IEEE Transactions on Automatic Control

最新文章

• A Class of Convex Optimization-Based Recursive Algorithms for Identification of Stochastic Systems AI翻译
  作者：Ding, Mingxia; Zhao, Wenxiao; Chen, Tianshi; Zhang, Weidong
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Hainan University; Shanghai Jiao Tong University
  摘要：Focusing on identification, this article develops a class of convex optimization-based criteria and correspondingly the recursive algorithms to estimate the parameter vector theta* of a stochastic dynamic system. Not only do the criteria include the classical least-squares estimator but also the L-l = |center dot |(l), l >= 1, the Huber, the Log-cosh, and the Quantile costs as special cases. First, we prove that the minimizers of the convex optimization-based criteria converge to theta* with p...
• D4Q: Data-Driven Design of Dynamic QuantizerProposal of Method and Experimental Validation AI翻译
  作者：Fujimoto, Yusuke; Minami, Yuki
  作者单位：University of Osaka; University of Hyogo
  摘要：This article discusses a data-driven design method for a dynamic quantizer. In particular, we consider the parameter tuning of a noise-shaping filter under the assumption that the input-output data are available but the target plant itself is unknown. We first focus on the input-output relationship of the noise-shaping filter in the optimal dynamic quantizer (ODQ). Based on this relationship, a data-driven parameter tuning method that makes the noise-shaping filter similar to the optimal one i...
• Moment Matching by Kernel-Based Learning AI翻译
  作者：Moreschini, Alessio; Scandella, Matteo; Astolfi, Alessandro; Parisini, Thomas
  作者单位：Imperial College London; University of Bergamo; University of Rome Tor Vergata; Aalborg University; University of Trieste
  摘要：In this article, we introduce a kernel-based moment matching theory that relies upon a novel data-driven model reduction method, employing the estimation of moments within a reproducing kernel Hilbert space. We demonstrate that moment estimation can be enhanced by appropriately tuning the regularization term, regardless of the kernel choice. In addition, we present conditions to ensure that the reproducing kernel Hilbert space contains only functions, which are bona fide moments. While exact m...
• Differentially Private Distributed Model Predictive Control of Linear Discrete-Time Systems With Global Constraints AI翻译
  作者：Zhang, Kaixiang; Wang, Yongqiang; Song, Ziyou; Li, Zhaojian
  作者单位：Michigan State University; Clemson University; University of Michigan System; University of Michigan
  摘要：Distributed model predictive control (DMPC) has attracted extensive attention as it can explicitly handle system constraints and achieve optimal control in a decentralized manner. However, the deployment of DMPC strategies generally requires the sharing of sensitive data among subsystems, which may violate the privacy of participating systems. In this article, we propose a differentially private DMPC algorithm for linear discrete-time systems subject to coupled global constraints. Specifically...
• H∞ Control Based on the Discounted Hamilton-Jacobi-Isaacs Equations AI翻译
  作者：Chen, Guoyuan; Wang, Yi; Zhou, Qinglong
  作者单位：Zhejiang University of Finance & Economics; Zhejiang University
  摘要：The optimal H-infinity control problem over an infinite time horizon, which incorporates a performance functional with a discount factor e(-alpha t) (alpha > 0), is important in various fields. Solving this problem is equivalent to addressing a discounted Hamilton-Jacobi-Isaacs (HJI) partial differential equation. In this article, we first establish a precise estimate for the discount factor alpha that guarantees the existence of a nonnegative stabilizing solution to the discounted HJI equatio...
• Global H∞ Output Regulation of Nonlinear Systems via Output-Feedback Reinforcement Learning AI翻译
  作者：Li, Shaobao; Wang, Yuxiang; Zhang, Yuguang; Luo, Xiaoyuan; Wang, Juan; Xinping, Guan
  作者单位：Yanshan University; Shanghai Jiao Tong University
  摘要：This article addresses the H-infinity output regulation problem of affine nonlinear systems with unmeasurable states and completely unknown dynamics. The H-infinity output regulation problem is formulated into a two-player zero-sum differential game problem with the control input and disturbance being the two adversary players. A model-based global state-feedback optimal solution to the H-infinity output regulation problem is presented by solving a Hamilton-Jacobi-Isaacs (HJI) equation, and a ...
• Enforcement of Critical Observability in Modular Discrete-Event Systems AI翻译
  作者：Miao, Shaowen; Komenda, Jan; Masopust, Tomas; Lai, Aiwen
  作者单位：Xiamen University; Czech Academy of Sciences; Institute of Mathematics of the Czech Academy of Sciences; Palacky University Olomouc
  摘要：In safety-critical applications, the ability to distinguish between critical and noncritical states based on observations, known as critical observability (CO), is essential for ensuring reliability and security. We address the enforcement of CO in discrete-event systems (DES) through supervisory control. While a supremal critically observable sublanguage does not exist, we overcome this challenge by leveraging the concept of normality, proposing an algorithm to compute the least restrictive c...
• Distributed Norm Optimal Iterative Learning Control for High-Performance Consensus Tracking AI翻译
  作者：Chen, Bin; Chu, Bing
  作者单位：University of Sheffield; University of Southampton
  摘要：High-performance consensus tracking problem, which requires all the subsystems operating repetitively to track a desired reference, has found a number of important applications in the last decade. To achieve the high-performance requirement, recent designs use iterative learning control (ILC) to avoid the use of an accurate model that is usually required in conventional control methods. However, most of the existing distributed ILC algorithms have poor scalability (i.e., they will have difficu...
• Evolutionary Games on Infinite Strategy Sets: Convergence to Nash Equilibria via Dissipativity AI翻译
  作者：Anderson, Brendon G.; Li, Jingqi; Sojoudi, Somayeh; Arcak, Murat
  作者单位：California State University System; California Polytechnic State University San Luis Obispo; University of California System; University of California Berkeley
  摘要：In this article, we consider evolutionary dynamics for population games in which players have a continuum of strategies at their disposal. Models in this setting amount to infinite-dimensional differential equations evolving on the manifold of probability measures. We generalize dissipativity theory for evolutionary games from finite to infinite strategy sets that are compact metric spaces, and derive sufficient conditions for the stability of Nash equilibria under the infinite-dimensional dyn...
• Adaptive Output Feedback Control Using Lyapunov-Based Deep Recurrent Neural Networks (Lb-DRNNs) AI翻译
  作者：Griffis, Emily J.; Patil, Omkar Sudhir; Makumi, Wanjiku A.; Dixon, Warren E.
  作者单位：State University System of Florida; University of Florida
  摘要：Unlike traditional feedforward neural networks, recurrent neural networks (RNNs) possess a recurrent connection that allows them to retain past information. This internal memory enables RNNs to effectively model and capture time-varying and accumulative effects observed in dynamic systems, which cannot be achieved by static feedforward neural networks, making RNNs more suitable for tasks, such as state estimation and output feedback (OFB) control. Motivated by the dynamic behavior of RNNs, thi...