This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem ...This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.展开更多
Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based softw...Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.展开更多
Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently fragmented.Such a decentralized nature of databases presents the serious challe...Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently fragmented.Such a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy concerns.Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client.Here we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning framework.It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly convex.Finally,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.展开更多
It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex ne...It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.展开更多
Designing an efficient distributed economic dispatch(DED)strategy for the smart grid(SG)in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power syst em,suc...Designing an efficient distributed economic dispatch(DED)strategy for the smart grid(SG)in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power syst em,such as easy implementation,low maintenance cos t,high energy efficiency,and strong robus tn ess agains t uncertainties.It has drawn a lot of interest from a wide variety of scientific disciplines,including power engineering,control theory,and applied mathematics.We present a state-of-the-art review of some theoretical advances toward DED in the SG,with a focus on the literature published since 2015.We systematically review the recent results on this topic and subsequently categorize them into distributed discrete-and continuous-time economic dispatches of the SG in the presence of multiple generators.After reviewing the literature,we briefly present some future research directions in DED for the SG,including the distributed security economic dispatch of the SG,distributed fast economic dispatch in the SG with practical constraints,efficient initialization-free DED in the SG,DED in the SG in the presence of smart energy storage batteries and flexible loads,and DED in the SG with artificial intelligence technologies.展开更多
A significant body of work on reinforcement learning has been focused on the single-agent tasks where the agent aims to learn a policy thatmaximizes the cumulative reward in a dynamic environment.1 In the past decades...A significant body of work on reinforcement learning has been focused on the single-agent tasks where the agent aims to learn a policy thatmaximizes the cumulative reward in a dynamic environment.1 In the past decades,quite a few single-agent-based reinforcement learning algorithms have been developed in the literature.1 Yet,it is increasingly recognized that the single-agentbased reinforcement learning algorithms may fail to effectively handle largescale optimization(decision)tasks with joint features.展开更多
基金supported in part by the National Natural Science Foundation of China(U22B2046,62073079,62088101)in part by the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(8091B022114)in part by NPRP(NPRP 9-466-1-103)from Qatar National Research Fund。
文摘This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.
基金supported in part by the National Natural Science Foundation of China(61621003,62073079,62088101,12025107,11871463,11688101)。
文摘Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role.Within this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge engineering.Obviously,the term“network”has different meanings in different scenarios.Meanwhile,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge engineering.This paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks.For the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering.Finally,some challenges and further trends are discussed.
基金supported by the National Natural Science Foundation of China(Grant Nos.92167201,52188102,62133003,61991403,61991404,and 61991400)Jiangsu Industrial Technology Research Institute(JITRI).
文摘Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently fragmented.Such a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy concerns.Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client.Here we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning framework.It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly convex.Finally,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.
基金This work was supported by the National Science Fund for Excellent Young Scholars[grant number 61322302]the National Science Fund for Distinguished Young Scholars[grant number 61025017]+3 种基金the National Natural Science Foundation of China[grant number 61104145],[grant number 61304168]the Natural Science Foundation of Jiangsu Province of China[grant number BK2011581],[grant number BK20130595]the Research Fund for the Doctoral Program of Higher Education of China[grant number 20110092120024]the Fundamental Research Funds for the Central Universities of China,and the Discovery Scheme under[grant number DP140100544].
文摘It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.
基金Project supported by the National Natural Science Foundation of China(Nos.61722303,61673104,and 61973133)the Six Talent Peaks Project of Jiangsu Province,China(No.2019-DZXX-006)the Australian Research Council(No.DP200101199)。
文摘Designing an efficient distributed economic dispatch(DED)strategy for the smart grid(SG)in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power syst em,such as easy implementation,low maintenance cos t,high energy efficiency,and strong robus tn ess agains t uncertainties.It has drawn a lot of interest from a wide variety of scientific disciplines,including power engineering,control theory,and applied mathematics.We present a state-of-the-art review of some theoretical advances toward DED in the SG,with a focus on the literature published since 2015.We systematically review the recent results on this topic and subsequently categorize them into distributed discrete-and continuous-time economic dispatches of the SG in the presence of multiple generators.After reviewing the literature,we briefly present some future research directions in DED for the SG,including the distributed security economic dispatch of the SG,distributed fast economic dispatch in the SG with practical constraints,efficient initialization-free DED in the SG,DED in the SG in the presence of smart energy storage batteries and flexible loads,and DED in the SG with artificial intelligence technologies.
文摘A significant body of work on reinforcement learning has been focused on the single-agent tasks where the agent aims to learn a policy thatmaximizes the cumulative reward in a dynamic environment.1 In the past decades,quite a few single-agent-based reinforcement learning algorithms have been developed in the literature.1 Yet,it is increasingly recognized that the single-agentbased reinforcement learning algorithms may fail to effectively handle largescale optimization(decision)tasks with joint features.
