IEEE Transactions on Automatic Control

最新文章

• Distributed Estimation and Control for LTI Systems Under Finite-Time Agreement AI翻译
  作者：Fioravanti, Camilla; Makridis, Evagoras; Oliva, Gabriele; Vrakopoulou, Maria; Charalambous, Themistoklis
  作者单位：University Campus Bio-Medico - Rome Italy; University of Cyprus
  摘要：This article considers a strongly connected network of agents, each capable of partially observing and controlling a discrete-time linear time-invariant (LTI) system that is jointly observable and controllable. Additionally, agents collaborate to achieve a shared estimated state, computed as the average of their local state estimates. Recent studies suggest that increasing the number of average consensus steps between state estimation updates allows agents to choose from a wider range of state...
• Optimization-Free Control of Safety-Critical Systems Subject to the Intersection of Multiple Time-Varying Concave Constraints AI翻译
  作者：Hosseinzadeh, Mehdi
  作者单位：Washington State University
  摘要：The explicit reference governor (ERG) is an add on unit that provides the constraint handling capability to a prestabilized system by providing the system with an applied reference which is the best approximation of the desired reference at any time and converges to the desired reference. One of the main strengths of ERG is that it does not make use of any online optimization, which makes it an appropriate solution for real-time applications; in particular, it has been shown that ERG has poten...
• Distributed MPC for Self-Organized Cooperation of Multiagent Systems AI翻译
  作者：Koehler, Matthias; Mueller, Matthias A.; Allgoewer, Frank
  作者单位：University of Stuttgart; Leibniz University Hannover
  摘要：In this article, we present a sequential distributed model predictive control (MPC) scheme for cooperative control of multiagent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. T...
• RL-ARNE: A Reinforcement Learning Algorithm for Computing Average Reward Nash Equilibrium of Nonzero-Sum Stochastic Games AI翻译
  作者：Sahabandu, Dinuka; Moothedath, Shana; Allen, Joey; Bushnell, Linda; Lee, Wenke; Poovendran, Radha
  作者单位：University of Washington; University of Washington Seattle; Iowa State University; University System of Georgia; Georgia Institute of Technology
  摘要：Stochastic games model the strategic interactions between two or more players that occur in a sequence of stages. In this article, we focus on computing the average reward Nash equilibrium (ARNE) of a nonzero-sum stochastic game when the transition probabilities of the game and reward structure of the players are unknown. We note that the current state-of-the-art reinforcement learning (RL) algorithms that compute the ARNE of nonzero-sum stochastic games require solving a matrix game correspon...
• Acoustic Boundary Output Feedback Stabilization of Dynamic n-τ Model Flame via Duct With Variable Cross Section AI翻译
  作者：Wilhelmsen, Nils Christian A.; Di Meglio, Florent
  作者单位：Universite PSL; MINES ParisTech
  摘要：We propose an output feedback controller to stabilize thermoacoustic instabilities in a duct with variable cross section, by actuating and sensing the acoustic boundary opposite from the flame. A linear parametric state-space model, taking into account both time-delay effects from the classical n - tau model and low-pass filtering effects, models the flame subsystem. For the acoustics, a distributed model taking into account variations in the cross-sectional area along the duct is developed an...
• Probabilistic Constraint for Safety-Critical Reinforcement Learning AI翻译
  作者：Chen, Weiqin; Subramanian, Dharmashankar; Paternain, Santiago
  作者单位：International Business Machines (IBM); IBM USA
  摘要：In this article, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high probability, maintains the trajectory of the agent in a given safe set. We establish a connection between this probabilistic-constrained setting and the cumulative-constrained formulation that is frequently explored in the existing literature. We provide theoretical bounds elucidating that the probabilisti...
• Privacy-Preserving Average Consensus Through Network Augmentation AI翻译
  作者：Ramos, Guilherme; Aguiar, Antonio Pedro; Kar, Soummya; Pequito, Sergio
  作者单位：Universidade de Lisboa; Instituto de Telecomunicacoes; Universidade do Porto; Carnegie Mellon University; Uppsala University
  摘要：Average consensus protocols play a central role in distributed systems and decision-making, such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. In its basic form, the goal of average consensus protocols is to compute an aggregate such as the average of network data; however, existing protocols could lead to leakage of individu...
• Noninterference Analysis of Bounded Petri Nets Using Basis Reachability Graph AI翻译
  作者：Ran, Ning; Nie, Jingyao; Meng, Aiwen; Seatzu, Carla
  作者单位：Hebei University; University of Cagliari
  摘要：In this article, we deal with two problems related to security and privacy of bounded Petri nets, namely, noninterference analysis and enforcement. A system could be monitored by different types of users, high-level and low-level users, who have access to different information even if both know the structure of the system. Low-level users can observe only the occurrence of a subset of events. On the contrary, high-level users can observe the occurrence of all the events affecting the system dy...
• Robust Variable Projection Algorithm for the Identification of Separable Nonlinear Models AI翻译
  作者：Chen, Guang-Yong; Su, Xiang-Xiang; Gan, Min; Guo, Wenzhong; Chen, C. L. Philip
  作者单位：Fuzhou University; South China University of Technology
  摘要：Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and...
• Solving Feynman-Kac Forward-Backward SDEs Using McKean-Markov Branched Sampling AI翻译
  作者：Hawkins, Kelsey P.; Pakniyat, Ali; Theodorou, Evangelos; Tsiotras, Panagiotis
  作者单位：University System of Georgia; Georgia Institute of Technology; University of Alabama System; University of Alabama Tuscaloosa; University System of Georgia; Georgia Institute of Technology
  摘要：We propose a new method for the numerical solution of the forward-backward stochastic differential equations (FBSDE) appearing in the Feynman-Kac representation of the value function in stochastic optimal control problems. Using Girsanov's change of probability measures, it is demonstrated how a McKean-Markov branched sampling method can be utilized for the forward integration pass, as long as the controlled drift term is appropriately compensated in the backward integration pass. Subsequently...