3期

最新文章

• Hierarchical Selective Recruitment in Linear-Threshold Brain Networks-Part I: Single-Layer Dynamics and Selective Inhibition AI翻译
  作者：Nozari, Erfan; Cortes, Jorge
  作者单位：University of Pennsylvania; University of California System; University of California San Diego
  摘要：Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control theory as well as insights and structures from neuroscience. Central to this framework is an...
• Sliding Mode-Based Robustification of Consensus and Distributed Optimization Control Protocols AI翻译
  作者：Pilloni, Alessandro; Franceschelli, Mauro; Pisano, Alessandro; Usai, Elio
  作者单位：University of Cagliari
  摘要：This article proposes a design approach, based on the integral sliding mode control paradigm, devoted to give robustness to multiagent systems executing arbitrary distributed optimization and consensus protocols which do not take this feature into account. Robustness is understood as the capability of rejecting the effect of exogenous disturbances, parameter uncertainties, and uncertain couplings between the agents dynamics, by achieving the same emerging behavior as that corresponding to a re...
• Maximum Correntropy Rauch-Tung-Striebel Smoother for Nonlinear and Non-Gaussian Systems AI翻译
  作者：Wang, Guoqing; Zhang, Yonggang; Wang, Xiaodong
  作者单位：China University of Mining & Technology; China University of Mining & Technology; China University of Mining & Technology; Harbin Engineering University; Columbia University
  摘要：We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-Gaussian process and measurement noises, i.e., the nominal Gaussian noise is polluted by large noise from unknown distributions. Taking advantage of correntropy in handling non-Gaussian noise, a robust Rauch-Tung-Striebel smoother is derived according to the maximum-correntropy-criterion-based cost functions with nonlinear functions linearized by their first-order Taylor series expansions, where two weigh...
• Solution to Delayed Forward and Backward Stochastic Difference Equations and Its Applications AI翻译
  作者：Xu, Juanjuan; Wang, Wei; Zhang, Huanshui
  作者单位：Shandong University
  摘要：In this article, we will study a class of forward and backward stochastic difference equations (discrete-time FBSDEs) with time delay. By establishing a nonhomogeneous relationship between the forward and backward stochastic processes, we give the explicit solution of the discrete-time FBSDEs with time delay in terms of a class of Riccati-like equations. The explicit solution is of great significance in solving the stochastic control problem. To show this point, we study a stochastic leader-fo...
• Distributed Adaptive Output Feedback Control for Multiagent Systems With Unknown Dynamics AI翻译
  作者：Zhang, Ying; Su, Youfeng; Wang, Xinghu
  作者单位：Fuzhou University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
  摘要：In this article, we present a novel distributed adaptive output feedback control for the cooperative output regulation problem of linear multiagent systems. The agent plant is general in the sense that those interesting practical issues such as unknown dynamics, directed network graph, and agents with nonidentical relative degrees are taken into account simultaneously. The obtained controller consists of the reduced-order observer, the internal model, as well as the adaptive control law, all o...
• Distributed Newton's Method for Network Cost Minimization AI翻译
  作者：Cao, Xuanyu; Liu, K. J. Ray
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Maryland; University of Maryland College Park
  摘要：In this article, we examine a novel generic network cost minimization problem, in which every node has a local decision vector to optimize. Each node incurs a cost associated with its decision vector, while each link incurs a cost related to the decision vectors of its two end nodes. All nodes collaborate to minimize the overall network cost. The formulated network cost minimization problem has broad applications in distributed signal processing and control, in which the notion of link costs o...
• Linear Convergence of Consensus-Based Quantized Optimization for Smooth and Strongly Convex Cost Functions AI翻译
  作者：Kajiyama, Yuichi; Hayashi, Naoki; Takai, Shigemasa
  作者单位：University of Osaka
  摘要：This article proposes a distributed optimization method for minimizing the sum of smooth and strongly convex functions with a finite communication bandwidth. Each agent has a state and an auxiliary variable to estimate the optimal solution and the average gradient of the global cost function. To cooperatively estimate the optimal solution, agents exchange the states and the auxiliary variables with their neighbors over weight-balanced networks by a dynamic encoding and decoding scheme. After t...
• Identification of FIR Systems With Binary Input and Output Observations AI翻译
  作者：Leong, Alex S.; Weyer, Erik; Nair, Girish N.
  作者单位：Defence Science & Technology; University of Melbourne
  摘要：This article considers the identification of finite-impulse response systems, where information about the inputs and outputs of the system undergoes quantization into binary values before transmission to the estimator. In the case where the thresholds of the input and output quantizers can be adapted, we propose identification schemes that are strongly consistent for Gaussian distributed inputs and noises. The algorithms are based on the idea that certain joint probabilities of the unquantized...
• Efficient Learning for Selecting Important Nodes in Random Network AI翻译
  作者：Li, Haidong; Xu, Xiaoyun; Peng, Yijie; Chen, Chun-Hung
  作者单位：Peking University; Ateneo de Manila University; Peking University; George Mason University
  摘要：In this article, we consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance is characterized by the stationary probabilities of the corresponding nodes in a Markov chain defined over the network, as in Google's PageRank. Unlike a deterministic network, the transition probabilities in a random network are unknown but can be estimated by sampling. Under a Bayesian learning fra...
• Contract-Based Design of Symbolic Controllers for Safety in Distributed Multiperiodic Sampled-Data Systems AI翻译
  作者：Saoud, Adnane; Girard, Antoine; Fribourg, Laurent
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS)
  摘要：This article presents a symbolic control approach to the design of distributed safety controllers for a class of continuous-time nonlinear systems. More precisely, we consider systems made of components where each component is equipped with a sampled-data controller with its own sampling period, resulting globally in a distributed multiperiodic sampled-data system. Moreover, controllers receive partial information on the state of the other components. We propose a component-based approach to c...