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Rapid distributed model predictive control design using singular value decomposition for linear systems

JOURNAL OF PROCESS control 2014年第7期24卷 1135-1148页

作者： Cai, Xing Sun, Pei Chen, Junghui Xie, Lei Zhejiang Univ Inst Cyber Syst & Control Natl Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Chung Yuan Christian Univ Dept Chem Engn Chungli 320 Taiwan

The issue of model predictive control design of distribution systems using a popular singular value decomposition (SVD) technique is addressed. Namely, projection to a set of conjugate structure is dealt with in this paper. The structure of the resulting predictive model is decomposed into small sets of subsystems. The optimal inputs can be separately designed at each subsystem in parallel without any interaction problems. The optimal inputs can be directly obtained and the communication among the subsystems can be significantly reduced. In addition, the design of distribution model predictive control (DMPC) with constraints using the SVD framework is also presented. The unconstraint inputs are checked in parallel in the conjugate space. Without solving the QP problem of each subsystem, the suboptimal solution can be quickly obtained by selecting the bigger singular values and discarding the small singular values in the singular value space. The convergence condition of the proposed algorithm is also proved. Two case studies are used to illustrate the distribution control systems using the suggested approach. Comparisons between the centralized model predictive control method and the proposed DMPC method are carried out to show the advantages of the newly proposed method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Decomposition distributed model predictive control Parallel computing Singular value decomposition

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distributed model predictive control Based on Multi-agent model for Electric Multiple Units

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Acta Automatica Sinica 2014年第11期40卷 2625-2631页

作者： Zhong-Qi LI Hui YANG Kun-Peng ZHANG Ya-Ting FU School of Mechanical and Electrical Engineer Nanchang University Nanchang 330031 China School of Electrical and Electronic Engineering East China Jiaotong University Nanchang 330013 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China

The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.

关键词： Electric multiple units (EMUs) multi-agent nonlinear multi-models distributed model predictive control synchronous tracking

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Constrained distributed model predictive control for state-delayed systems with polytopic uncertainty description

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2014年第8期36卷 954-962页

作者： Zhang, Langwen Wang, Jingcheng Ge, Yang Wang, Bohui 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

Although distributed model predictive control (MPC) has received significant attention in the literature, the robustness of distributed MPC with respect to model uncertainties and state delays has not been explicitly addressed. In this paper, a novel approach to design robust distributed MPC is proposed for polytopic uncertain systems with state delays. The algorithm requires decomposing the entire system into M subsystems and solving M linear matrix inequality optimization problems to minimize an upper bound on a robust performance objective for each subsystem. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed controllers. The algorithm is a flexible structure of robust control, which allows the independent computation of the state feedback laws for the subsystems. Convergence and robust stability of the proposed distributed MPC are analysed. Two numerical examples are carried out to demonstrate the effectiveness of the proposed algorithm.

关键词： distributed model predictive control polytopic uncertain systems constrained control state-delayed systems

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A distributed reinforcement learning and model predictive control architecture for routing decisions in urban road networks 22

A distributed reinforcement learning and model predictive co...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Giannini, Francesco Fortino, Giancarlo Franze, Giuseppe Pupo, Francesco Univ Calabria DIMES Calabria Italy Univ Calabria DIMEG Calabria Italy

ISBN: (纸本)9781713872344

In this paper, platoons of autonomous vehicles in urban road networks are considered. From a methodological point of view, the problem consists in characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data and a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Copyright (c) 2023 The Authors.

关键词： Autonomous vehicles Routing decisions distributed Reinforcement Learning distributed model predictive control

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On Feasibility, Stability and Performance in distributed model predictive control

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IEEE TRANSACTIONS ON AUTOMATIC control 2014年第4期59卷 1031-1036页

作者： Giselsson, Pontus Rantzer, Anders Lund Univ Dept Automat Control S-22100 Lund Sweden

In distributed model predictive control (DMPC), where a centralized optimization problemis solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.

关键词： distributed model predictive control feasibility performance guarantee stability

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distributed Economic model predictive control of a Catalytic Reactor: Evaluation of Sequential and Iterative Architectures 9

Distributed Economic Model Predictive Control of a Catalytic...

