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Optimization-based Estimation of Expected Values with Application to Stochastic Programming

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Optimization-based Estimation of Expected Values with Applic...

IEEE Annual Conference on Decision and control

作者： Raphael Chinchilla Joao P. Hespanha Center for Control Dynamical Systems and Computation (CCDC) University of California Santa Barbara CA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

This paper constructs bounds on the expected value of a scalar function of a random vector. The bounds are obtained using an optimization method, which can be computed efficiently using state-of-the-art solvers, and do not require integration or sampling the random vector. This optimization based approach is especially useful in stochastic programming, where the criteria to be minimized takes the form of an expected value. In particular, we minimize the bounds to solve problems of discrete time finite horizon open-loop control with stochastic perturbations and also uncertainty in the system's parameters. We illustrate this application with two numerical examples.

关键词： Optimization Programming Upper bound Entropy Random variables Monte Carlo methods Loss measurement

Iterative algorithms for distributed leader-follower model predictive control

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Iterative algorithms for distributed leader-follower model p...

IEEE Annual Conference on Decision and control

作者： Henrique Ferraz Joao P. Hespanha Center for Control Dynamical-systems and Computation (CCDC) University of California Santa Barbara CA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Two distributed algorithms to estimate the optimal control input sequence that solves a finite horizon quadratic optimization are proposed. The first algorithm utilizes information from 2-hop neighbors, whereas the second only considers 1-hop neighbors. The estimates obtained from both algorithms converge asymptotically, under appropriate assumptions, for any initialization of the algorithm. For the 2-hop algorithm, we show that the converged estimate is the optimal solution to the original optimization problem, while for the 1-hop algorithm the result is generally a suboptimal solution. We evaluate the methods with simulations for a leader-follower model predictive control problem with unstable linear agents dynamics.

关键词： Prediction algorithms Predictive control Heuristic algorithms Optimal control Cost function Distributed algorithms

Heavy-Tailed Distribution of the Number of Papers within Scientific Journals

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arXiv 2020年

作者： Delabays, Robin Tyloo, Melvyn Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

Scholarly publications represent at least two benefits for the study of the scientific community as a social group. First, they attest of some form of relation between scientists (collaborations, mentoring, heritage,...), useful to determine and analyze social subgroups. Second, most of them are recorded in large data bases, easily accessible and including a lot of pertinent information, easing the quantitative and qualitative study of the scientific community. Understanding the underlying dynamics driving the creation of knowledge in general, and of scientific publication in particular can contribute to maintaining a high level of research, by identifying good and bad practices in science. In this article, we aim at advancing this understanding by a statistical analysis of publication within peer-reviewed journals. Namely, we show that the distribution of the number of papers published by an author in a given journal is heavy-tailed, but has lighter tail than a power law. Interestingly, we demonstrate (both analytically and numerically) that such distributions match the result of an modified preferential attachment process, where, on top of a Barabási-Albert process, we take finite career span of scientists into account. © 2020, CC BY.

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When smoothness is not enough: Toward exact quantification and optimization of the price-of-anarchy

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arXiv 2019年

作者： Chandan, Rahul Paccagnan, Dario Marden, Jason R. Department of Electrical and Computer Engineering Center of Control Dynamical Systems and Computation UC Santa Barbara United States Department of Mechanical Engineering Center of Control Dynamical Systems and Computation UC Santa Barbara United States

Today's multiagent systems have grown too complex to rely on centralized controllers, prompting increasing interest in the design of distributed algorithms. In this respect, game theory has emerged as a valuable tool to complement more traditional techniques. The fundamental idea behind this approach is the assignment of agents' local cost functions, such that their selfish minimization attains, or is provably close to, the global objective. Any algorithm capable of computing an equilibrium of the corresponding game inherits an approximation ratio that is, in the worst case, equal to its price-of-anarchy. Therefore, a successful application of the game design approach hinges on the possibility to quantify and optimize the equilibrium performance. Toward this end, we introduce the notion of generalized smoothness, and show that the resulting efficiency bounds are significantly tighter compared to those obtained using the traditional smoothness approach. Leveraging this newly-introduced notion, we quantify the equilibrium performance for the class of local resource allocation games. Finally, we show how the agents' local decision rules can be designed in order to optimize the efficiency of the corresponding equilibria, by means of a tractable linear program. Copyright © 2019, The Authors. All rights reserved.

