IEEE Transactions on Automatic Control

最新文章

• Tradeoff Between Time and Energy Costs for Controlling Stochastic Coupled Neural Networks AI翻译
  作者：Zhao, Lingzhi; Dai, Haifeng; Yang, Chunyu; Lu, Jianquan; Sun, Yongzheng
  作者单位：China University of Mining & Technology; Southeast University - China; China University of Mining & Technology; Southeast University - China
  摘要：Time cost (TC) and energy cost (EC) are two fundamental indicators for evaluating the designed protocols for controlling networked systems. Yet the relationships of which as well as their dependence on the network topology are far from clear. In this note, we explore this problem with the stochastic synchronization of coupled neural networks. A novel controller is articulated, which switches between the linear feedback control and finite-time feedback control relying on the size of system erro...
• Quantized Primal-Dual Algorithms for Network Optimization With Linear Convergence AI翻译
  作者：Chen, Ziqin; Liang, Shu; Li, Li; Cheng, Shuming
  作者单位：Tongji University
  摘要：This note investigates a network optimization problem in which a group of agents cooperate to minimize a global function under the practical constraint of finite-bandwidth communication. We propose an adaptive encoding-decoding scheme to handle the quantization communication between agents. Based on this scheme, we develop a continuous-time quantized distributed primal-dual algorithm for the network optimization problem. Our algorithm achieves linear convergence to an exact optimal solution. F...
• Inverse Optimal Safety Filters AI翻译
  作者：Krstic, Miroslav
  作者单位：University of California System; University of California San Diego
  摘要：Control barrier function quadratic programs (QP) safety filters are pointwise minimizers of the control effort at a given state, i.e., myopically optimal at each time. But are they optimal over the entire infinite time horizon? What does it mean for a controlled system to be optimally safe as opposed to, conventionally optimally stable? When disturbances, deterministic and stochastic, have unknown upper bounds, how should safety be defined to allow a graceful degradation under disturbances? Ca...
• Distributed Control for Uncertain Multiagent Systems With the Powers of Positive-Odd Numbers: A Low-Complexity Design Approach AI翻译
  作者：Lv, Maolong; Wang, Ning
  作者单位：Air Force Engineering University
  摘要：This work shows that low-complexity prescribed performance control (PPC) can be used to realize leader-following consensus for uncertain second-order multiagent systems (MASs) with the powers of positive odd numbers, i.e., agents whose dynamics are a chain of integrators with positive odd powers. Low-complexity PPC is a control methodology whose strongest feature is its adaptation-free structural simplicity: uncertainty can be handled without estimation of unknown parameters nor approximation ...
• Adaptive Control of Noncanonical Nonlinear Systems With Time-Varying Dynamics AI翻译
  作者：Wen, Liyan; Jiang, Bin; Chen, Mou; Ma, Yajie
  摘要：In this article, adaptive control designs are systematically studied for uncertain noncanonical nonlinear systems with time-varying dynamics. As a key system characterization, the relative degrees are directly defined for noncanonical nonlinear time-varying systems, and at the same time, the necessary time-varying parameter conditions are specifically derived for different system dynamics. Then, robust adaptive feedback linearization-based control designs are studied for noncanonical nonlinear...
• Adaptive Boundary Observers for Hyperbolic PDEs With Application to Traffic Flow Estimation AI翻译
  作者：Wu, Jiahao; Zhan, Jingyuan; Zhang, Liguo
  作者单位：Beijing University of Technology
  摘要：This article studies the problem of adaptive boundary observer design for a class of linear hyperbolic partial differential equations (PDEs) subject to in-domain and boundary parameter uncertainties. Based on the swapping transformation technique, a Luenberger-type boundary observer with the least squares parameter estimation law is designed, which relies only on the measurements at boundaries of the system. By employing the Lyapunov function method, we prove that the exponential convergence o...
• BIG Hype: Best Intervention in Games via Distributed Hypergradient Descent AI翻译
  作者：Grontas, Panagiotis D.; Belgioioso, Giuseppe; Cenedese, Carlo; Fochesato, Marta; Lygeros, John; Dorfler, Florian
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Hierarchical decision making problems, such as bilevel programs and Stackelberg games, are attracting increasing interest in both the engineering and machine learning communities. Yet, existing solution methods lack either convergence guarantees or computational efficiency, due to the absence of smoothness and convexity. In this work, we bridge this gap by designing a first-order hypergradient-based algorithm for Stackelberg games and mathematically establishing its convergence using tools fro...
• A Decentralized Control Synthesis Approach for Networked Systems With Arbitrary Interconnections AI翻译
  作者：Welikala, Shirantha; Lin, Hai; Antsaklis, Panos J.
  作者单位：Stevens Institute of Technology; University of Notre Dame
  摘要：This article considers the problem of decentralized analysis and control synthesis to verify and enforce properties like stability and dissipativity of large-scale networked systems comprised of linear subsystems interconnected in an arbitrary topology. In particular, we design systematic networked system analysis and control synthesis processes that can be executed in a decentralized manner with minimal information sharing among the subsystems. We also show that, for such decentralized proces...
• A Family of Hyperparameter Estimators Linking EB and SURE for Kernel-Based Regularization Methods AI翻译
  作者：Zhang, Meng; Chen, Tianshi; Mu, Biqiang
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen
  摘要：Hyperparameter estimation is a critical aspect of kernel-based regularization methods (KRMs), alongside kernel design. Empirical Bayes (EB) and Stein's unbiased risk estimator (SURE) are two widely used hyperparameter estimators for tuning the unknown hyperparameters associated with the kernel matrix of KRMs. However, EB and SURE exhibit different characteristics in both theory and practice. Theoretically, SURE is asymptotically optimal in terms of minimizing the mean squared error (MSE), wher...
• Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent AI翻译
  作者：Jing, Gangshan; Bai, He; George, Jemin; Chakrabortty, Aranya; Sharma, Piyush K.
  作者单位：Chongqing University; Oklahoma State University System; Oklahoma State University - Stillwater; North Carolina State University
  摘要：Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estimation process, almost all of them require random samples with the same dimension as the global variable and/or require evaluation of the global cost function, which may induce high estimation variance for large-scale networks. In this paper, we propose a novel distributed zeroth-order algorithm by leveraging the netw...