the art in both theory and106 IEEE CONTROL SYSTEMS MAGAZINE » JUNE 2014applications in control, decision, auto-mation, robotics, and emerging technol-ogies. The five keynote addresses were»“Perception-Enabled Control—A New Paradigm for Biomimet-ics and Machine Autonomy” by John Baillieul, Boston Univer-sity, United States»“Recursive Approach to System Identification” by Han-Fu Chen, Chinese Academy of Sciences, China»“Sc ience, Tec h nolog y a nd Industry, Quo Vadis?” by Okyay Kaynak, Bogazici University, Turkey»“Signal Processing via Sampled-data Control—Beyond Shan-non” by Yutaka Yamamoto, Kyoto University, Japan»“Hardware-in-Loop Simula-tion” by Zicai Wang, Harbin Institute of Technology, China.Distinguished lectures were delivered by Xiaohua Xia of the University of Pretoria, South Africa, “Applications of Model Predictive Control in Opera-tion Efficiency Optimization,” Ji-Feng Zhang of the Chinese Academy of Sciences, China, “How Much Infor-mation Is Needed for a Given Control Task,” Jagannathan Sarangapani of the Missouri University of Science and Technology, United States, “Optimal Adaptive Control of Uncertain Non-linear Dynamic Systems,” Jun Wang of The Chinese University of Hong Kong, China, “Neurodynamic OptimizationApproaches to Robust Pole Assign-ment,” Jie Chen of City University of Hong Kong, China, “When Is a Time-Delay System Stable and Stabilizable?,” Guoxiang Gu of Jiangnan University, China, “New Perspectives of Consen-sus Control for Multi-agent Systems,” and Jiafu Tang of Northeastern Univer-sity, China, “Weight Vehicle Routing Problems and Beam Search Combined Max-Min Ant System.”All keynote addresses, distin-guished lectures, oral sessions, and interactive sessions were well attended and produced active discussions. The conference banquet, which included entertainment, was held in the evening of May 26, 2013.A CD-ROM that contained all papers presented at the conference was provided to each registered del-egate. The official conference proceed-ings were published by IEEE and are included in IEEE Xplore .ZHANG SI-YING (CCdC)OuTSTANdING YOuTH AWARdThe Zhang Si-Ying (CCDC) Outstand-ing Youth Paper Award is in recogni-tion of Zhang Si-Ying’s highly regarded perseverance, character, and academic contribution. The award also aims to inspire, motivate, and encourage young scholars in their research. For apaper to be eligible for the award, the first author of the paper must not be older than 35 on the day of the award presentation at the conference. The award is 3000 RMB with a certificate.The Award Committee for the 2013 CCDC was composed of five members:Zhong-Ping Jiang, United States, John Baillieul, United States, Changyun Wen, Singapore, Xin He Xu, China, and Guang-Hong Yang, China.For the 2013 CCDC, 133 papers were selected for consideration for theZhang Si-Ying Outstanding Youth PaperJohn Baillieul delivering a keynote address.Han-Fu Chen delivering a keynote address.Opening ceremony of the 2013 CCDC.Okyay Kaynak delivering a keynote address.Yutaka Yamamoto during his keynote session.JUNE 2014 « IEEE CONTROL SYSTEMS MAGAZINE 107Award based on reviewers’ comments,nominations, and the evaluations of theTechnical Program Committee mem-bers. These papers were sent to famousexperts including some members of theInternational Advisory Committee forfurther evaluation. Based on their evalu-ations and recommendation, the Techni-cal Program Committee short-listed fivepapers as the finalists for the Award:»“Exponential Stability of a Cou-pled Heat-ODE System” by Dong-Xia Zhao and Jun-Min Wang, Beijing Institute of Technology»“Global Exponential Stability of Nonlinear Impulsive Discrete System with Time Delay” by Kexue Zhang and Xinzhi L iu, University of Waterloo»“Nonlinear Oscillations in TCPnetworks with Drop-Tail Buffers” by Nizar Malangadan, Haseen Rahman, and Gaurav Raina, Indian Institute of Technology»“Globally Adaptive Path Track-ing Control of Under-actuated Ships” by Wei Wang and Jiangsh-uai Huang, Tsinghua University »“Finite Time Containment Con-trol of Nonlinear Multiagent Networks” by Di Yu, Lijuan Bai, and Weijian Ren, Northeast Petroleum University.During the conference, all Award Committee members attended the oral presentations of the five finalist papers. Each member independently assessedtheir originality, technical quality, and written and oral presentations. On the basis of these assessments, the 2013 CCDC Zhang Si-Ying Outstanding Youth Paper Award was presented toWei Wang from Tsinghua University.The International Advisory Commit-tee Chair of 2013 CCDC, Si-Ying Zhang, presented certificates to all the finalists and presented the award with a certifi-cate to the winner during the banquet.THE 26TH CHINESE CONTROLANd dECISION CONFERENCEThe 26th CCDC (2014 CCDC) will be held in Changsha, China, May 31 to June 2, 2014. Keynote addresses will be deliv-ered by Alessandro Astolfi of Imperial College, United Kingdom, Daizhan Cheng of the Chinese Academy of Sci-ences, and Jay A. Farrell of University of California, Riverside, United States. The Organizing Committee will invite prominent researchers as well as dis-tinguished lecturers. Information about the 2014 CCDC will be consistently updated on the conference Web site: .Changyun Wen andZhong-Ping Jiang International TechnicalProgram ChairsA well-attended banquet.2013 CCDC award committee members and finalists of the Zhang Si-Ying Outstanding Youth Award.Ke Fang, who delivered the keynote ad-dress for Prof. Zicai Wang.。

