检索结果-内蒙古大学图书馆

第三十一届中国控制会议

作者： ZHENG Yi~(1,2),LI Shaoyuan~1,WU Jie~3,ZHANG Xianxia~4 1.Department of Automation,Shanghai Jiao Tong University,Key Laboratory of System control and Information Processing,Ministry of Education of China,Shanghai 200240,China 2.Department of Electrical Engineering,Shanghai Jiao Tong University,China 3.Bank of China Investment Management,Shanghai 200120,China 4.Shanghai Key Laboratory of Power Station Automation Technology,Shanghai University,Shanghai 200072,China

A class of large scale systems,which is naturally divided into many small interacting subsystems with constraints, are usually controlled by a distributed or decentralized control *** model predictive control(DMPC),in which each subsystem is controlled by a model predictive control(MPC),is a method of choice in the venues of explicitly accommodating constraints,less computational cost and high *** stability and global performance improvement are the difficulties of *** an extend of previous work of Neighborhood Optimization based distributed MPC(NO-DMPC), a stabilized NO-DMPC(SNO-DMPC) is *** each subsystem-based MPC,not only the performance of itself but also the performance of its' neighbors are taken as cost function to improve the dynamic performance of entire system with less communication and computational *** consistency and stabilization constraints,as will as the bound of them are designed and the dual mode strategy is adopted to guarantee the closed-loop system *** an initially feasible solution can be found,subsequent feasibility of the algorithm is guaranteed at every update,and the asymptotic stabilization is established.

关键词： model predictive control distributed model predictive control distributed system neighborhood optimization

来源：评论

学校读者我要写书评

暂无评论

An evolutionary approach to modeling and control of space heating and thermal storage systems

引用

ENERGY AND BUILDINGS 2021年 234卷 110674-110674页

作者： Devia, William Agbossou, Kodjo Cardenas, Alben Univ Quebec Trois Rivieres UQTR Intelligent Energy Res & Innovat Lab 3351 Boul ForgesCP 500 Trois Rivieres PQ G9A 5H7 Canada

Home Energy Management systems are in a rapid development curve, supported by the advancements in computational intelligence, smart appliances, and new smart-grid frameworks. These systems are a fundamental part to implement demand-side management strategies and to shift local energy demand to periods of lower consumption effectively. In this paper, we explore the application of distributed co-evolutionary optimization algorithms and an agent-based architecture to reduce the consumption profile signature of the heating system during the critical peak demand periods, by reducing costs and respecting the comfort constraints of the occupants. The proposed control architecture targets the typical baseboard space heating systems and electrical thermal storage systems, as these represent a large portion of the energy usage in Nordic countries and are commonly controlled by room independent thermostats, which could be easily replaced by smart devices running an algorithm as the one presented in this work. Results prove the strategy proposed getting a cost reduction of up to 23% and a peak-to-average ratio decrease of up to 25% for reference scenarios. Also, an emulation Simulink model is developed to recreate a house and the different heating loads studied in this paper and an experimental test bed is built to model a real ETS system, two different complexity degree RC models are proposed to describe such systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed model predictive control Electric thermal storage Evolutionary algorithms Demand-side management Thermal parameter estimation NSGA-II

来源：评论

学校读者我要写书评

暂无评论

Combined Scheduling and control Design for the Coordination of Automated Vehicles at Intersections 21st

Combined Scheduling and Control Design for the Coordination ...

