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GA based decomposition of large scale distributed model predictive control systems

control ENGINEERING PRACTICE 2016年第0期57卷 111-125页

作者： Xie, Lei Cai, Xing Chen, Junghui Su, Hongye Zhejiang Univ Inst Cyber Syst & Control Natl Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China United Technol Res Ctr China Shanghai 201204 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

A novel method is proposed to find the optimal decomposition structure of distributed model predictive control (DMPC) systems. The input clustering decomposition (ICD) is first developed to minimize the coupling effects of subsystems and average the computational balance of each subsystem. To select the inputs and outputs in each subsystem, the input-output pairing decomposition (IOPD) is done. Then the genetic algorithm is used to solve decomposition problems for ICD and IOPD. The proposed method can achieve efficient coordination. Its structure is more flexible than the traditional DMPC. Two examples are used to show the abilities of the proposed method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Genetic algorithm Input clustering decomposition Input-output pairing decomposition System decomposition

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Reference Tracking using a Non-Cooperative distributed model predictive control Algorithm

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IFAC-PapersOnLine 2016年第7期49卷 1079-1084页

作者： Maxim, Anca Ionescu, Clara M. Caruntu, Constantin F. Lazar, Corneliu Keyser, Robin De Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iasi Blvd. Mangeron 27 Iasi700050 Romania Department of Electrical energy Systems and Automation Ghent University Technologiepark 914 GentB9052 Belgium

In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for tracking constant references is developed and evaluated. As such, an augmented model is employed (i.e. the control loop is embedded with integrators) and the augmented state contains the state increments and the error between the reference and the predicted output. The algorithm is tested in real life experiments on the quadruple tank process with non-minimum phase behaviour. The experimental results show acceptable performance index for the DMPC method when compared with the centralized approach. © 2016

关键词： model predictive control MIMO systems predictive control systems Acceptable performance Centralized approaches distributed model predictive control Non cooperative Non minimum phase Non minimum phase systems Reference tracking Two agents

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Non-iterative distributed model predictive control for flexible vehicle platooning of connected vehicles

Non-iterative distributed model predictive control for flexi...

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American control Conference

作者： Peng Liu Umit Ozguner Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA

ISBN: (纸本)9781509045839

This work proposes a non-iterative distributed model predictive control (DMPC) scheme for flexible vehicle platooning with state and input constraints. The proposed DMPC scheme partitions the multi-vehicle system into alternate clusters belonging to two categories, and distributively solves constrained optimal control problems (COCPs) in a two-step manner at each sampling instant. At the first step, the scheme solves COCPs over one category of vehicle clusters that are disjoint. Then, at the second step, COCPs are solved over the other category with predicted states of common neighbors transmitted from the first step. Instead of applying compatibility constraints on all vehicles, only compatibility constraints of common neighbors of two neighboring clusters are required in the proposed scheme to ensure feasibility and closed-loop stability. The proposed DMPC law can be obtained by solving a semidefinite program (SDP). A flexible vehicle platooning example is simulated to illustrate the effectiveness of the proposed DMPC scheme.

关键词： Flexible Vehicle Platooning Car following vehicle distributed model predictive control FLEXIBLE consistency constraint Dimyristoylphosphatidylcholine Scheme

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Coordinated Non-Cooperative distributed model predictive control for Decoupled Systems Using Graphs

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IFAC-PapersOnLine 2016年第22期49卷 216-221页

作者： Alrifaee, Bassam Heßeler, Frank-Josef Abel, Dirk Institute of Automatic Control Department of Mechanical Engineering RWTH Aachen University Aachen52074 Germany