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9th IFAC Symposium on Advanced control of Chemical Processes ADCHEM 2015

作者： Anderson, Timothy L. Ellis, Matthew Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

The development, and application of distributed economic model predictive control (DEMPC) methodologies to a catalytic reactor is considered. Two DEMPC methodologies are designed For sequential and iterative implementation, respectively. The DEMPC architectures arc evaluated on the basis of the closed-loop performance and on-line computation time requirements compared to a centralized EMPC approach. For the catalytic reactor considered, DEMPC proves to be a viable option as it is able to give similar closed-loop performance while reducing the on-line computation time requirements relative to a centralized EMPC strategy. (C) 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved,

关键词： Economic model predictive control distributed model predictive control process control nonlinear systems

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distributed model predictive control of Wind and Solar Generation System

Distributed Model Predictive Control of Wind and Solar Gener...

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第三十三届中国控制会议

作者： Yubin.Jia X.J.Liu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University

ISBN: (纸本)9781479947249

distributed model predictive control for a hybrid system that comprises wind and photovoltaic generation subsystems, a battery bank and an ac load is developed in this paper. Consider that the wind subsystem and the solar subsystem are two spatial distributed energy generation systems, so we design a distributed MPC for optimal management and operation of distributed wind and solar energy generation system. The wind and solar generation system is characterized by nonlinearity. Therefore, neural model is used to approximating the dynamics of nonlinear process. Reasonable solution to the optimization and constraints by using distributed model predictive control is presented. The performance of the distributed model predictive control is show through computer simulation to illustrate the advantages of the proposed method.

关键词： distributed model predictive control wind and solar generation system neural model linearization

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Experimental results for distributed model predictive control applied to a water distribution system

Experimental results for distributed model predictive contro...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Hentzelt, Sebastian Klingler, Andreas Graichen, Knut Univ Ulm Inst Measurement Control & Microtechnol D-89069 Ulm Germany

ISBN: (纸本)9781479974061

In this paper, experimental results are presented for a distributed model predictive control ( DMPC) scheme applied to a laboratory-scale water distribution system consisting of connected water tanks. The setup is an example of coupled dynamical systems whose modular structure makes them candidates for agent based distributed control methods. A set of low cost Raspberry Pi microcomputers connected via a common Ethernet-network is used for the implementation of the DMPC. The DMPC algorithm employs an augmented Lagrangian approach to solve the distributed optimal control problem (OCP) subject to nonlinear system dynamics in continuous-time form and input constraints. In this way, the performance and modular character of the DMPC as well as the individual runtime footprints of the communication and computation with respect to the overall runtime of the distributed algorithm are studied.

关键词： continuous time systems distributed algorithms distributed control local area networks microcomputers nonlinear dynamical systems optimal control predictive control tanks (containers) water supply DMPC algorithm Ethernet-network OCP augmented Lagrangian approach connected water tanks continuous-time form coupled dynamical systems distributed algorithm distributed model predictive control scheme distributed optimal control problem input constraints laboratory-scale water distribution system low cost Raspberry Pi microcomputers modular character modular structure nonlinear system dynamics Couplings Heuristic algorithms Optimization Storage tanks Topology Valves Water resources distributed model predictive control distributed algorithms MODULAR CONSTRUCTIONS Micro computer Continuous time systems Water distribution systems decentralised control Local Area Networks Open circuit potential Storage Tanks Nonlinear dynamical systems Heuristic algorithms predictive control VALVE GATE Dimyristoylphosphatidylcholine

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distributed model predictive control Based on Dissipativity

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AICHE JOURNAL 2013年第3期59卷 787-804页

作者： Tippett, Michael James Bao, Jie Univ New South Wales UNSW Sch Chem Engn Sydney NSW 2052 Australia

A noncooperative approach to plant-wide distributed model predictive control based on dissipativity conditions is developed. The plant-wide process and distributed control system are represented as two interacting process and controller networks, with interaction effects captured by the dissipativity properties of subsystems and network topologies. The plant-wide stability and performance conditions are developed based on global dissipativity conditions, which in turn are translated into the dissipative trajectory conditions that each local model predictive control MPC must satisfy. This approach is enabled by the use of dynamic supply rates in quadratic difference forms, which capture detailed dynamic system information. A case study is presented to illustrate the results. (C) 2012 American Institute of Chemical Engineers AIChE J, 59: 787-804, 2013

关键词： process control distributed model predictive control dissipativity dynamic supply rate quadratic difference forms

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distributed model predictive control for polytopic uncertain systems subject to actuator saturation

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JOURNAL OF PROCESS control 2013年第8期23卷 1075-1089页

作者： Zhang, Langwen Wang, Jingcheng Li, Chuang 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

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min-max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Actuator saturation Polytopic uncertain Linear matrix inequalities

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