引用

21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Kneissl, Maximilian Molin, Adam Kehr, Sebastian Esen, Hasan Hirche, Sandra DENSO Automot Deutschland GmbH Corp R&D Freisinger Str 21-23 D-85386 Eching Germany Tech Univ Munich Inst Informat Oriented Control Arcisstr 21 D-80290 Munich Germany

Solving the problem of intersection crossing for autonomous vehicles is a challenging task due to combined combinatoric and dynamical control decisions. To reduce the complexity of the computations and distribute the resulting global optimization problem, we propose a combined scheduling-control method. Thereby, in this paper, we focus on the formulation of a resource-constrained-project-scheduling problem (RCPSP) to solve the combinatoric decision, i.e. the order in which vehicles cross an intersection area in a central coordination unit. This problem considers control decisions from the vehicles, which are computed using model predictive control (MPC) laws. In turn, the resulting scheduling solution is incorporated again in local vehicle MPC problems, which negotiate among each other to find a dynamically feasible solution. This seamless combination of scheduling and control results in efficient solutions, which is illustrated using numerical simulation and the results are compared with a first-come-firstserved (FCFS) strategy. Copyright (C) 2020 The Authors.

关键词： Interactive vehicle control Autonomous vehicles Intersection crossing distributed model predictive control Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

A distributed optimization algorithm for Nash bargaining in multi-agent systems 21st

A distributed optimization algorithm for Nash bargaining in ...

引用

21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Camisa, Andrea Koehler, Philipp N. Mueller, Matthias A. Notarstefano, Giuseppe Allgower, Frank Univ Bologna Dept Elect Elect & Informat Engn Alma Mater Studiorum Bologna Italy Univ Stuttgart Inst Syst Theory & Automat Control Stuttgart Germany Leibniz Univ Hannover Inst Automat Control Hannover Germany

In this paper, we consider a multi-objective optimization problem over networks in which agents aim to maximize their own objective function, while satisfying both local and coupling constraints. This set up includes, e.g., the computation of optimal steady states in multi-agent control systems. Since fairness is a key feature required for the solution, we resort to Cooperative Game Theory and search for the Nash bargaining solution among all the efficient (or Pareto optimal) points of a bargaining game. We propose a negotiation mechanism among the agents to compute such a solution in a distributed way. The problem is reformulated as the maximization of a properly weighted sum of the objective functions. The proposed algorithm is then a two step procedure in which local estimates of the Nash bargaining weights are updated online and existing distributed optimization algorithms are applied. The proposed method is formally analyzed for a particular case, while numerical simulations are provided to corroborate the theoretical findings and to demonstrate its efficacy. Copyright (C) 2020 The Authors.

关键词： Cooperative game theory distributed optimization Multi-Objective optimization Nash bargaining distributed model predictive control Network games

来源：评论

学校读者我要写书评

暂无评论

distributed Parameterized predictive control for Multi-robot Curve Tracking 21st

Distributed Parameterized Predictive Control for Multi-robot...

引用

21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Pacheco, Gabriel, V Pimenta, Luciano C. A. Raffo, Guilherme, V Univ Fed Minas Gerais Grad Program Elect Engn Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil

This work proposes a guidance strategy of multiple robots to converge and circulate a curve while avoiding collisions by using a distributed model predictive control. To build the model predictive control framework, systems guided by control laws with parameters are considered, which laws are embedded in the optimization problem. After that, the same problem is distributed using the Alternating Direction Method of Multipliers and nonlinear optimization. To solve the task of convergence and circulation of a closed path, a vector field based control law is embedded in the predictive control scheme. The control law results from the sum of two components, a convergence term and a circulation term, whereas each term has one proportional parameter associated. Numerical results present an application example, and the strategy effectiveness is discussed. Copyright (C) 2020 The Authors.

关键词： Vector fields distributed model predictive control alternating direction method of multipliers multi-robot systems coordination

来源：评论

学校读者我要写书评

暂无评论

Prediction-based Transmission-control Codesign for Vehicle Platooning 92

Prediction-based Transmission-Control Codesign for Vehicle P...

引用

92nd IEEE Vehicular Technology Conference (IEEE VTC-Fall)

作者： Hu, Yuying Chen, Cailian He, Jianping Yang, Bo Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781728194844

The advancements in automation and vehicular communication have enabled the platoon systems. The vehicles in a platoon are dynamically decoupled but constrained by spatial geometry. To coordinate decisions and act cooperatively, information sharing among vehicles is required, which means the platooning problem involves both communication and control issues. We consider a communication resource-aware control problem for platoon systems in this paper. To provide desirable driving performances and flexible communication scheduling opportunities for platoon systems, the distributed model predictive control (DMPC) method and communication trigger mechanism are jointly designed, which yields the communication-aware DMPC (CA-DMPC) algorithm. Simulations have been conducted to demonstrate the satisfactory control performances and significant communication savings.