Robust ControlRobust control is a field of engineering and mathematics that deals with the design and implementation of control systems that can effectively handle uncertainty and variations in the system being controlled. This is an important area of study because in real-world applications, systems are often subject to various disturbances and uncertainties that can affect their behavior. Robust control aims to ensure that the system remains stable and performs as desired despite these uncertainties. One perspective on robust control is from the standpoint of aerospace engineering. In the field of aerospace, robust control is crucial for ensuring the stability and performance of aircraft and spacecraft. These vehicles operate in highly uncertain and dynamic environments, and their control systems must be able to adapt to changing conditions while maintaining stability and safety. Robust control techniques such as H-infinity control and mu-synthesis have been widely used in aerospace applications to design control systems that can handle uncertainties in the aerodynamic properties of the vehicle, variations in the operating conditions, and disturbances such as gusts and turbulence. Another perspective on robust control comes from the field of robotics. In robotics, control systems must be able to handle uncertainties in the dynamics of the robot and variations in the environment in which it operates. Robust control techniques such as sliding mode control and adaptive control have been applied to develop robot control systems that can maintain stability and achieve desired performance in the presence of uncertainties. This is particularly important for applications such as autonomous vehicles, where the robot must be able to operate in diverse and changing environments. From a theoretical perspective, robust control is grounded in the field of control theory and optimization. The development of robust control techniques involves the use of mathematical tools such as linear matrix inequalities (LMIs), convex optimization, and frequency domain analysis. These tools are used to formulate and solve the robust control design problem, which involves finding a control law that ensures stability and performance for a given range of uncertainties. The theoretical aspects of robust control are often complex and require a deep understanding of mathematical concepts, making it a challenging but intellectually rewarding fieldof study. In the industrial automation and process control domain, robust control plays a critical role in ensuring the stability and performance of industrial processes. Many industrial processes are subject to uncertainties and disturbances, such as variations in the parameters of the process, external disturbances, and equipment faults. Robust control techniques such as model predictive control (MPC) and robust PID control have been applied to address these challenges and improve the performance of industrial processes. Robust control is essential for maintaining the safety, efficiency, and quality of industrial operations, makingit an integral part of modern industrial automation systems. One of the key challenges in robust control is the trade-off between performance and robustness. In many cases, increasing the robustness of a control system may come at the cost of reduced performance, and vice versa. For example, a control system that is designed to be highly robust to uncertainties may exhibit conservative behaviorand may not be able to achieve the desired level of performance. On the other hand, a control system that is optimized for performance may be sensitive touncertainties and disturbances, leading to instability or poor performance in the presence of such disturbances. Balancing the trade-off between performance and robustness is a fundamental challenge in robust control design, and it requires careful consideration of the specific requirements and constraints of the application. In conclusion, robust control is a critical area of study in engineering and mathematics, with applications in aerospace, robotics, industrial automation, and many other fields. It addresses the challenge of designing control systems that can handle uncertainties and variations in the system being controlled, ensuring stability and performance in the presence of disturbances. Robust control techniques have been developed and applied in diverse domains, and they continue to be an active area of research and development. The theoreticaland practical aspects of robust control present both challenges and opportunities for engineers and researchers, making it a fascinating and important field of study.。

Robust ControlRobust control is a field of engineering and mathematics that focuses on designing systems that can perform effectively in the presence of uncertaintiesand variations. This is especially important in applications where the environment or operating conditions are unpredictable, such as in aerospace, automotive, and industrial control systems. The goal of robust control is to ensure that the system can maintain stability and performance despite these uncertainties, providing a reliable and consistent operation. One of the key challenges inrobust control is dealing with the inherent uncertainties in system dynamics, external disturbances, and measurement noise. Traditional control design methods often assume perfect knowledge of the system and its operating conditions, which can lead to poor performance or even instability when these assumptions are violated. Robust control techniques, on the other hand, explicitly account for these uncertainties and aim to provide guarantees on the system's behavior underall possible scenarios. There are several approaches to robust control, each with its own strengths and weaknesses. One common technique is H-infinity control,which seeks to minimize the worst-case performance of the system while taking into account the uncertainties. This approach is particularly well-suited for systems with hard performance requirements and where there is a need to explicitlyquantify