检索结果-内蒙古大学图书馆

2008 American control Conference (ACC), vol.4

作者： Stephen L. Smith Francesco Bullo Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

In this paper we focus on problems in which tasks (demands for service) arrive in an environment sequentially over time. A task is completed when a robot (or team of robots) provides the required service, and the goal is to minimize the expected delay between a task's arrival, and its completion. We develop a general framework in which these problems can be described, and propose a set of scaling laws for studying the relationship between the number of robots, the expected task delay, and the task arrival rate. We describe two existing problems in our framework, namely the dynamic traveling repairperson problem, and the dynamic pickup delivery problem, and present their asymptotic performance. We then introduce the dynamic team forming problem, in which tasks require services that can be provided only through complex teams of heterogeneous robots. We determine a lower bound on the problem's achievable performance, and propose three policies for solving the problem. We show that for each policy, there is a broad class of tasks for which the policy's performance is within a constant factor the optimal.

关键词： Throughput Robots Wireless networks Delay effects Relays Dynamic scheduling Space exploration Queueing analysis

来源：评论

学校读者我要写书评

暂无评论

Stochastic analysis of noisy gene expression

Stochastic analysis of noisy gene expression

引用

International Symposium on Communications control and Signal Processing (ISCCSP)

作者： Mustafa Khammash Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

We give an overview of the numerical and analytical methods for modeling and analysis of cellular noise in gene regulatory networks.

关键词： Stochastic processes Gene expression Stochastic resonance Fluctuations Biological system modeling Working environment noise Kinetic theory Analytical models Chemicals Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

computation of Switch Time Distributions in Stochastic Gene Regulatory Networks

Computation of Switch Time Distributions in Stochastic Gene ...

引用

2008 American control Conference (ACC), vol.7

作者： Brian Munsky Mustafa Khammash Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

Many gene regulatory networks are modeled at the mesoscopic scale, where chemical populations are assumed to change according a discrete state (jump) Markov process. The chemical master equation (CME) for such a process is typically infinite dimensional and is unlikely to be computationally tractable without further reduction. The recently proposed Finite State Projection (FSP) technique allows for a bulk reduction of the CME while explicitly keeping track of its own approximation error. In previous work, this error has been reduced in order to obtain more accurate CME solutions for many biological examples. In this paper, we show that this "error" has far more significance than simply the distance between the approximate and exact solutions of the CME. In particular, we show that apart from its use as a measure for the quality of approximation, this error term serves as an exact measure of the rate of first transition from one system region to another. We demonstrate how this term may be used to directly determine the statistical distributions for stochastic switch rates, escape times, trajectory periods, and trajectory bifurcations. We illustrate the benefits of this approach to analyze the switching behavior of a stochastic model of Gardner's genetic toggle switch.

关键词： Switches Computer networks Distributed computing Stochastic processes Chemical processes Particle measurements Markov processes Equations Biology computing Approximation error

来源：评论

学校读者我要写书评

暂无评论

Multi-group SIS epidemics with simplicial and higher-order interactions

arXiv

引用

arXiv 2020年

作者： Cisneros-Velarde, Pedro Bullo, Francesco The Center for Control Dynamical Systems and Computation University of California Santa Barbara United States

This paper analyzes a Susceptible-Infected-Susceptible (SIS) model of epidemic propagation over hypergraphs and, motivated by an important special case, we refer to the model as to the simplicial SIS model. Classically, the multi-group SIS model has assumed pairwise interactions of contagion across groups and thus has been vastly studied in the literature. It is only recently that a renewed special attention has been drawn to the study of contagion dynamics over higher-order interactions and over more general graph structures, like simplexes. Previous work on mean-field approximation scalar models of the simplicial SIS model has indicated that a new dynamical behavior domain, compared to the classical SIS model, appears due to the newly introduced higher order interaction terms: both a disease-free equilibrium and an endemic equilibrium coexist and are both locally asymptotically stable. This paper formally establishes that bistability (as a new epidemiological behavior) also appears in the multi-group simplicial SIS model. We give sufficient conditions over the model's parameters for the appearance of this and the other behavioral domains present in the classical multi-group SIS model. We additionally provide an algorithm to compute the value of the endemic equilibrium and report numerical analysis of the transition from the disease-free domain to the bistable *** Codes 92B05, 37N25, 37N99 Copyright © 2020, The Authors. All rights reserved.

关键词： Epidemiology

来源：评论

学校读者我要写书评

暂无评论

Experimental Investigation of Fully Informed Particle Swarm Optimization tuned Multi Loop L-PID and NL-PID controllers for Gantry Crane System

引用

Procedia Computer Science 2020年 171卷 130-138页

作者： Sudarshan K. Valluru Manpreet Kaur Kumar Kartikeya Arnav Goel Daksh Dobhal Incubation Center for Control Dynamical Systems and Computation Department of Electrical Engineering Delhi Technological University. Delhi-110042 India

This paper presents a novel stabilizing nonlinear PID controller with real time experimental validation on a gantry crane system. Our adaptive control technique describes the underactuated system as a nonlinear mapping of control signal with the hyperbolic tangent function. The proposed function is used to impose a constraint on the proportional, integral and derivative errors such that the system results an effective and robust performance. The main trait of the controller is to bound the high control error to result in a lesser correction value. An enhanced hybrid method based on the bioinspired meta-heuristic algorithm Particle Swarm Optimisation is utilised to tune the gain coefficients K p δ , K I δ and K D δ . The Linear controller and the proposed controllers were compared based on the stabilizing action on the real time system. The real time results depict that the nonlinear PID performs better than the conventional PID.

