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2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2005

作者： Medard, Muriel MIT Department of Electrical Engineering and Computer Science MIT Laboratory for Information and Decision Systems Simmons Hall

The advent of network coding has blurred the line between compression and routing in networks. In this talk, we overview the main principles of network coding and discuss the applicability of network coding to wireless network settings. In particular, we argue that the ablity to construct both network codes and minimum-cost subgraphs in a completely distributed fashion may provide considerable improvements, in performance and in ease of network management, over current routing-based approaches.

关键词： Wireless telecommunication systems

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Identifiability and Solvability in Inverse Linear Quadratic Optimal control Problems

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Journal of systems Science & Complexity 2021年第5期34卷 1840-1857页

作者： LI Yibei WAHLBERG Bo HU Xiaoming Optimization and Systems Theory Department of MathematicsKTH Royal Institute of TechnologySE-10044StockholmSweden Division of Decision and Control School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologySE-10044StockholmSweden

In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is *** the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.

关键词： Inverse optimal control linear quadratic regulators model identifiability

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A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第5期9卷 812-833页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technologyand also affiliated with the Digital FuturesStockholm 10044Sweden Department of Electrical Engineering University of North TexasDentonTX 76203 USA IEEE State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of n local cost functions by using local information exchange is *** problem is an important component of many machine learning techniques with data parallelism,such as deep learning and federated *** propose a distributed primal-dual stochastic gradient descent(SGD)algorithm,suitable for arbitrarily connected communication networks and any smooth(possibly nonconvex)cost *** show that the proposed algorithm achieves the linear speedup convergence rate O(1/(√nT))for general nonconvex cost functions and the linear speedup convergence rate O(1/(nT)) when the global cost function satisfies the Polyak-Lojasiewicz(P-L)condition,where T is the total number of *** also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global *** demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.

关键词： Distributed nonconvex optimization linear speedup Polyak-Lojasiewicz(P-L)condition primal-dual algorithm stochastic gradient descent

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A Survey of Model Predictive control Methods for Traffic Signal control

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 623-640页

作者： Bao-Lin Ye Weimin Wu Keyu Ruan Lingxi Li Tehuan Chen Huimin Gao Yaobin Chen IEEE the School of Mechanical and Electrical Engineering Jiaxing University the State Key Laboratory of Industrial Control Instituteof Cyber-Systems and Control Technology Zhejiang University the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Mechanical Engineering and Mechanics Ningbo University

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

关键词： Autonomous vehicles coordination control mixed integer programming model predictive control system decomposition traffic flow models traffic signal control

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Filtering and Stochastic control: A Historical Perspective

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IEEE control systems 1996年第3期16卷 67-76页

作者： Mitter, Sanjoy K. Department of Electrical Engineering and Computer Science and Laboratory for Information and Decision Systems Massachusetts Institute of Technology MA 02139 United States

We attempt to give a historical account of the main ideas leading to the development of nonlinear filtering and stochastic control as we know it today. We present a development of linear filtering theory, beginning with Wiener-Kolmogoroff filtering and ending with Kalman filtering. The linear-quadratic-Gaussian problem of stochastic control is considered and states that for this problem the optimal stochastic control can be constructed by solving separately a state estimation problem and a deterministic optimal control problem. Many of the ideas presented here generalize to the nonlinear situation. A reasonably detailed discussion of nonlinear filtering, again from the innovations viewpoint, is given. Finally, we deal with optimal stochastic control. The general method of discussing these problems is dynamic programming.

关键词： Stochastic processes Nonlinear filters Optimal control Filtering theory Maximum likelihood detection Wiener filter Kalman filters State estimation Technological innovation Dynamic programming

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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STATE FEEDBACK STABILIZATION FOR A CLASS OF NONLINEAR TIME-DELAY systems VIA DYNAMIC LINEAR controlLERS

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Journal of systems Science & Complexity 2014年第3期27卷 453-462页

作者： CHAI Lin QIAN Chunjiang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Department of Electrical and Computer Engineering University of Texas at San AntonioSan AntonioTX 78249USA

The dynamic linear state feedback control problem is addressed for a class of nonlinear systems subject to ***,using the dynamic change of coordinates,the problem of global state feedback stabilization is solved for a class of time-delay systems under a type of nonhomogeneous growth *** the aid of an appropriate Lyapunov-Krasovskii functional and the adaptive strategy used in coordinates,the closed-loop system can be globally asymptotically stabilized by the dynamic linear state feedback *** growth condition in perturbations are more general than that in the existing *** correctness of the theoretical results are illustrated with an academic simulation example.

