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作者：Song, Weihao; Wang, Zidong; Li, Zhongkui; Dong, Hongli
作者单位：Peking University; Brunel University; Northeast Petroleum University; Northeast Petroleum University
摘要：In practice, the cache, capable of storing frequently accessed data, is widely deployed in edge servers to guarantee quick retrieval and improve overall system performance. In this article, the moving-horizon state estimation problem is investigated for a class of multisensor systems under the effects of limited caching capacity and sensor resolution. The measurement information collected by multiple sensors is first transmitted to an edge server for state estimation purposes and then stored i...
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作者：Zheng, Yang; Pai, Chih-Fan Rich; Tang, Yujie
作者单位：University of California System; University of California San Diego; Peking University
摘要：Many optimal and robust control problems are nonconvex and potentially nonsmooth in their policy optimization forms. In Part II of this article, we introduce a new and unified extended convex lifting (ECL) framework to reveal hidden convexity in classical optimal and robust control problems from a modern optimization perspective. Our ECL offers a bridge between nonconvex policy optimization and convex reformulations, enabling convex analysis for nonconvex problems. Despite nonconvexity and non...
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作者：Zhu, Wenbo; Hong, Yiguang
作者单位：Tongji University; Tongji University
摘要：In this article, we consider how a selfish agent manipulates the optimal value of multiagent optimization by local false information. First, we formulate a distributed optimization problem for multiagent systems with a selfish agent that aims to shift the global optimal state to its preference via local information manipulation. Then, based on a well-known distributed proportion-integral optimization, the deception strategies are proposed, and the exponential convergence of the manipulated alg...
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作者：Reiter, Sean; Duff, Igor Pontes; Gosea, Ion Victor; Gugercin, Serkan
作者单位：New York University; Max Planck Society
摘要：In this work, we consider the H-2-optimal model reduction of dynamical systems that are linear in the state equation with up to a quadratic nonlinearity in the output equation. As our primary theoretical contributions, we derive gradients of the squared H(2 )system error with respect to the reduced model quantities and, from the stationary points of these gradients, introduce Gramian-based first-order necessary conditions for the H-2-optimal approximation of a linear quadratic output (LQO) sys...
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作者：Watanabe, Yuto; Sakurama, Kazunori
作者单位：University of California System; University of California San Diego; University of Osaka
摘要：This study explores distributed optimization problems with cliquewise coupling via operator splitting and how we can utilize this framework for performance analysis and enhancement. This framework extends beyond conventional pairwise coupled problems (e.g., consensus optimization) and is applicable to broader examples. To this end, we first introduce a new distributed algorithm by leveraging a clique-based matrix and the Davis-Yin splitting (DYS), a three-operator splitting method. We then dem...
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作者：Plate, Christoph; Martensen, Carl Julius; Sager, Sebastian
作者单位：Otto von Guericke University; Max Planck Society
摘要：Complex dynamic systems are typically either modeled using expert knowledge in the form of differential equations or via data-driven universal approximation models, such as artificial neural networks (ANN). While the first approach has advantages with respect to interpretability, transparency, data efficiency, and extrapolation, the second approach is able to learn completely unknown functional relations from data and may result in models that can be evaluated more efficiently. To combine the ...
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作者：Shi, Shengling; Tsiamis, Anastasios; De Schutter, Bart
作者单位：Massachusetts Institute of Technology (MIT); Delft University of Technology; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：This work analyzes how the tradeoff between the modeling error, the terminal value function error, and the prediction horizon affects the performance of a nominal receding-horizon linear quadratic (LQ) controller. By developing a novel perturbation result of the Riccati difference equation, a novel performance upper bound is obtained and suggests that for many cases, the prediction horizon can be either 1 or $+\infty$ to improve the control performance, depending on the relative difference bet...
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作者：Geromel, Jose C.; Colaneri, Patrizio
作者单位：Universidade Estadual de Campinas; Consiglio Nazionale delle Ricerche (CNR); Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni (IEIIT-CNR)
摘要：This article tackles the optimal partial output feedback control problem for discrete-time time-invariant systems with quadratic cost. It is shown via the theory of periodic systems and using simple examples that the classical linear time-invariant $\mathcal {H}_{2}$ optimal full-order output feedback controller can be outperformed by a linear periodic full-order output feedback controller. In our opinion, this fact is not available till now in the literature. A new design procedure for the op...
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作者：Ortega, Romeo; Bobtsov, Alexey; Castanos, Fernando; Nikolaev, Nikolay
作者单位：Instituto Tecnologico Autonomo de Mexico; Hangzhou Dianzi University; ITMO University; Instituto Politecnico Nacional - Mexico; CINVESTAV - Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional; Technion Israel Institute of Technology
摘要：In this article, we apply the recently developed generalized parameter estimation-based observer design technique for state-affine systems to the practically important case of linear time-varying descriptor systems with uncertain parameters. We proceed from the general description of the system given by the standard canonical form and try to develop a comprehensive theory for the design of adaptive observers for these systems. A consequence of our intention to keep the analysis as general as p...
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作者：Xi, Jiachen; Garcia, Alfredo; Momcilovic, Petar
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：We consider a single-loop algorithm for regularized Q-learning with linear function approximation. The proposed algorithm is motivated by a bilevel optimization formulation of regularized Q-learning wherein the lower level optimization problem aims to identify a value function approximation that satisfies Bellman's recursive optimality condition, and the upper level aims to find the projection onto the span of basis vectors. We show that under certain assumptions, the proposed algorithm conver...