the trade-offs between performance and robustness. Another popular approach to robust control is mu-synthesis, which is based on the theory of structured singular value analysis. This method allows for the design ofcontrollers that explicitly account for the structured uncertainties in the system, such as parametric variations or unmodeled dynamics. By considering these uncertainties in the controller design, mu-synthesis can provide improved robustness compared to traditional methods. In addition to these techniques,there are also other robust control methods such as loop-shaping, gain-scheduling, and model predictive control, each with its own set of advantages and limitations. The choice of method depends on the specific requirements of the application, as well as the available knowledge of the system and its uncertainties. From a practical perspective, robust control has a wide range of applications in various industries. For example, in aerospace engineering, robust control is essential forensuring the stability and performance of aircraft and spacecraft in the presence of uncertain aerodynamic forces, atmospheric disturbances, and sensor noise. Similarly, in automotive systems, robust control is used to design vehiclestability and traction control systems that can operate effectively under varying road conditions and driver inputs. In industrial automation, robust control is critical for maintaining the performance of manufacturing processes and robotic systems in the presence of uncertainties such as variations in material properties, external disturbances, and wear and tear. By incorporating robust control techniques into the design of these systems, engineers can ensure reliable operation and consistent performance, even in challenging and unpredictable environments. Despite its many benefits, robust control also has its limitations and challenges. One of the main difficulties in robust control is the trade-off between performance and conservatism. Because robust control techniques aim to provide guarantees under all possible uncertainties, they often lead to conservative designs that sacrifice some performance in order to ensure stability and robustness. This can be particularly challenging in applications where high performance is critical, such as in high-speed control systems or precision motion control. Another challenge in robust control is the complexity of the design process. Unlike traditional control design, which often relies on analytical or empirical methods, robust control design typically involves complex mathematical optimization and analysis techniques. This can make the design process more time-consuming and resource-intensive, requiring specialized expertise andcomputational tools. In addition, robust control techniques often requireaccurate knowledge of the system and its uncertainties, which may not always be available in practice. For example, in many real-world applications, the exact dynamics of the system or the magnitude of the uncertainties may be unknown or difficult to quantify. This can make it challenging to apply robust control techniques effectively and may require additional efforts in system identification and uncertainty quantification. Despite these challenges, the benefits of robust control in terms of reliability, safety, and performance make it an important and valuable tool for engineers and researchers. By addressing the uncertainties and variations inherent in complex systems, robust control techniques can provide alevel of confidence and assurance that is essential for critical applications in aerospace, automotive, and industrial systems. In conclusion, robust control is a vital field of study with wide-ranging applications and significant impact on various industries. By addressing uncertainties and variations in system dynamics, robust control techniques provide a means to ensure reliable and consistent performance, even in challenging and unpredictable environments. While there are challenges and limitations associated with robust control, its benefits in terms of stability, performance, and reliability make it an essential tool for engineers and researchers working in complex and uncertain systems. As technology continues to advance and new challenges emerge, the development and application of robust control techniques will remain a critical area of research and innovation.。

Robust ControlRobust control is a field of control theory that deals with the design of controllers that can handle uncertainties and disturbances in the system. The goal of robust control is to ensure that the system remains stable and performs well even in the presence of uncertainties and disturbances. In this response, I will discuss the importance of robust control, the challenges associated with designing robust controllers, and some of the techniques used in robust control. Robust control is important because most real-world systems are subject to uncertainties and disturbances. These uncertainties can arise from various sources, such as changes in the environment, sensor noise, or modeling errors. If a controller is designed without considering these uncertainties, the system may become unstableor perform poorly. Robust control techniques ensure that the controller can handle these uncertainties and disturbances, leading to a more reliable and robust system. Designing robust controllers is challenging because uncertainties and disturbances can be difficult to model and quantify. In addition, the design of robustcontrollers often requires trade-offs between performance and robustness. For example, a controller that is very robust to uncertainties may not provide thebest performance, while a controller that provides optimal performance may not be robust enough to