IEEE Transactions on Automatic Control

最新文章

• Distributed Discrete-Time Dynamic Outer Approximation of the Intersection of Ellipsoids AI翻译
  作者：Sebastian, Eduardo; Aldana-Lopez, Rodrigo; Aragues, Rosario; Montijano, Eduardo; Sagues, Carlos
  作者单位：University of Zaragoza; University of Zaragoza
  摘要：This article presents the first discrete-time distributed algorithm to track the tightest ellipsoids that outer approximates the global dynamic intersection of ellipsoids. Given an undirected network, we consider a setup where each node measures an ellipsoid, defined as a time-varying positive semidefinite matrix. The goal is to devise a distributed algorithm to track the tightest outer approximation of the intersection of all the ellipsoids. The solution is based on a novel distributed reform...
• Learning Dynamical Systems by Leveraging Data From Similar Systems AI翻译
  作者：Xin, Lei; Ye, Lintao; Chiu, George; Sundaram, Shreyas
  作者单位：Purdue University System; Purdue University; Chinese University of Hong Kong; Huazhong University of Science & Technology; Purdue University System; Purdue University; Purdue University in Indianapolis; Purdue University System; Purdue University
  摘要：We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide a finite sample error bound of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary dat...
• Multiagent Reinforcement Learning for Constrained Markov Decision Processes by Consensus-Based PrimalDual Method AI翻译
  作者：Cui, Gaochen; Jia, Qing-Shan; Guan, Xiaohong
  作者单位：Tsinghua University; Xi'an Jiaotong University
  摘要：In this work, we consider multiagent reinforcement learning for constrained Markov decision processes and develop a consensus-based primal-dual method to solve the problem, which is model-free and with provable convergence. Compared with existing methods, our algorithm does not require the dynamic model of the system, nor ask the agents to share their local policies. The constraint is incorporated in the objective function to form the Lagrangian with the dual variables updated through the prim...
• Data-Driven Mode Detection and Stabilization of Unknown Switched Linear Systems AI翻译
  作者：Eising, Jaap; Liu, Shenyu; Martinez, Sonia; Cortes, Jorge
  作者单位：University of California System; University of California San Diego; Swiss Federal Institutes of Technology Domain; ETH Zurich; Beijing Institute of Technology; University of California System; University of California San Diego
  摘要：This article considers the stabilization of unknown switched linear systems using data. Instead of a full system model, we have access to a finite number of trajectories of each of the different modes prior to the online operation of the system. On the basis of informative enough measurements, we design an online switched controller that alternates between a mode detection phase and a stabilization phase. Since the currently active mode is unknown, the controller employs online measurements to...
• Efficient Reconstruction of Neural Mass Dynamics Modeled by Linear-Threshold Networks AI翻译
  作者：Wang, Xuan; Cortes, Jorge
  作者单位：George Mason University; University of California System; University of California San Diego
  摘要：This article studies the data-driven reconstruction of firing rate dynamics of brain activity described by linear-threshold network models. Identifying the system parameters directly leads to a large number of variables and a highly nonconvex objective function. Instead, our approach introduces a novel reformulation that incorporates biological organizational features and turns the identification problem into a scalar variable optimization of a discontinuous, nonconvex objective function. We p...
• Consensus for Multiagent Systems With Input Delay and Observation Packet Loss AI翻译
  作者：Wang, Zhenhua; Xie, Lihua
  作者单位：Shandong Normal University; Nanyang Technological University
  摘要：In this article, the mean-square consensus problem for linear discrete multiagent systems over both undirected and directed graphs is explored. The agent dynamics in the network is influenced by both input delay and observation packet loss. The packet loss probabilities are allowed to be nonidentical for every agent. An optimal predictor-based protocol is developed, and conditions ensuring the mean-square consensus are provided, which are expressed in terms of system dynamics, graph parameters...
• Differentially Private Distributed Algorithms for Aggregative Games With Directed Communication Graphs AI翻译
  作者：Guo, Kai-Yuan; Wang, Yan-Wu; Luo, Yun-Feng; Xiao, Jiang-Wen; Liu, Xiao-Kang
  作者单位：Huazhong University of Science & Technology; Huazhong University of Science & Technology
  摘要：Due to the transmission of information during seeking the Nash equilibrium and the possible leaking of sensitive information deduced from the transmitted information, it is urgent to propose privacy-preserving seeking algorithms for aggregative games. This article proposes two & varepsilon;-differentially private distributed Nash equilibrium seeking algorithms for aggregative games under directed communication graphs with row- and column-stochastic adjacency matrices, respectively. By utilizin...
• Accelerating Iterative Learning Control Using Fractional-Proportional-Type Update Rule AI翻译
  作者：Li, Zihan; Shen, Dong; Yu, Xinghuo
  作者单位：Renmin University of China; Renmin University of China; Royal Melbourne Institute of Technology (RMIT)
  摘要：Using the proportional-type update rule (PTUR) is the most common update approach for iterative learning control. By combining PTUR and a newly proposed fractional-power-type update rule (FTUR), a fractional-proportional-type update rule is proposed to achieve fast convergence for scenarios where the tracking errors can be large or small. The nonlinearity of fractional power term and tracking error accumulation along the time axis introduce considerable challenges in convergence analysis and c...
• On Semiconvergence, Eventual Positivity, and Accretiveness of Signed Laplacians: A Spectral Abscissa Versus Log-Norm Perspective AI翻译
  作者：Zhu, Shouchong; Tian, Ye; Chen, Wei
  作者单位：Peking University; Peking University
  摘要：In this note, we study the relationships among semiconvergence, eventual exponential positivity, and accretiveness of repelling signed Laplacians in a broader context from a spectral abscissa versus logarithmic norm (log-norm) perspective. We give both algebraic and geometrical characterizations of when spectral abscissa equals log-norm for a general real matrix. Furthermore, we characterize the interplay among spectral abscissa, eventual exponential positivity, and log-norm. These results, wh...
• Predictability Verification of Fault Patterns in Labeled Petri Nets AI翻译
  作者：Cong, Xuya; Yu, Zhenhua; Fanti, Maria Pia; Mangini, Agostino Marcello; Li, Zhiwu
  作者单位：Xi'an University of Science & Technology; Politecnico di Bari; Macau University of Science & Technology
  摘要：This article focuses on the predictability verification problem of fault patterns for both bounded and unbounded discrete event systems modeled with labeled Petri nets. A system is said to be predictable with respect to a fault pattern if any complete fault behavior in a fault pattern can be correctly predicted before its occurrence, where the fault patterns are characterized by a particular composition of a labeled Petri net with a fault pattern net. In this article, we construct a fault patt...