IEEE Transactions on Automatic Control

最新文章

• Optimal Model Reduction by Time-Domain Moment Matching for Lur'e-Type Models AI翻译
  作者：Shakib, Fahim; Scarciotti, Giordano; Jungers, Marc; Pogromsky, Alexander Yu; Pavlov, Alexey; van de Wouw, Nathan
  作者单位：Imperial College London; Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Eindhoven University of Technology; Norwegian University of Science & Technology (NTNU)
  摘要：This article considers the problem of model reduction for Lur'e-type models consisting of a feedback interconnection between linear dynamics and static nonlinearities. We propose an optimal variant of the time-domain moment-matching method in which the H-infinity -norm of the error transfer-function matrix of the linear part of the model is minimized while the static nonlinearities are inherited from the full-order model. We show that this approach also minimizes an error bound on the L-2 -nor...
• Analysis of Equilibrium Points and Convergent Behaviors for Constrained Signed Networks AI翻译
  作者：Song, Qiang; Meng, Deyuan; Wen, Guanghui; Cao, Jinde; Liu, Fang
  作者单位：Huanghuai University; Huanghuai University; Beihang University; Beihang University; Southeast University - China; Southeast University - China; Purple Mountain Laboratories; Ahlia University Bahrain
  摘要：This article is devoted to analyzing the equilibrium points and convergent behaviors for a constrained signed network with general topology containing a directed spanning tree, where the output of each agent is restricted by a constraint set. Different from unconstrained signed networks, the rooted subgraph and constraint sets are both critical for the theoretical analysis of the constrained signed network. By utilizing $H$-matrix theories, projection techniques, invariance principle, and an e...
• Adaptive Linear Quadratic Control for Stochastic Discrete-Time Linear Systems With Unmeasurable Multiplicative and Additive Noises AI翻译
  作者：Jiang, Yi; Liu, Lu; Feng, Gang
  作者单位：City University of Hong Kong
  摘要：This note investigates the adaptive linear quadratic control problem (ALQCP) for stochastic discrete-time (DT) linear systems with unmeasurable multiplicative and additive noises. A data-driven value iteration algorithm is developed to solve the stochastic algebraic Riccati equation (SARE) that results from the concerned problem and to simultaneously obtain the optimal feedback policy. The proposed algorithm directly uses online data to solve the ALQCP based on an unbiased estimator and an ini...
• Social Power Games in Concatenated Opinion Dynamics AI翻译
  作者：Wang, Lingfei; Chen, Guanpu; Bernardo, Carmela; Hong, Yiguang; Shi, Guodong; Altafini, Claudio
  作者单位：Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Royal Institute of Technology; Linkoping University; Tongji University; University of Sydney
  摘要：In this article, we propose and solve a social power game, i.e., a strategic game formulated on an opinion dynamics model and in which the agents aim to maximize their social power. As model we consider the concatenated Friedkin-Johnsen (FJ) model, which describes opinion evolution over a sequence of discussion events, while as actions we take the stubbornness coefficients, which can be freely chosen by the agents in order to maximize their social power, here corresponding to the utility funct...
• Prescribed-Time Extremum Seeking for Delays and PDEs Using Chirpy Probing AI翻译
  作者：Yilmaz, Cemal Tugrul; Krstic, Miroslav
  作者单位：University of California System; University of California San Diego
  摘要：Extremum seeking, an online model-free optimization algorithm with traditionally exponential convergence, was recently advanced by Poveda and coauthors to fixed-time convergence, using nonsmooth time-invariant feedback. In this article, we introduce an alternative time-varying prescribed-time extremum seeking (PT-ES) approach to reaching the optimum in a user-assignable prescribed time (PT), independent of the initial condition of the estimator. Instead of conventional sinusoidal probing signa...
• Relative Q-Learning for Average-Reward Markov Decision Processes With Continuous States AI翻译
  作者：Yang, Xiangyu; Hu, Jiaqiao; Hu, Jian-Qiang
  作者单位：Shandong University; State University of New York (SUNY) System; Stony Brook University; Fudan University
  摘要：Markov decision processes (MDPs) are widely used for modeling sequential decision-making problems under uncertainty. We propose an online algorithm for solving a class of average-reward MDPs with continuous state spaces in a model-free setting. The algorithm combines the classical relative Q-learning with an asynchronous averaging procedure, which permits the Q-value estimate at a state-action pair to be updated based on observations at other neighboring pairs sampled in subsequent iterations....
• Linear Quadratic Path-Following via Online Trajectory Speed Optimization AI翻译
  作者：Antunes, Duarte J.
  作者单位：Eindhoven University of Technology
  摘要：Consider a linear system subject to stochastic disturbances and a path to be followed by a system's output. The path-following problem is posed here as choosing both the control input and the speed along the path to minimize the expected value of a quadratic function of the control input and of the error between the output and the resulting trajectory. The optimal control input policy for the deterministic version (no stochastic disturbances) is first provided and shown to be the sum of linear...
• Input-Constrained Funnel Control of Nonlinear Systems AI翻译
  作者：Berger, Thomas
  作者单位：University of Paderborn
  摘要：We study tracking control for uncertain nonlinear multi-input, multi-output systems modeled by rth order functional differential equations (encompassing systems with arbitrary strict relative degree) in the presence of input constraints. The objective is to guarantee the evolution of the tracking error within a performance funnel with prescribed asymptotic shape (thus achieving desired transient and asymptotic accuracy objectives), for any sufficiently smooth reference signal. We design a nove...
• Distributed Constrained Optimization Over Unbalanced Time-Varying Digraphs: A Randomized Constraint Solving Algorithm AI翻译
  作者：Luan, Meng; Wen, Guanghui; Lv, Yuezu; Zhou, Jialing; Chen, C. L. Philip
  作者单位：Southeast University - China; Beijing Institute of Technology; South China University of Technology
  摘要：Despite the recent development of distributed constrained optimization algorithms in the literature, it is still a challenging issue to construct distributed algorithms to efficiently solve the constrained optimization problem with convergence rate guarantees, especially for the case with general constraints and unbalanced time-varying digraphs. This article aims to investigate the distributed discrete-time optimization problem over time-varying unbalanced digraphs with general constraints inc...
• Strategically Revealing Intentions in General Lotto Games AI翻译
  作者：Paarporn, Keith; Chandan, Rahul; Kovenock, Dan; Alizadeh, Mahnoosh; Marden, Jason R.
  作者单位：University of Colorado System; University of Colorado at Colorado Springs; University of California System; University of California Santa Barbara; Chapman University System; Chapman University
  摘要：Strategic decision-making in uncertain and adversarial environments is crucial for the security of modern systems and infrastructures. A salient feature of many optimal decision-making policies is a level of unpredictability, or randomness, which helps to keep an adversary uncertain about the system's defensive strategies. This article seeks to explore decision-making policies on the other end of the spectrum-namely, whether there are benefits in revealing one's strategic intentions to an oppo...