This paper proposes a novel strategy for Non-Cooperative distributed model predictive control (Non-Coop. DMPC) of Networked control Systems (NCS) consisting of dynamically decoupled agents where the coupling is achieved in the objective function or in the constraints. Moreover, the coupling is considered to be time-variant. We call this strategy Priority-Based Non-Coop. DMPC (PB-Non-Coop. DMPC). It is based on assigning priorities to the agents and the use of the coupling graph. Each PB-Non-Coop. DMPC is associated with a different agent and computes the local control inputs based only on its states and that of its neighbors. PB-Non-Coop. DMPC satisfies the prediction consistency property and reduces the overall computation time in comparison with existing Non-Coop. DMPC strategies in literature, thereby improves the overall behavior of NCS. We compare PB-Non-Coop. DMPC with centralized MPC as well as with another Non-Coop. DMPC strategy from literature. © 2016

关键词： model predictive control Networked control systems predictive control systems Computation time Consistency property Decoupled system distributed model predictive control Novel strategies Objective functions Priority Time variant

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distributed model predictive control of constrained spatially-invariant interconnected systems in input-output form

Distributed model predictive control of constrained spatiall...

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American control Conference

作者： Qin Liu Hossam S. Abbas Javad Mohammadpour Simon Wollnack Herbert Werner Complex Systems Control Laboratory College of Engineering University of Georgia Athens 30602 USA Electrical Engineering Department Faculty of Engineering Assiut University 71515 Egypt Institute of Control Systems Hamburg University of Technology 21075 Germany

ISBN: (纸本)9781467386838

This paper proposes a non-iterative, Lyapunov-based distributed model predictive control (MPC) design for invariant spatially-interconnected systems comprised of a network of subsystems with coupled dynamcis and subject to local state and/or input constraints. Considered here is the distributed MPC design for linear systems in input-output form with fixed-structure local state-feedback-like control law. The proposed distributed MPC design approach ensures asymptotic stability and recursive feasibility, and its online implementation can be formulated as solving a linear matrix inequality (LMI) problem defined at the subsystem level. Simulation results using a heat equation in one-dimensional space demonstrate the merits and effectiveness of the proposed approach.

关键词： Closed loop systems Interconnected systems Stability analysis Asymptotic stability Nickel predictive control Cost function large-scale systems distributed model predictive control Closed loop systems asymptotic stability region Cost functions predictive control INPUT-OUTPUT Stability analysis Nickel Maximum Allowable Concentration SUB-SYSTEM

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distributed model predictive control for consensus of sampled-data multi-agent systems with double-integrator dynamics

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IET control THEORY AND APPLICATIONS 2015年第12期9卷 1774-1780页

作者： Zhou, Lifeng Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

This study proposes a distributed model predictive control (MPC) strategy to achieve consensus of sampled-data multi-agent systems with double-integrator dynamics. On the basis of the error of state between each agent and the centre of its subsystem, a novel distributed MPC strategy (Algorithm 1) is obtained with the exchange of current states only. Then, a reverse iterative algorithm (Algorithm 2) is specially designed for the receding horizon optimisation of sampled-data double-integrator dynamics. Illustrative examples are finally displayed to verify the effectiveness and advantage of the distributed MPC consensus strategy and the impact of sampling period on consensus.

关键词： distributed control predictive control sampled data systems multi-robot systems iterative methods optimisation mobile robots control system synthesis autonomous aerial vehicles distributed model predictive control sampled-data multiagent system consensus double-integrator dynamics state exchange reverse iterative algorithm receding horizon optimisation sampled-data double-integrator dynamics distributed MPC consensus strategy sampling period

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distributed model predictive control Method for Optimal Coordination of Signal Splits in Urban Traffic Networks

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ASIAN JOURNAL OF control 2015年第3期17卷 775-790页

作者： Ye, Bao-Lin Wu, Weimin Mao, Weijie Zhejiang Univ Inst Cyber Syst & Control Natl Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