关键词： Dynamic linear controller global stabilization nonlinear time-delay systems state feed-back control.

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GROUP CONSENSUS FOR MULTIPLE NETWORKED EULER-LAGRANGE systems WITH PARAMETRIC UNCERTAINTIES

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Journal of systems Science & Complexity 2014年第4期27卷 632-649页

作者： HU Hongxiang ZHANG Zhe YU Li YU Wenwu XIE Guangming Department of Mathematics Hangzhou Dianzi University Zhejiang Provincial United Key Laboratory of Embedded Systems Department of AutomationZhejiang University of Technology Department of Mathematics Southeast University School of Electrical and Computer Engineering RMIT University Faculty of Engineering King Abdulaziz University Center for Systems and Control State Key Laboratory of Turbulence and Complex SystemsCollege of EngineeringPeking University

In this paper,a group consensus problem is investigated for multiple networked agents with parametric uncertainties where all the agents are governed by the Euler-Lagrange system with uncertain *** the group consensus problem,the agents asymptotically reach several different states rather than one consistent state.A novel group consensus protocol and a time-varying estimator of the uncertain parameters are proposed for each agent in order to solve the couple-group consensus *** is shown that the group consensus is reachable even when the system contains the uncertain ***,the multi-group consensus is discussed as an extension of the couple-group consensus,and then the group consensus with switching topology is *** results are finally provided to validate the effectiveness of the theoretical analysis.

关键词： Euler-Lagrange system group consensus multi-agent systems switching topologies.

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Message passing and distributed statistical inference 45

Message passing and distributed statistical inference

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45th Annual Allerton Conference on Communication, control, and Computing 2007

作者： Chandrasekaran, Venkat Johnson, Jason Kreidl, O. Patrick Malioutov, Dmitry Sanghavi, Sujay Willsky, Alan S. Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems MIT CambridgeMA United States

ISBN: (纸本)9781605600864

In this paper , we summarize several research directions involving message-passing algorithms and distributed fusion. The starting point is inference for graphical models , but the different constraints and objectives found in sensor networks suggest new questions. In addition to providing pointers to a variety of other parts of our research in this area , we spend most of our time on two research investigations. The irst involves team-theoretic approaches to distributed decision-making and the development of message-passing algorithms for sensor network organization in order to optimize distributed team objectives. The second deals with the exploitation of so-called walk-sum analysis for distributed linear estimation. In particular under an easily checked condition for walk-summability , we can show that essentially any messaging strategy converges to the optimal distributed solution;moreover with modest use of local memory , this remains true in the presence of random message losses. We also describe how this framework leads to a tractable max-spanning tree problem for optimal adaptive message routing-i.e. , for determining a spanning tree for the next wave of messages to reduce an upper bound on estimation error as quickly as possible.

关键词： Sensor networks

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Blind regression: Nonparametric regression for latent variable models via collaborative filtering 30

Blind regression: Nonparametric regression for latent variab...

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30th Annual Conference on Neural Information Processing systems, NIPS 2016

作者： Lee, Christina E. Li, Yihua Shah, Devavrat Song, Dogyoon Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We introduce the framework of blind regression motivated by matrix completion for recommendation systems: given m users, n movies, and a subset of user-movie ratings, the goal is to predict the unobserved user-movie ratings given the data, i.e., to complete the partially observed matrix. Following the framework of non-parametric statistics, we posit that user u and movie i have features x1(u) and x2 (i) respectively, and their corresponding rating y(u, i) is a noisy measurement of f(x1(u), x2(i)) for some unknown function f. In contrast with classical regression, the features x = (x1(u), x2(i)) are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings. Inspired by the classical Taylor's expansion for differentiable functions, we provide a prediction algorithm that is consistent for all Lipschitz functions. In fact, the analysis through our framework naturally leads to a variant of collaborative filtering, shedding insight into the widespread success of collaborative filtering in practice. Assuming each entry is sampled independently with probability at least max(m-1+δ,n-1/2+δ) with δ > 0, we prove that the expected fraction of our estimates with error greater than e is less than γ2/ϵ2 plus a polynomially decaying term, where γ2 is the variance of the additive entry-wise noise term. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides principled improvements over basic collaborative filtering and is competitive with matrix factorization methods. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Collaborative filtering

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