IEEE Transactions on Automatic Control

最新文章

• LQG Differential Stackelberg Game Under Nested Observation Information Patterns AI翻译
  作者：Li, Zhipeng; Marelli, Damian; Fu, Minyue; Zhang, Huanshui
  作者单位：University of Newcastle; Shandong University
  摘要：We investigate the linear quadratic Gaussian-Stackelberg game under a class of nested observation information patterns. The follower uses its observation data to design its strategy, whereas the leader implements its strategy using global observation data. We show that the solution requires solving a new type of forward-backward stochastic differential equation, whose drift components contain two conditional expectation terms associated with the adjoint variables. We then propose a method to f...
• Certifying the LTL Formula p Until q in Hybrid Systems AI翻译
  作者：Han, Hyejin; Maghenem, Mohamed; Sanfelice, Ricardo G.
  作者单位：University of California System; University of California Santa Cruz; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS)
  摘要：In this article, we propose sufficient conditions to guarantee that a linear temporal logic formula of the form p Until q, denoted by p Uq, is satisfied for a hybrid system. Roughly speaking, the formula p Uq is satisfied means that the solutions, initially satisfying proposition p, keep satisfying this proposition until proposition q is satisfied. To certify such a formula, connections to invariance notions-specifically, conditional invariance and eventual conditional invariance-as well as fi...
• Exploiting Partial Observability for Optimal Deception AI翻译
  作者：Karabag, Mustafa O.; Ornik, Melkior; Topcu, Ufuk
  作者单位：University of Texas System; University of Texas Austin; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：Deception is a useful tool in situations where an agent operates in the presence of its adversaries. We consider a setting where a supervisor provides a reference policy to an agent, expects the agent to operate in an environment by following the reference policy, and partially observes the agent's behavior. The agent instead follows a different deceptive policy to achieve a different task. We model the environment with a Markov decision process and study the synthesis of optimal deceptive pol...
• On Equivalence of Data Informativity for Identification and Data-Driven Control of Partially Observable Systems AI翻译
  作者：Sadamoto, Tomonori
  作者单位：University of Electro-Communications - Japan
  摘要：This study shows that the informativity for the identification of partially observable systems is equivalent to that for designing dynamical measurement-feedback stabilizers. This finding is entirely different from the input-state case, where the direct data-driven design of state-feedback stabilizers requires less informativity than system identification. We derive the equivalence between the two types of informativity based on a newly introduced vector autoregressive with exogenous input (VA...
• Recursive Network Estimation for a Model With Binary-Valued States AI翻译
  作者：Xing, Yu; He, Xingkang; Fang, Haitao; Johansson, Karl Henrik
  作者单位：Royal Institute of Technology; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
  摘要：This article studies how to estimate the weighted adjacency matrix of a network out of the state sequence of a model with binary-valued states, by using a recursive algorithm. In the considered system, agents display and exchange these binary-valued states generated from intrinsic quantizers. It is shown that stability of the model and identifiability of the system parameters can be guaranteed under continuous random noise. Under standard Gaussian noise, the problem of estimating the real-valu...
• On Optimizing the Conditional Value-at-Risk of a Maximum Cost for Risk-Averse Safety Analysis AI翻译
  作者：Chapman, Margaret P. P.; Fauss, Michael; Smith, Kevin M. M.
  作者单位：University of Toronto; Princeton University; Tufts University
  摘要：The popularity of Conditional Value-at-Risk (CVaR), a risk functional from finance, has been growing in the control systems community due to its intuitive interpretation and axiomatic foundation. We consider a nonstandard optimal control problem in which the goal is to minimize the CVaR of a maximum random cost subject to a Borel-space Markov decision process. The objective represents the maximum departure from a desired operating region averaged over a given fraction of the worst cases. This ...
• Nonminimality of the Realizations and Possessing State Matrices With Integer Elements in Linear Discrete-Time Controllers AI翻译
  作者：Tavazoei, Mohammad Saleh
  作者单位：Sharif University of Technology
  摘要：It is known that discrete-time controllers, whose state matrices have no noninteger element, are beneficial in homomorphic-based encrypted control systems. Nevertheless, it has been recently shown that possessing state matrices with integer elements usually yields unstable discrete-time controllers. In this article, we investigate the problem from a nonminimality perspective. It is shown that nonminimal realizations, in comparison to minimal ones, can theoretically provide a wider framework to...
• Robust Nonlinear MPC With Variable Prediction Horizon: An Adaptive Event-Triggered Approach AI翻译
  作者：Wang, Peng-Biao; Ren, Xue-Mei; Zheng, Dong-Dong
  作者单位：Beijing Institute of Technology
  摘要：This article investigates the event-triggered model predictive control (ETMPC) problem for nonlinear systems with the bounded disturbance. First, a novel adaptive event-triggered mechanism without Zeno behaviors, in which the triggering threshold can constantly be adjusted with the change of the system state, is proposed for computational load reduction. Then, an adaptive prediction horizon update strategy is proposed to further reduce the computational complexity of the optimization problem a...
• Formal Verification of Unknown Discrete- and Continuous-Time Systems: A Data-Driven Approach AI翻译
  作者：Nejati, Ameneh; Lavaei, Abolfazl; Jagtap, Pushpak; Soudjani, Sadegh; Zamani, Majid
  作者单位：Technical University of Munich; University of Munich; Newcastle University - UK; Bosch; Indian Institute of Science (IISC) - Bangalore; University of Colorado System; University of Colorado Boulder
  摘要：This article is concerned with a formal verification scheme for both discrete- and continuous-time deterministic systems with unknown mathematical models. The main target is to verify the safety of unknown systems based on the construction of barrier certificates via a set of data collected from trajectories of systems while providing an a-priori guaranteed confidence on the safety. In our proposed framework, we first cast the original safety problem as a robust convex program (RCP). Solving t...
• Model-Based Policy Iterations for Nonlinear Systems via Controlled Hamiltonian Dynamics AI翻译
  作者：Sassano, Mario; Mylvaganam, Thulasi; Astolfi, Alessandro
  作者单位：University of Rome Tor Vergata; Imperial College London; Imperial College London
  摘要：The infinite-horizon optimal control problem for nonlinear systems is studied. In the context of model-based, iterative learning strategies we propose an alternative definition and construction of the temporal difference error arising in policy iteration strategies. In such architectures, the error is computed via the evolution of the Hamiltonian function (or, possibly, of its integral) along the trajectories of the closed-loop system. Herein the temporal difference error is instead obtained v...