Coordination and control approaches based on model predictive control (MPC) have been widely investigated for traffic signal control in urban traffic networks. However, due to the complex non-linear characters of traffic flows and the large scale of traffic networks, a basic challenge faced by these approaches is the high online computational complexity. In this paper, to reduce the computational complexity and improve the applicability of traffic signal control approaches based on MPC in practice, we propose a distributed MPC approach (DCA-MPC) to coordinate and optimize the signal splits. Instead of describing the dynamics of traffic flow within each link of the traffic network with a simplified linear model, we present an improved nonlinear traffic model. Based on the nonlinear model, an MPC optimization framework for the signal splits control is developed, whereby the interactions between subsystems are accurately modeled by employing two interconnecting constraints. In addition, by designing a novel dual decomposition strategy, a distributed coordination algorithm is proposed. Finally, with a benchmark traffic network, experimental results are given to illustrate the effectiveness of the proposed method.

关键词： Urban traffic network distributed model predictive control dual decomposition coordination

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Asynchronous Cooperative method for distributed model predictive control 55

Asynchronous Cooperative method for Distributed Model Predic...

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55th IEEE Conference on Decision and control (CDC)

作者： Maffei, Alessio Iannelli, Luigi Glielmo, Luigi Borrelli, Francesco Univ Sannio Dipartimento Ingn Piazza Roma 21 I-82100 Benevento Italy Univ Calif Berkeley Mech Engn Berkeley CA 94720 USA

ISBN: (纸本)9781509018376

In this paper we consider a distributed solution of the model predictive control problem (DMPC), based on the block version of the Jacobi algorithm applied to the dual problem. In order to accelerate the convergence, a Nesterov's schema can be considered, but the updating rule coming out in such way is fully distributed and parallel, but synchronous. This assumption is often unrealistic in real-life large-scale systems. For this reason an asynchronous version of the method has been proposed and the convergence properties have been studied. Numerical experiments show the effectiveness of the approach by comparing it with the methods presented in the literature.

关键词： distributed model predictive control networks convex optimization

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distributed model predictive control Based on Nash Optimality for Large Scale Irrigation Systems 9

Distributed Model Predictive Control Based on Nash Optimalit...

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

作者： Zhang, Rongchao Liu, Andong Yu, Li Zhang, Wen-an Zhejiang Univ Technol Dept Automat Zhejiang Prov United Key Lab Embedded Syst Hangzhou 310023 Zhejiang Peoples R China

Irrigation system is a large scale system, consisting of many interacting channels, and spanning vast geographical areas. In practice, the water level is expected to the desired reference values for safety and efficiency. In this paper, the model of irrigation system is modeled by the principle on conservation of mass. Because of the strong coupling between neighboring pools in irrigation system and large disturbance from the environment, a non-cooperative distributed model predictive control (NDMPC;) algorithm based on Nash optimality is proposed for the large scale irrigation system to design the controller for setting points regulation. Finally, a simulation example is given to demonstrate the effectiveness of the proposed algorithm. (C) 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Irrigation systems Nash optimality Large scale systems

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distributed model predictive control for Plant‐Wide Systems 1

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2015年

作者： Shaoyuan Li Yi Zheng

ISBN: (数字)9781118921579

ISBN: (纸本)9781118921562

A comprehensive examination of DMPC theory and its technological applications • A comprehensive examination of DMPC theory and its technological applications from basic through to advanced level • A systematic introduction to DMPC technology providing classic DMPC coordination strategies, analysis of their performance, and design methods for both unconstraint and constraint systems • Includes the system partition methods, coordination strategies, the performance analysis and how to design stabilized DMPC under different coordination strategies • Presents useful theories and technologies which can be used in many different industrial fields, such as the metallurgical process and high speed transport, helping readers to grasp the procedure of using the DMPC • Reflects the authors’ combined research in the area, providing a wealth of and current and background information

关键词： distributed model predictive control theory technological applications DMPC coordination strategies unconstraint and constraint systems system partition methods coordination strategies performance analysis error tolerance high flexibility explicit handling constraints good dynamic performance network connectivity error tolerance performance of entire closed-loop systems multi-inputs and multi-outputs systems plant-wide systems proposed algorithms

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