handle uncertainties. Therefore, designing a robust controller requires careful consideration of the trade-offs between performance and robustness. There are several techniques used in robust control. One of the most common techniques is H-infinity control, which is based on the H-infinity norm.The H-infinity norm is a measure of the maximum gain from disturbances to theoutput of the system. H-infinity control seeks to minimize this gain whileensuring that the system remains stable. Another technique used in robust controlis mu-synthesis, which is based on the mu-norm. The mu-norm is a measure of the worst-case gain from uncertainties to the output of the system. Mu-synthesis seeks to minimize this gain while ensuring that the system remains stable. In additionto these techniques, there are also several other approaches to robust control,such as sliding mode control, adaptive control, and nonlinear control. Slidingmode control is a technique that uses a discontinuous control law to force the system to follow a desired trajectory. Adaptive control is a technique thatadjusts the controller parameters in real-time based on the system's behavior. Nonlinear control is a technique that uses nonlinear models to design controllers that can handle nonlinearities in the system. Overall, robust control is acritical field of study in control theory. It ensures that controllers can handle uncertainties and disturbances, leading to more reliable and robust systems. However, designing robust controllers is challenging and requires careful consideration of the trade-offs between performance and robustness. There are several techniques used in robust control, including H-infinity control, mu-synthesis, sliding mode control, adaptive control, and nonlinear control. Each of these techniques has its own strengths and weaknesses, and the choice of technique depends on the specific requirements of the system.。

Robust ControlRobust control, within the realm of engineering and specifically in the domain of control theory, is a concept that holds paramount importance in ensuring the stability and performance of systems in the face of uncertainties and variations.It's a fascinating area that delves into the intricacies of designing control systems capable of withstanding unforeseen disturbances and fluctuations, makingit an indispensable tool in various fields such as aerospace, automotive, robotics, and more. At its core, robust control seeks to address the inherent uncertainties and variations present in real-world systems. Unlike traditional control methods that assume perfect knowledge of system dynamics and disturbances, robust control acknowledges the presence of uncertainties and aims to develop controllers thatcan effectively handle them. This is particularly crucial in applications where system parameters may vary over time, environmental conditions are unpredictable, or disturbances occur unexpectedly. One of the key perspectives in robust control is the notion of stability. In conventional control design, stability is typically analyzed under ideal conditions, neglecting the effects of uncertainties. However, in robust control, stability analysis takes into account the worst-case scenario, ensuring that the system remains stable even in the presence of the most adverse conditions. This is achieved through techniques such as μ (mu) synthesis and H-infinity control, which provide robust stability guarantees by optimizingcontroller performance across a range of possible uncertainties. Beyond stability, robust control also addresses the performance aspect of control systems. While stability ensures that the system remains bounded under all conditions, performance deals with how well the system tracks reference signals and rejects disturbances. Robust control techniques aim to strike a balance between stability and performance, ensuring that the system not only remains stable but alsoachieves satisfactory performance in the presence of uncertainties. Another important perspective in robust control is that of trade-offs. In the design of robust controllers, engineers often face trade-offs between various conflicting objectives such as stability, performance, and robustness. For example, increasing the robustness of a controller may come at the expense of performance, or vice versa. Balancing these trade-offs requires careful consideration of systemrequirements and constraints, as well as a deep understanding of the underlying dynamics and uncertainties. Moreover, robust control extends beyond the design phase to implementation and real-world deployment. In practice, robust controllers must be validated and tested extensively to ensure that they perform as expected under various operating conditions. This involves conducting simulation studies, hardware-in-the-loop testing, and field trials to evaluate the robustness and performance of the controller in realistic scenarios. Additionally, robust control algorithms may need to be adapted or retuned over time to accommodate changes in system dynamics or operating conditions. In conclusion, robust control represents a fundamental paradigm shift in control theory, offering a systematic approach to designing controllers that can effectively handle uncertainties and variations in real-world systems. By focusing on stability, performance, trade-offs, and practical implementation considerations, robust control enables engineers to develop control systems that are not only stable and reliable but also capable of delivering satisfactory performance in the face of uncertainties. As technology continues to advance and systems become increasingly complex, the importance of robust control will only continue to grow, making it an indispensable tool for engineers across various disciplines.。