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作者：Zhang, Yu-Long; Liu, Shaojie; Wang, Zhaohui; Wang, Jun-Min; Li, Donghai; Zhu, Min
作者单位：Beijing Institute of Technology; Tsinghua University; Tsinghua University
摘要：In this article, we consider the stabilization of combustion oscillations in the Rijke tube, which is modeled by a linearized wave equation with heat release fluctuation ordinary differential equation (ODE) acting as a point source term. A boundary proportional-integral (PI) feedback controller is designed to suppress the combustion oscillations. The Nyquist criterion is applied to prove that eigenvalues of the operator of the close-loop system are all located on the left-hand side of the comp...
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作者：Eldesoukey, Asmaa; Georgiou, Tryphon T.
作者单位：University of California System; University of California Irvine
摘要：The problem of reconciling a prior probability law on paths with data was introduced by Schrodinger in 1931 and 1932. It represents an early formulation of a maximum likelihood problem. This specific formulation can also be seen as the control problem to modify the law of a diffusion process so as to match specifications on marginal distributions at given times. Thereby, in recent years, this so-called Schrodinger's bridge problem has been at the center of the uncertainty control development. ...
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作者：Lupien, Jean-Luc; Shames, Iman; Lesage-Landry, Antoine
作者单位：Mila Quebec Artificial Intelligence Institute; Universite de Montreal; Polytechnique Montreal; Universite de Montreal; Australian National University
摘要：An important challenge in the online convex optimization (OCO) setting is to incorporate generalized inequalities and time-varying constraints. The inclusion of constraints in OCO widens the applicability of such algorithms to dynamic and safety-critical settings such as the online optimal power flow (OPF) problem. In this work, we propose the first projection-free OCO algorithm admitting time-varying linear constraints and convex generalized inequalities: the online interior-point method for ...
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作者：Wang, Jiwei; Baldi, Simone; van Waarde, Henk J.
作者单位：Southeast University - China; University of Groningen; Southeast University - China
摘要：The objective of model reference control is to design a controller that regulates the system's behavior so as to match a specified reference model. This article investigates necessary and sufficient conditions for model reference control from a data-driven perspective, when only a set of data generated by the system is utilized to directly accomplish the matching. Noiseless and noisy data settings are both considered. Notably, all methods we propose build on the concept of data informativity a...
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作者：Chen, Jianqi; Chen, Wei; Chen, Chao; Qiu, Li
作者单位：Nanjing University; Peking University; Peking University; KU Leuven; Hong Kong University of Science & Technology; The Chinese University of Hong Kong, Shenzhen
摘要：This study first introduces the frequency-wise phases of n-port linear time-invariant networks based on recently defined phases of complex matrices. Such a phase characterization can be utilized to quantify capacitive, inductive, and passive behaviors of n-port networks, as well as to relate to the power factor of the networks. Further, a class of matrix operations induced by fairly common n-port network connections is examined. The intrinsic phase properties of networks under such connections...
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作者：Lin, Yifu; Li, Wenling; Zhang, Bin; Du, Junping
作者单位：Beihang University; Beijing University of Posts & Telecommunications; Beijing University of Posts & Telecommunications
摘要：This article explores the problem of distributed optimization for functions that are smooth and nonstrongly convex over directed networks. To address this issue, an improved distributed Nesterov gradient tracking (IDNGT) algorithm is proposed, which utilizes the adapt-then-combine rule and row-stochastic weights. A main novelty of the proposed algorithm is the introduction of a scale factor into the gradient tracking scheme to suppress the consensus error. By the estimate sequence approach, th...
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作者：Ye, Jikai; Jayaraman, Amitesh S.; Chirikjian, Gregory S.
作者单位：National University of Singapore; Stanford University; University of Delaware
摘要：In this article, we propose a general method for uncertainty propagation on unimodular matrix Lie groups that have a surjective exponential map when the initial probability density function is concentrated. We derive the exact formula for the propagation of mean and covariance expressed in the form of expectation in a continuous-time setting from the governing Fokker-Planck equation. Two approximate propagation methods are discussed based on the exact formula. One uses numerical quadrature and...
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作者：Rezaeinia, Pouya; Gharesifard, Bahman; Linder, Tamas
作者单位：Queens University - Canada; University of California System; University of California Los Angeles
摘要：In this article, we consider a distributed optimization problem for the sum of convex functions where the underlying communication network connecting nodes at each time epoch is drawn at random from a collection of directed graphs. We propose a modified version of the subgradient-push algorithm that provably almost surely converges to an optimizer on any such sequence of random directed graphs. We also prove that the convergence rate of our proposed algorithm is upper bounded as O(1/root t), w...
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作者：Jiang, Liangze; Wu, Zheng-Guang; Wang, Lei
作者单位：Zhejiang University
摘要：In this note, we study distributed time-varying optimization for a multiagent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the int...
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作者：Wang, Shimin; Guay, Martin; Chen, Zhiyong; Braatz, Richard D.
作者单位：Massachusetts Institute of Technology (MIT); Queens University - Canada; University of Newcastle
摘要：A nonparametric learning solution framework is proposed for the global nonlinear robust output regulation problem. We first extend the assumption that the steady-state generator is linear in the exogenous signal to the more relaxed assumption that it is polynomial in the exogenous signal. In addition, a nonparametric learning framework is proposed to eliminate the construction of an explicit regressor, as required in the adaptive method, which can potentially simplify the implementation and re...
