In this paper,we review the development of a phase theory for systems and networks in its first five years,represented by a trilogy:Matrix phases and their properties;The MIMO LTI system phase response,its physical in...In this paper,we review the development of a phase theory for systems and networks in its first five years,represented by a trilogy:Matrix phases and their properties;The MIMO LTI system phase response,its physical interpretations,the small phase theorem,and the sectored real lemma;The synchronization of a multi-agent network using phase alignment.Towards the end,we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.展开更多
The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered.This problem is an important component of...The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered.This problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated learning.We propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost functions.We show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of iterations.We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum.We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.展开更多
Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as t...Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.展开更多
In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the par...In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the parameters of the filteres is established to ensure the consistency,i.e..the mean square error is upper bounded by a known parameter matrix at each time.We apply the consistent fiters to the track to-track association analysis of multi targets with uncertain dynamics.A novel track to-track asocaion algoritm is proposed to idenify whether two tracks are from the same target.It is proven that the resulting probability of mis.asociation is lower than the desired threshold.Numerical simulations on track.to track association are given to show the ffetives of the methods.展开更多
A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy sensors.This paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from act...A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy sensors.This paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a context.First,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM filter.Next,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private information.The authors show how to estimate such posterior distributions from observed optimal actions taken by the agent.In the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal actions.Applications range from financial portfolio investments to life science decision systems.展开更多
A fnite.-time consensus protocol is proposed for multi -dimensional multi- agent systems, using direction peserving signumcontrols. Flipp solutions and nonsmooh analysis tehniques are adopted to handle discontinuities...A fnite.-time consensus protocol is proposed for multi -dimensional multi- agent systems, using direction peserving signumcontrols. Flipp solutions and nonsmooh analysis tehniques are adopted to handle discontinuities. Suficient and ncessaryconditions are provided to guarantee infinte time convergence and boundedness of the solutions. It turns out that the numberof agents which have cotinuous contol law plays an ssenan role in fnite-tine conerence In adidio it is shown thatthe unit bals itoduced bylp, norms. where p ∈[1,∞] , are inariat for the closed lop.展开更多
A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate...A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.展开更多
This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation.Distinct error variables and hyper-variables a...This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation.Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities,accelerations and higher order information of neighbors.Consequently,our approach only requires agents to exchange position information or to measure the relative positions of the neighbors.The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players.Furthermore,after establishing a sufficient and necessary condition for a class of high-order integrators,we present conditions for capture and formation control with exponential convergence for three scenarios:one-pursuer-one-evader,multiple-pursuer-one-evader,and multiple-pursuer-multiple-evader.It is shown that the conditions depend on the structure of the communication graph,the weights in the control law,and the expected formation configuration.Finally,the effectiveness of the proposed algorithm is demonstrated through simulation results.展开更多
基金supported in part by the National Natural Science Foundation of China(62073003,72131001)Hong Hong Research Grants Council under GRF grants(16200619,16201120,16205421,1620-3922)Shenzhen-Hong Kong-Macao Science and Technology Innovation Fund(SGDX20201103094600006)。
文摘In this paper,we review the development of a phase theory for systems and networks in its first five years,represented by a trilogy:Matrix phases and their properties;The MIMO LTI system phase response,its physical interpretations,the small phase theorem,and the sectored real lemma;The synchronization of a multi-agent network using phase alignment.Towards the end,we also summarize a list of ongoing research on the phase theory and speculate what will happen in the next five years.
基金supported by the Knut and Alice Wallenberg Foundationthe Swedish Foundation for Strategic Research+1 种基金the Swedish Research Councilthe National Natural Science Foundation of China(62133003,61991403,61991404,61991400)。
文摘The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is considered.This problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated learning.We propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost functions.We show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of iterations.We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum.We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.
基金supported in part by the National Natural Science Foundation of China(No.62273287)by the Shenzhen Science and Technology Innovation Council(Nos.JCYJ20220530143418040,JCY20170411102101881)the Thousand Youth Talents Plan funded by the central government of China.