关键词： Platoon network resource-aware distributed model predictive control communication-trigger

来源：评论

学校读者我要写书评

暂无评论

An iterative scheme for distributed model predictive control using Fenchel's duality

引用

JOURNAL OF PROCESS control 2011年第5期21卷 746-755页

作者： Minh Dang Doan Keviczky, Tamas De Schutter, Bart Delft Univ Technol Delft Netherlands

We present an iterative distributed version of Han's parallel method for convex optimization that can be used for distributed model predictive control (DMPC) of industrial processes described by dynamically coupled linear systems. The underlying decomposition technique relies on Fenchel's duality and allows subproblems to be solved using local communications only. We investigate two techniques aimed at improving the convergence rate of the iterative approach and illustrate the results using a numerical example. We conclude by discussing open issues of the proposed method and by providing an outlook on research in the field. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： distributed optimization Dual decomposition methods Decentralized and cooperative control distributed model predictive control

来源：评论

学校读者我要写书评

暂无评论

A robust distributed model predictive control algorithm

引用

JOURNAL OF PROCESS control 2011年第8期21卷 1127-1137页

作者： Al-Gherwi, Walid Budman, Hector Elkamel, Ali Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

Although distributed model predictive control (DMPC) has received significant attention in the literature, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, a novel online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations examples were considered to illustrate the application of the proposed method. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Robust control Linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Sensitivity-based coordination in distributed model predictive control

引用

JOURNAL OF PROCESS control 2011年第5期21卷 715-728页

作者： Scheu, Holger Marquardt, Wolfgang Rhein Westfal TH Aachen AVT Proc Syst Engn D-52056 Aachen Germany

A new distributed model-predictive control method is introduced, which is based on a novel distributed optimization algorithm, relying on a sensitivity-based coordination mechanism. Coordination and therefore overall optimality is achieved by means of a linear approximation of the objective functions of neighboring controllers within the objective function of each local controller. As for most of the distributed optimization methods, an iterative solution of the distributed optimal control problems is required. An analysis of the method with respect to its convergence properties is provided. For illustration, the sensitivity-driven distributed model-predictive control (S-DMPC) method is applied to a simulated alkylation process. An almost optimal control sequence can be achieved after only one iteration in this case. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control distributed optimization Optimal control Large-scale systems

来源：评论

学校读者我要写书评

暂无评论

distributed MPC for Coordinated Energy Efficiency Utilization in Microgrid Systems

引用

IEEE TRANSACTIONS ON SMART GRID 2019年第2期10卷 1781-1790页

作者： Du, Yigao Wu, Jing Li, Shaoyuan Long, Chengnian Paschalidis, Ioannis Ch Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Boston Univ Dept Elect & Comp Engn Boston MA 02215 USA Boston Univ Div Syst Engn Boston MA 02215 USA

To improve the renewable energy utilization of distributed microgrid systems, this paper presents an optimal distributed model predictive control strategy to coordinate energy management among microgrid systems. In particular, through information exchange among systems, each microgrid in the network, which includes renewable generation, storage systems, and some controllable loads, can maintain its own system-wide supply and demand balance. With our mechanism, the closed-loop stability of the distributed microgrid systems can be guaranteed. In addition, we provide evaluation criteria of renewable energy utilization to validate our proposed method. Simulations show that the supply demand balance in each microgrid is achieved while, at the same time, the system operation cost is reduced, which demonstrates the effectiveness and efficiency of our proposed policy.

关键词： Microgrids energy management strategy distributed model predictive control renewable energy sources coordinated scheduling

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：