IEEE Transactions on Automatic Control

最新文章

• Identifiability in the Behavioral Setting AI翻译
  作者：Markovsky, Ivan; Dorfler, Florian
  作者单位：ICREA; Universitat Politecnica de Catalunya; Centre Internacional de Metodes Numerics en Enginyeria (CIMNE); Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Identifiability, i.e., uniqueness of a solution of the identification problem, is a fundamental issue in system identification and data-driven control. Necessary and sufficient identifiability conditions for deterministic linear time-invariant systems that do not require a priori given input/output partitioning of the variables nor controllability of the true system are derived in the article. The prior knowledge needed for identifiability is the number of inputs, lag, and order of the true sy...
• Reinforcement Learning of Structured Stabilizing Control for Linear Systems With Unknown State Matrix AI翻译
  作者：Mukherjee, Sayak; Vu, Thanh Long
  作者单位：United States Department of Energy (DOE); Pacific Northwest National Laboratory
  摘要：This article delves into designing stabilizing feedback control gains for continuous-time linear systems with unknown state matrix, in which the control gain is subjected to a structural constraint. We bring forth the ideas from reinforcement learning (RL) in conjunction with sufficient stability and performance guarantees in order to design these structured gains using the trajectory measurements of states and controls. We first formulate a model-based linear quadratic regulator (LQR) framewo...
• Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control AI翻译
  作者：Chen, Bingquan; Cao, Jinde; Lu, Guoping; Rutkowski, Leszek
  作者单位：Southeast University - China; Southeast University - China; Southeast University - China; Purple Mountain Laboratories; Yonsei University; Nantong University; Polish Academy of Sciences; Systems Research Institute of the Polish Academy of Sciences; AGH University of Krakow
  摘要：In this article, we investigate the stabilization of Markovian jump Boolean control networks by a kind of event-triggered control, which is essentially an intermittent control scheme. First, a novel condition for the stability of Markovian jump Boolean networks is obtained based on the recurrence of finite-state homogeneous Markov chains. After that, the necessary and sufficient conditions for the stabilization of a Markovian jump Boolean control network by an event-triggered control are propo...
• Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations AI翻译
  作者：Dorfler, Florian; Coulson, Jeremy; Markovsky, Ivan
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; ICREA; Universitat Politecnica de Catalunya; Centre Internacional de Metodes Numerics en Enginyeria (CIMNE)
  摘要：In this article, we discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary direct data-driven control approach seeking an optimal decision compatible with recorded data assembled in a Hankel matrix and robustified through suitable regularizations. We formulate these two problems in the language of behavioral systems theory and parametric mathematical programs, and we bridge...
• Stealthy Hacking and Secrecy of Controlled State Estimation Systems With Random Dropouts AI翻译
  作者：Lu, Jingyi; Quevedo, Daniel E. E.; Gupta, Vijay; Dey, Subhrakanti
  作者单位：East China University of Science & Technology; Queensland University of Technology (QUT); University of Notre Dame; Maynooth University
  摘要：We study the maximum information gain that an adversary may obtain through hacking without being detected. Consider a dynamical process observed by a sensor that transmits a local estimate of the system state to a remote estimator according to some reference transmission policy across a packet-dropping wireless channel equipped with acknowledgments (ACK). An adversary overhears the transmissions and proactively hijacks the sensor to reprogram its transmission policy. We define perfect secrecy ...
• Hierarchical Supervisory Control Under Partial Observation: Normality AI翻译
  作者：Komenda, Jan; Masopust, Tomas
  作者单位：Czech Academy of Sciences; Institute of Mathematics of the Czech Academy of Sciences; Palacky University Olomouc
  摘要：Conditions preserving observability of specifications between the plant and its abstraction are essential for hierarchical supervisory control of discrete-event systems under partial observation. Observation consistency and local observation consistency were identified as such conditions. To preserve normality, only observation consistency is required. Although observation consistency preserves normality between the levels for normal specifications, for specifications that are not normal, obse...
• Hybrid Feedback for Autonomous Navigation in Planar Environments With Convex Obstacles AI翻译
  作者：Sawant, Mayur; Berkane, Soulaimane; Polushin, Ilia; Tayebi, Abdelhamid
  作者单位：Western University (University of Western Ontario); University of Quebec; University Quebec Outaouais; Lakehead University
  摘要：We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments cluttered with obstacles having arbitrary convex shapes. The proposed navigation approach relies on a hybrid feedback to guarantee global asymptotic stabilization of the robot toward a predefined target location while ensuring the forward invariance of the obstacle-free workspace. The main idea consists in designing an appropriate switching strategy between the move-to-target mode and the obstac...
• Contraction-Based Stochastic Model Predictive Control for Nonlinear Systems With Input Delay Using Multidimensional Taylor Network AI翻译
  作者：Wang, Guo-Biao; Yan, Hong-Sen; Zheng, Xiao-Yi
  作者单位：Southeast University - China
  摘要：For nonlinear systems with stochastic uncertainties and input delay, the existing control approaches based on the robust mechanism are generally conservative in most practical scenarios. Within this context, a stochastic model predictive control (MPC) scheme based on uncertainty contraction is proposed here to reduce conservativeness and improve real-time performance. In terms of the Lyapunov-Razumikhin theorem, a robust variational controller (RVC) is deduced as the primary controller for the...
• Hierarchical Motion Planning Under Probabilistic Temporal Tasks and Safe-Return Constraints AI翻译
  作者：Guo, Meng; Liao, Tianjun; Wang, Junjie; Li, Zhongkui
  作者单位：Peking University; Peking University
  摘要：Safety is crucial for robotic missions within an uncertain environment. Common safety requirements such as collision avoidance are only state-dependent, which can be restrictive for complex missions. In this article, we address a more general formulation as safe-return constraints, which require the existence of a return policy to drive the system back to a set of safe states with high probability. The robot motion is modeled as a Markov decision process with probabilistic labels, which can be...
• The Binary Distributed Observer Approach to the Cooperative Output Regulation of Linear Multiagent Systems AI翻译
  作者：Liang, Dong; Tian, Engang
  作者单位：University of Shanghai for Science & Technology
  摘要：In this note, the cooperative output regulation problem of general heterogeneous linear multiagent systems with binary constraints is studied. To speed up the convergence rate, the binary distributed observer is proposed only utilizing sign of the relative state instead of using full relative information. Under some mild conditions, the binary distributed observer can estimate the state of the leader system exponentially in finite time sense. The leader system can generate various reference si...