IEEE Transactions on Automatic Control

最新文章

• Value of Information in Feedback Control: Global Optimality AI翻译
  作者：Soleymani, Touraj; Baras, John S.; Hirche, Sandra; Johansson, Karl H.
  作者单位：Royal Institute of Technology; University System of Maryland; University of Maryland College Park; Technical University of Munich
  摘要：rate-regulation tradeoff, defined between two objective functions, one penalizing the packet rate and one the regulation cost, can express the fundamental performance bound of networked control systems. However, the characterization of the set of globally optimal solutions in this tradeoff for multidimensional Gauss-Markov processes has been an open problem. In this article, we characterize a policy profile that belongs to this set without imposing any restrictions on the information structure...
• Prescribed-Time Tracking Control of MIMO Nonlinear Systems With Nonvanishing Uncertainties AI翻译
  作者：Ye, Hefu; Song, Yongduan
  作者单位：Chongqing University
  摘要：It is highly desirable yet challenging to achieve zero-error tracking within a given short time period for uncertain multi-input-multi-output (MIMO) nonlinear systems. Previous results are primarily state-regulation oriented and are valid only for single-input single-output linear systems or nonlinear systems in normal-form. This note presents a tracking control solution for high-order MIMO nonlinear systems in strict-feedback-like form. The proposed control is able to achieve precise tracking...
• Predictive Control Barrier Functions: Enhanced Safety Mechanisms for Learning-Based Control AI翻译
  作者：Wabersich, Kim P.; Zeilinger, Melanie N.
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications. Recent developments address this issue through so-called predictive safety filters, which assess if a proposed learning-based control input can lead to constraint violations and modifies it if necessary to ensure safety for all future time steps. The theoretical guarantees of such predictive safety filters rely on the model assump...
• H2 ??????? and H8, Suboptimal Distributed Filters Design for Linear Systems AI翻译
  作者：Jiao, Junjie; Trentelman, Harry L.; Camlibel, M. Kanat
  作者单位：Technical University of Munich; University of Groningen
  摘要：This article investigates the H-2 and H-8. suboptimal distributed filtering problems for continuous-time linear systems. We consider a linear system monitored by a number of filters, where each of the filters receive only part of the measured output of the system. Each filter can communicate with the other filters according to a priori given strongly connected weighted directed graph. The aim is to design filter gains that guarantee the H-2 or H-8. norm of the transfer matrix from the disturba...
• On the Exact Feasibility of Convex Scenario Programs With Discarded Constraints AI翻译
  作者：Romao, Licio; Papachristodoulou, Antonis; Margellos, Kostas
  作者单位：University of Oxford
  摘要：We revisit the so-called sampling and discarding approach used to quantify the probability of constraint violation of a solution to convex scenario programs when some of the original samples are allowed to be discarded. Motivated by two scenario programs that possess analytic solutions and the fact that the existing bound for scenario programs with discarded constraints is not tight, we analyze a removal scheme that consists of a cascade of optimization problems, where, at each step, we remove...
• Stability Analysis of Probabilistic Boolean Networks With Switching Discrete Probability Distribution AI翻译
  作者：Yang, Xinrong; Li, Haitao
  作者单位：Shandong Normal University
  摘要：This article analyzes the stability of probabilistic Boolean networks (PBNs) with switching discrete probability distribution (DPD). First, the dynamics of PBNs with switching DPD is converted to an algebraic form by using the semitensor product of matrices. Second, based on the algebraic form, two essential concepts, that is, expectation of number of trajectories (ENT) and state expectation matrix, are proposed. Third, some criteria are presented for the finite-time stability and asymptotical...
• Comments on New Results on the Characterization of Strictly Positive Real Matrix Transfer Functions AI翻译
  作者：Hakimi-Moghaddam, Mojtaba; Ferrante, Augusto
  作者单位：Quchan University of Technology; University of Padua
  摘要：In this note, we address an inconsistency in our recent article (Hakimi-Moghaddam and Ferrante, 2021). In particular, we provide an example showing that Lemma III.1 of that article is not correct. We show how the statement of this lemma can be modified in order to resolve this inconsistency. We also describe how this modification impact the other results of the article. Notwithstanding the fact that Lemma III.1 plays a central role in the proof of the main result of (Hakimi-Moghaddam and Ferra...
• Adversarial Resilience for Sampled-Data Systems Under High-Relative-Degree Safety Constraints AI翻译
  作者：Usevitch, James; Panagou, Dimitra
  作者单位：University of Michigan System; University of Michigan
  摘要：Control barrier functions (CBFs) have recently become a powerful method for rendering desired safe sets forward invariant in single-agent and multiagent systems. In the multiagent case, prior literature has considered scenarios where all agents cooperate to ensure that the corresponding set remains invariant. However, these works do not consider scenarios where a subset of the agents are behaving adversarially with the intent to violate safety bounds. In addition, prior results on multiagent C...
• On the Absence of Spurious Local Trajectories in Time-Varying Nonconvex Optimization AI翻译
  作者：Fattahi, Salar; Josz, Cedric; Ding, Yuhao; Mohammadi, Reza; Lavaei, Javad; Sojoudi, Somayeh
  作者单位：University of Michigan System; University of Michigan; Columbia University; University of California System; University of California Berkeley
  摘要：In this article, we study the landscape of an online nonconvex optimization problem, for which the input data vary over time and the solution is a trajectory rather than a single point. To understand the complexity of finding a global solution of this problem, we introduce the notion of spurious (i.e., nonglobal) local trajectory as a generalization to the notion of spurious local solution in nonconvex (time-invariant) optimization. We develop an ordinary differential equation (ODE) associated...
• Reachability Analysis in Stochastic Directed Graphs by Reinforcement Learning AI翻译
  作者：Possieri, Corrado; Frasca, Mattia; Rizzo, Alessandro
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti (IASI-CNR); University of Catania; Polytechnic University of Turin; New York University; New York University Tandon School of Engineering
  摘要：We characterize the reachability probabilities in stochastic directed graphs by means of reinforcement learning methods. In particular, we show that the dynamics of the transition probabilities in a stochastic digraph can be modeled via a difference inclusion, which, in turn, can be interpreted as a Markov decision process. Using the latter framework, we offer a methodology to design reward functions to provide upper and lower bounds on the reachability probabilities of a set of nodes for stoc...