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作者:Narayanan, Vignesh; Zhang, Wei; Li, Jr-Shin
作者单位:Washington University (WUSTL); University of South Carolina System; University of South Carolina Columbia
摘要:Controlling large-scale dynamic population systems, known as ensemble control, is a pervasive and essential task in many emerging applications from diverse scientific domains. Previous focuses in the area of ensemble control have been placed on seeking open-loop control strategies due to the unavailability of state feedback information for each individual system in the ensemble. In this article, we develop a foundational framework for analysis and control of ensemble systems with closed feedba...
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作者:Li, Dan; Fooladivanda, Dariush; Martinez, Sonia
作者单位:University of California System; University of California San Diego; University of California System; University of California Berkeley
摘要:This article proposes a novel approach to construct data-driven online solutions to optimization problems (P) subject to a class of distributionally uncertain dynamical systems. The introduced framework allows for the simultaneous learning of distributional system uncertainty via a parameterized, control-dependent ambiguity set using a finite historical dataset, and its use to make online decisions with probabilistic regret function bounds. Leveraging the merits of machine learning, the main t...
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作者:Shi, Wenrui; Hou, Mingzhe; Duan, Guangren
作者单位:Harbin Institute of Technology
摘要:The preassigned performance control (PPC) methods have attracted considerable attention in recent years; however, most of the mainstream PPC methods utilize barrier functions, and thus, may suffer from the singularity problem of the control law under some unexpected conditions, such as sensor faults. In this article, a new robust PPC method without using barrier functions is proposed for second-order vector nonlinear systems, which can completely avoid the singularity problem of the control la...
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作者:Liu, Yuhang; Zhao, Wenxiao; Yin, George
作者单位:Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of Connecticut
摘要:This article develops a class of novel algo-rithms for online convex optimization. The key constructis a forgetting-factor regret. It introduces weights to theobjective functions at each time instanttand allows theweights of the past objective functions decaying to zero.We establish the forgetting-factor regret bounds of clas-sical algorithms including online gradient descent algo-rithms, online gradient-free algorithms, and online Frank-Wolfe algorithms. In addition, the article introduces on...
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作者:Ye, Linwei; Zhao, Zhonggai; Liu, Fei
作者单位:Jiangnan University; Jiangnan University
摘要:This article addresses the infinite-region linear quadratic regulation problem of the discrete two-dimensional (2-D) Roesser model, drawing inspiration from reinforcement learning principles. It introduces a novel proof establishing that expressing the optimal control law in 2-D through state feedback is unattainable. Subsequently, a policy iteration framework is proposed to derive suboptimal state feedback, followed by an exploration of the true optimal policy through value iteration. The eff...
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作者:Viana, Valessa V.; Kreiss, Jeremie; Jungers, Marc
作者单位:Universite de Lorraine; Centre National de la Recherche Scientifique (CNRS)
摘要:This article proposes a new approach for computing controlled invariant and output invisible subspaces of parameter-dependent systems. Nonstrictly proper systems are considered, and the system matrices can exhibit a polynomial dependence on the parameter. The proposed approach is applied in both contexts of constant and parameter-dependent inputs making the subspace invariant, referred to as the generalized or generalized adaptively controlled invariant and output invisible subspaces. A large ...
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作者:Sayedana, Borna; Afshari, Mohammad; Caines, Peter E.; Mahajan, Aditya
作者单位:McGill University; University System of Georgia; Georgia Institute of Technology
摘要:In this article, we investigate the problem of system identification for autonomous Markov jump linear systems (MJS) with complete state observations. We propose switched least squares method for identification of MJS, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-independent rate of convergence shows that, almost surely, the system identification error is ( O root log (T)/(T) where T is the time horiz...
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作者:Sassano, Mario
作者单位:University of Rome Tor Vergata
摘要:Within the framework of finite-horizon optimal control problems involving nonlinear, input-affine dynamics, a connection between the costate variable and generating functions of the annihilating codistribution of the underlying Hamiltonian vector field is established. It is shown that the inverse mapping of any collection of n, such generating functions coincide, for any time and for a certain constant vector, with the costate of the optimal process. In particular, the corresponding constant v...
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作者:Li, Kuo; Ding, Steven X.; Zheng, Wei Xing; Hua, Chang-Chun
作者单位:University of Duisburg Essen; Western Sydney University; Yanshan University
摘要:This article is concerned with the distributed leader-following fault-tolerant consensus control problem of uncertain nonlinear delayed multiagent systems with hybrid faults including actuator faults and sensor faults. The faults are described as unknown time-varying functions, which can cause uncertain changes in the fault coefficients of sensors and actuators. In this case, we put forward a novel distributed consensus algorithm. First, we transform the consensus problem into the stability on...
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作者:Chen, Guangwei; Vazquez, Rafael; Krstic, Miroslav
作者单位:Beijing University of Technology; University of Sevilla; University of California System; University of California San Diego
摘要:In this article, we present rapid boundary stabilization of a Timoshenko beam with antidamping and antistiffness at the uncontrolled boundary, by using infinite-dimensional backstepping. We introduce a Riemann transformation to map the Timoshenko beam states into a set of coordinates that verify a 1-D hyperbolic PIDE-ODE system. Then backstepping is applied to obtain a control law guaranteeing closed-loop stability of the origin in the $L<^>{2}$ sense. Arbitrarily rapid stabilization can be ac...