关键词： PID FIPSO Gantry Crane Hyperbolic Tangent

来源：评论

学校读者我要写书评

暂无评论

Construction of Lyapunov Functions for Piecewise-Deterministic Markov Processes

Construction of Lyapunov Functions for Piecewise-Determinist...

引用

2010 49th IEEE Conference on Decision and control

作者： Alexandre R. Mesquita Joao P. Hespanha Center for Control Dynamical Systems and Computation University of California Santa Barbara CA 93106

ISBN: (纸本)9781424477456

The purpose of this contribution is twofold: 1) to present for the first time a Lyapunov function that proves exponential ergodicity of a process studied by the authors in [1], where the problem of controlling the probability density of a swarm of robotic agents was solved;2) to introduce alongside the method used to construct this Lyapunov function, which is of interest in its own since it may be applicable to a wide class of piecewise-deterministc Markov processes. Our method searches for the Lyapunov function that maximizes a measure of the rate of convergence that appears in the theory of large deviations. Analytical solutions are often possible as shown by examples.

关键词： Liapunov function Markov chain dike swarms

来源：评论

学校读者我要写书评

暂无评论

Opinion evolution in time-varying social influence networks with prejudiced agents

引用

IFAC-PapersOnLine 2017年第1期50卷 11896-11901页

作者： Proskurnikov, Anton V. Tempo, Roberto Cao, Ming Friedkin, Noah E. Delft University of Technology Netherlands St. Petersburg Russia ITMO University St. Petersburg Russia CNR-IEIIT Politecnico di Torino Torino Italy University of Groningen Netherlands Center for Control Dynamical Systems and Computation University of California Santa Barbara Santa Barbara United States

Investigation of social influence dynamics requires mathematical models that are “simple” enough to admit rigorous analysis, and yet sufficiently “rich” to capture salient features of social groups. Thus, the mechanism of iterative opinion pooling from (DeGroot, 1974), which can explain the generation of consensus, was elaborated in (Friedkin and Johnsen, 1999) to take into account individuals’ ongoing attachments to their initial opinions, or prejudices. The “anchorage” of individuals to their prejudices may disable reaching consensus and cause disagreement in a social influence network. Further elaboration of this model may be achieved by relaxing its restrictive assumption of a time-invariant influence network. During opinion dynamics on an issue, arcs of interpersonal influence may be added or subtracted from the network, and the influence weights assigned by an individual to his/her neighbors may alter. In this paper, we establish new important properties of the (Friedkin and Johnsen, 1999) opinion formation model, and also examine its extension to time-varying social influence networks. © 2017

关键词： Economic and social effects Time varying networks Influence networks Interpersonal influences Opinion dynamics Opinion formation models Rigorous analysis Salient features Social influence Time invariants

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

A distributed simplex algorithm and the multi-agent assignment problem

A distributed simplex algorithm and the multi-agent assignme...

引用

作者： Bürger, Mathias Notarstefano, Giuseppe Allgöwer, Frank Bullo, Francesco Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70550 Stuttgart Germany Department of Engineering University of Lecce Via per Monteroni 73100 Lecce Italy Center for Control Dynamical Systems and Computation University of California Santa Barbara CA 93106 United States

ISBN: (纸本)9781457700804

In this paper we propose a novel distributed algorithm to solve degenerate linear programs on asynchronous networks. Namely, we propose a distributed version of the well known simplex algorithm. We prove its convergence to the global lexicographic minimum for possibly fully degenerate problems and provide simulations supporting the conjecture that the completion time scales linearly with the diameter of the graph. The algorithm can be interpreted as a dual version of the constraints consensus algorithm proposed in [1] to solve abstract programs when the last is applied to linear programs. Finally, we study a multi-agent task assignment problem and show that it can be solved by means of our distributed simplex algorithm. © 2011 AACC American Automatic control Council.

关键词： Linear programming

来源：评论

学校读者我要写书评

暂无评论

Convergent Second-Order Methods for Min-Max Optimizations

Convergent Second-Order Methods for Min-Max Optimizations

引用

American control Conference (ACC)

作者： Raphael Chinchilla Guosong Yang João Hespanha Center for Control Dynamical Systems and Computation University of California Santa Barbara United States of America

We address the use of second-order methods to solve optimizations of the form\begin{equation*}\mathop {\min }\limits_{u \in \mathcal{U}} \mathop {\max }\limits_{d \in \mathcal{D}} f(u,d),\tagcontrol\end{equation*}for a twice continuously differentiable function $f:\mathcal{U} \times \mathcal{D} \to \mathbb{R}$ and sets $\mathcal{U} \subset {\mathbb{R}^{{n_u}}},\mathcal{D} \subset {\mathbb{R}^{{n_d}}}$. This type of optimization arises in numerous applications, including robust machine learning [1], model predictive control [2], [3], and in reformulating stochastic programming as a min-max optimizations [4], [5].When the sets $\mathcal{U}$ and $\mathcal{D}$ are compact and convex and the function f (u, d) is convex with respect to u and concave with respect to d, the min and max in (1) commute [6] and the optimization becomes relatively simple. However, we are especially interested here in problems for which such assumptions do not hold, the min and max do not commute, and for which the optimizations may have local optima that are not *** this talk, we address the design of algorithms to solve nonconvex-nonconcave min-max optimizations like (1) using second order methods. These algorithms modify the Hessian matrix to obtain a search direction that can be seen as the solution to a quadratic program that locally approximates the min-max problem. We show that by selecting this type of modification appropriately, the only stable points of the resulting iterations are local min-max points. For min-max model predictive control problems, these algorithms leads to computation times that scale linearly with the horizon *** more information please see the main tutorial paper [7].

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：