关键词： Linear programming

Stable and Efficient Structures in Multigroup Network Formation

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arXiv 2020年

作者： Mohagheghi, Shadi Ma, Jingying Bullo, Francesco Department of Electrical and Computer Engineering Center for Control Dynamical Systems and Computation University of California at Santa Barbara Santa BarbaraCA93106-9560 United States School of Mathematics and Statistics Ningxia University Yinchuan750021 China

In this work we present a strategic network formation model predicting the emergence of multigroup structures. Individuals decide to form or remove links based on the benefits and costs those connections carry;we focus on bilateral consent for link formation. An exogenous system specifies the frequency of coordination issues arising among the groups. We are interested in structures that arise to resolve coordination issues and, specifically, structures in which groups are linked through bridging, redundant, and co-membership interconnections. We characterize the conditions under which certain structures are stable and study their efficiency as well as the convergence of formation dynamics. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

Transient stability of droop-controlled inverter networks with operating constraints

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arXiv 2019年

作者： Smith, Kevin D. Jafarpour, Saber Bullo, Francesco Center of Control Dynamical Systems and Computation UC Santa Barbara CA93106-5070 United States

Due to the rise of distributed energy resources, the control of networks of grid-forming inverters is now a pressing issue for power system operation. Droop control is a popular control strategy in the literature for frequency control of these inverters. In this paper, we analyze transient stability in droopcontrolled inverter networks that are subject to multiple operating constraints. Using two physically-meaningful Lyapunov-like functions, we provide two sets of criteria (one mathematical and one computational) to certify that a post-fault trajectory achieves frequency synchronization while respecting operating constraints. We demonstrate two applications of these results on a modified IEEE RTS 24 test case: estimating the scale of disturbances with respect to which the system is robust, and screening for contingencies that threaten transient stability. Copyright © 2019, The Authors. All rights reserved.

关键词： Electric inverters

Game Theoretic Potential Field for Autonomous Water Surface Vehicle Navigation Using Weather Forecasts

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Game Theoretic Potential Field for Autonomous Water Surface ...

American control Conference

作者： Evan Krell Luis Rodolfo Garcia Carrillo Scott A. King Joao P. Hespanha Control of Robots and Autonomous Agents Laboratory (CORAL) Texas A&M University Corpus Christi CORAL and the Unmanned Systems Laboratory (TAMUCC-USL) School of Engineering & Computing Sciences Texas A&M University Corpus Christi Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Imperfect weather forecasts complicate robot planning. A conservative motion planning algorithm is developed to address uncertainty in autonomous boat missions. Dynamic Programming generates an optimal action for every location and game theory strategically handles uncertainty. Experimental results using Massachusetts Bay forecasts show the robot is able minimize the cost of worst-case weather.

关键词： Robots Planning Force Games Heuristic algorithms Weather forecasting

WITHDRAWN: Rethinking the Micro-Foundation of Opinion Dynamics: Rich Consequences of an Inconspicuous Change

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IFAC-PapersOnLine 2020年第5期53卷 307-310页

作者： Wenjun Mei Francesco Bullo Ge Chen Julien M. Hendrickx Florian Dörfler Automatic Control Laboratory ETH Zurich 8006 Zurich Switzerland Center for Control Dynamical Systems and Computation University of California Santa Barbara CA 93106 USA Academy of Mathematics and Systems Science Chinese Academy of Science Beijing 100190 China Institute of Information and Communication Technologies Electronics and Applied Mathematics Université catholique de Louvain Louvain-la-Neuve B-1348 Belgium

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( http://***/locate/withdrawalpolicy ). This article has been withdrawn at the request of the editor and publisher. The publisher regrets that an error occurred which led to the premature publication of this paper. This error bears no reflection on the article or its authors. The publisher apologizes to the authors and the readers for this unfortunate error.

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Design of multi-loop L-PID and NL-PID controllers: An experimental validation 2

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Design of multi-loop L-PID and NL-PID controllers: An experi...

2nd IEEE International Conference on Power Electronics, Intelligent control and Energy systems, ICPEICES 2018

作者： Valluru, Sudarshan K. Singh, Madhusudan Goel, Arnav Kaur, Manpreet Dobhal, Daksh Kartikeya, Kumar Verma, Aditya Gupta, Anshul Delhi Technological University Incubation Center for Control Dynamical Systems and Computation Delhi110042 India

ISBN: (纸本)9781538666258

The three-term PID controllers are significantly used in most of the benchmarked systems as controllers. However, the benchmarked Single Input Multiple Output (SIMO) systems may require more than one PID controllers in the control loops, the way to design PID controllers is not an easy task. Although, the linear PID (L-PID) controllers are not yielded a satisfactory control results for a benchmarked system due to the presence of inherent nonlinearities. In order to improve performance, the linear PID controllers are modified by nonlinear characteristics. In this paper, the linear and nonlinear PID (NL-PID) controllers are implemented for the stabilization and control of Gantry Crane System as a test problem. The experimental results prove that the multi-loop NL-PID controllers exhibit more robustness to outer large with fast external disturbances. The L-PID and NL-PID controllers are compared to find out the best controller, which gives optimum responses and can also handle external disturbances. © 2018 IEEE.

关键词： controllers