文摘Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.
基金the National Natural Science Foundation of China(Nos.11931018,61973299)the Beijing Advanced Innovation Center for Intelligent Robots and Systems(No.2019IRS09).
文摘In this paper,we study how to design filters for nonlinear uncertain systems over sensor networks.We intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute frameworks.Moreover,the tuning method for the parameters of the filteres is established to ensure the consistency,i.e..the mean square error is upper bounded by a known parameter matrix at each time.We apply the consistent fiters to the track to-track association analysis of multi targets with uncertain dynamics.A novel track to-track asocaion algoritm is proposed to idenify whether two tracks are from the same target.It is proven that the resulting probability of mis.asociation is lower than the desired threshold.Numerical simulations on track.to track association are given to show the ffetives of the methods.
基金the Wallenberg AIAutonomous Systems and Software Program(WASP)the Swedish Research Council and the Swedish Research Council Research Environment NewLEADS under contract 2016-06079。
文摘A hidden Markov model(HMM)comprises a state with Markovian dynamics that can only be observed via noisy sensors.This paper considers three problems connected to HMMs,namely,inverse filtering,belief estimation from actions,and privacy enforcement in such a context.First,the authors discuss how HMM parameters and sensor measurements can be reconstructed from posterior distributions of an HMM filter.Next,the authors consider a rational decision-maker that forms a private belief(posterior distribution)on the state of the world by filtering private information.The authors show how to estimate such posterior distributions from observed optimal actions taken by the agent.In the setting of adversarial systems,the authors finally show how the decision-maker can protect its private belief by confusing the adversary using slightly sub-optimal actions.Applications range from financial portfolio investments to life science decision systems.
文摘A fnite.-time consensus protocol is proposed for multi -dimensional multi- agent systems, using direction peserving signumcontrols. Flipp solutions and nonsmooh analysis tehniques are adopted to handle discontinuities. Suficient and ncessaryconditions are provided to guarantee infinte time convergence and boundedness of the solutions. It turns out that the numberof agents which have cotinuous contol law plays an ssenan role in fnite-tine conerence In adidio it is shown thatthe unit bals itoduced bylp, norms. where p ∈[1,∞] , are inariat for the closed lop.
基金All authors are partially supported by the Wallenberg AI,Autonomous Systems and Software Program(WASP)funded by the Knut and Alice Wallenberg Foundation.The first and second authors are additionally supported by the ELLIIT Network Organization for Information and Communication Technology,Swedenthe Swedish Foundation for Strategic Research SSF(Smart Systems Project RIT15-0097)+1 种基金The second author is also supported by a RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology,ChinaThe fifth and eighth authors are additionally supported by the Swedish Research Council.
文摘A research arena(WARA-PS)for sensing,data fusion,user interaction,planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented.The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges.The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration.This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles.The motivating application for the demonstration is marine search and rescue operations.A state-of-art delegation framework for the mission planning together with three specific applications is also presented.The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles.The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles,and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments.The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility.It would be most difficult to do experiments on this large scale without the WARA-PS research arena.Furthermore,these demonstrator activities have resulted in effective research dissemination with high public visibility,business impact and new research collaborations between academia and industry.
基金supported by KTH Digital Futures Postdoctoral Program.
文摘This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation.Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities,accelerations and higher order information of neighbors.Consequently,our approach only requires agents to exchange position information or to measure the relative positions of the neighbors.The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players.Furthermore,after establishing a sufficient and necessary condition for a class of high-order integrators,we present conditions for capture and formation control with exponential convergence for three scenarios:one-pursuer-one-evader,multiple-pursuer-one-evader,and multiple-pursuer-multiple-evader.It is shown that the conditions depend on the structure of the communication graph,the weights in the control law,and the expected formation configuration.Finally,the effectiveness of the proposed algorithm is demonstrated through simulation results.
