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作者：Garcia de Marina, Hector
作者单位：Complutense University of Madrid
摘要：In this article, we first propose a novel maneuvering technique compatible with displacement-consensus-based formation controllers. We show that the formation can be translated with an arbitrary velocity by modifying the weights in the consensus Laplacian matrix. In fact, we demonstrate that the displacement-consensus-based formation control is a particular case of our more general method. We then uncover robustness issues with undesired steady-state motions and resultant distorted shapes in u...
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作者：Wang, Jingyi; Feng, Jianwen; Lou, Yijun; Chen, Guanrong
作者单位：Shenzhen University; Hong Kong Polytechnic University; City University of Hong Kong
摘要：In this article, we propose a practicable quantized sampled velocity data coupling protocol for synchronization of a set of harmonic oscillators. The coupling protocol is designed in a quantized way via interconnecting the velocities encoded by a uniform quantizer with a zooming parameter in either a fixed or an adjustable form over a directed communication network. By constructing a suitable norm to measure the convergence of the synchronization errors, we establish sufficient conditions for ...
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作者：Fosson, Sophie M.
作者单位：Polytechnic University of Turin
摘要：The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious system identification. In most of the literature on sparse time-varying problems, some prior information on the system's evolution is assumed to be avai...
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作者：Maros, Marie; Jalden, Joakim
作者单位：Royal Institute of Technology; Purdue University System; Purdue University
摘要：In this article, we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA), that is proven to converge R-linearly to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. Like dual decomposition, PANDA requires half the amount of variable exchanges per iterate of methods based on DIGing, and can provide with practical im...
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作者：Alexandru, Andreea B.; Gatsis, Konstantinos; Shoukry, Yasser; Seshia, Sanjit A.; Tabuada, Paulo; Pappas, George J.
作者单位：University of Pennsylvania; University of Oxford; University of California System; University of California Irvine; University of California System; University of California Berkeley; University of California System; University of California Los Angeles
摘要：This article develops a cloud-based protocol for a constrained quadratic optimization problem involving multiple parties, each holding private data. The protocol is based on the projected gradient ascent on the Lagrange dual problem and exploits partially homomorphic encryption and secure communication techniques. Using formal cryptographic definitions of indistinguishability, the protocol is shown to achieve computational privacy. We show the implementation results of the protocol and discuss...
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作者：Angeli, David; Manfredi, Sabato
作者单位：Imperial College London; University of Florence; University of Naples Federico II
摘要：A generalized family of adversary robust consensus protocols is proposed and analyzed. These are distributed algorithms for multiagent systems seeking to agree on a common value of a shared variable, even in the presence of faulty or malicious agents, which are updating their local state according to the protocol rules. In particular, we adopt monotone joint-agent interactions, a very general mechanism for processing locally available information and allowing cross-comparisons between state-va...
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作者：Faradonbeh, Mohamad Kazem Shirani; Tewari, Ambuj; Michailidis, George
作者单位：State University System of Florida; University of Florida; State University System of Florida; University of Florida; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：The main challenge for adaptive regulation of linear-quadratic systems is the tradeoff between identification and control. An adaptive policy needs to address both the estimation of unknown dynamics parameters (exploration), as well as the regulation of the underlying system (exploitation). To this end, optimism-based methods that bias the identification in favor of optimistic approximations of the true parameter are employed in the literature. A number of asymptotic results have been establis...
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作者：Siami, Milad; Olshevsky, Alexander; Jadbabaie, Ali
作者单位：Northeastern University; Massachusetts Institute of Technology (MIT)
摘要：In this article, we investigate the problem of actuator selection for linear dynamical systems. We develop a framework to design a sparse actuator schedule for a given large-scale linear system with guaranteed performance bounds using deterministic polynomial-time and randomized approximately linear-time algorithms. First, we introduce systemic controllability metrics for linear dynamical systems that are monotone and homogeneous with respect to the controllability Gramian. We show that severa...
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作者：Gatsis, Konstantinos; Hassani, Hamed; Pappas, George J.
作者单位：University of Oxford; University of Pennsylvania
摘要：The emerging interest in low-latency high-reliability applications, such as connected vehicles, necessitates a new abstraction between communication and control. Thanks to advances in cyber-physical systems over the past decades, we understand this interface for classical bit-rate models of channels as well as packet-loss-type channels. This article proposes a new abstraction characterized as a tradeoff curve between latency, reliability, and rate. Our aim is to understand-do we (control engin...
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作者：Huang, Wenjie; Haskell, William B.
作者单位：Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Purdue University System; Purdue University
摘要：We develop a stochastic approximation-type algorithm to solve finite state/action, infinite-horizon, risk-aware Markov decision processes. Our algorithm has two loops. The inner loop computes the risk by solving a stochastic saddle-point problem. The outer loop performs Q-learning to compute an optimal risk-aware policy. Several widely investigated risk measures (e.g., conditional value-at-risk, optimized certainty equivalent, and absolute semideviation) are covered by our algorithm. Almost su...
