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

IFAC-PapersOnLine 2015年第8期48卷 551-555页

作者： Rongchao Zhang Andong Liu Li Yu Wenan Zhang Department of Automation Zhejiang University of Technology Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou 310023 P.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.

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

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distributed model predictive control for polytopic uncertain systems with randomly occurring actuator saturation and packet loss

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IET control THEORY AND APPLICATIONS 2014年第5期8卷 297-310页

作者： Song, Yan Fang, Xiaosheng Shanghai Univ Sci & Technol Dept Control Sci & Engn Key Lab Modern Opt Syst Shanghai 200093 Peoples R China Zhejiang Normal Univ Coll Math Phys & Informat Technol Jinhua 321004 Zhejiang Peoples R China

In this study, a distributed model predictive control algorithm is presented for the polytopic uncertain system subject to randomly occurring actuator saturation and packet loss. The global system is decomposed into several subsystems, and a novel distributed controller model is established to account for both the actuator saturation and packet loss in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. By transforming the non-linear feedback law into a convex hull of linear feedback laws, the distributed controllers for subsystems are obtained by solving a linear matrix inequality (LMI) optimisation problem. Finally, two simulation examples are employed to show the effectiveness of the techniques proposed in this study.

关键词： distributed control feedback linear matrix inequalities linear systems nonlinear control systems optimisation predictive control uncertain systems distributed model predictive control polytopic uncertain systems randomly occurring actuator saturation packet loss distributed controller model Bernoulli distributed white sequences conditional probabilities nonlinear feedback law convex hull linear feedback laws LMI optimisation problem

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A Computationally Efficient Non-Cooperative distributed model predictive control Algorithm for Two Agent Systems 20

A Computationally Efficient Non-Cooperative Distributed Mode...

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20th International Conference on System controls and Computer Science 2015

作者： Maxim, Anca Lazar, Corneliu Caruntu, Constantin F. Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781479917808

This paper presents a non -cooperative distributed model predictive control algorithm used in cyber-physical systems for two agent systems coupled through the inputs. Each agent computes the optimal input trajectory for its corresponding subsystem as the minimum of a local optimization problem. The input trajectory of the neighbor, which is used in the local optimization problem, is obtained based on the previous optimal control sequence and is received in a communication session. After that, the optimization problem is solved and the optimal input is sent to the process. This approach is computationally efficient because the communication between the two agents is reduced at minimum (only one session each sampling period) and the optimal input is obtained solving one optimization problem thus diminishing the overall computational time. The algorithm was implemented in Matlab and the obtained performances were compared with a cooperative distributed model predictive control strategy. Both methods were tested in simulation on a quadruple tanks process and the results recommend the noncooperative strategy which has similar results with less computational requirements.

关键词： distributed model predictive control non-cooperative and cooperative games two agents control system

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distributed model predictive control for Rescheduling of Railway Traffic 17

Distributed Model Predictive Control for Rescheduling of Rai...

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IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)

作者： Kersbergen, Bart van den Boom, Ton De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control NL-2600 AA Delft Netherlands

ISBN: (纸本)9781479960781

In this paper we introduce two distributed model predictive control (DMPC) methods for the rescheduling of railway traffic. In each step of the DMPC approach dispatching actions are determined that reduce the amount of delay in the network as much as possible by solving a mixed integer linear programming (MILP) problem. The constraints of the MILP are based on a model of the railway traffic and network and the possible dispatching actions. In the first method each subproblem consists of the complete constraint matrix and the solver tries to minimize the centralized cost function, but can only change a limited number of binary variables (which correspond to the dispatching actions). By limiting the number of binary variables each subproblem is easier to solve than the centralized problem. For the second method each subproblem consists of only a part of the problem and the solver minimizes a local cost function, and it can only change the binary variables for that part of the problem. This reduces the complexity of the subproblems even further, but the solver can not determine the effects of the binary variables on the solution quality of the other subproblems. Both methods significantly reduce the time needed to determine the dispatching actions. The average time needed to compute the solution is 11.56 times shorter when using method 1 and 39.11 times shorter when using method 2. The solution found is on average only 0.6 3 % less optimal for method 1 and 1.27 % less optimal for method 2.

关键词： distributed control integer programming linear programming matrix algebra predictive control rail traffic control DMPC approach binary variables centralized cost function constraint matrix dispatching actions distributed model predictive control mixed integer linear programming railway traffic rescheduling Cost function Delays Dispatching predictive control predictive models Rail transportation Vectors

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distributed model predictive Load Frequency control for Virtual Power Plants with Novel Event-Based Low-Delay Technique Under Cloud-Edge-Terminal Framework

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ENERGIES 2025年第6期18卷 1380-1380页

作者： Kang, Kai Shi, Nian Cai, Si Zhang, Liang Shao, Xinan Cao, Haohao Fei, Mingjin Zhou, Shisen Wan, Xiongbo PowerChina Hubei Engn Co Ltd Wuhan 430040 Peoples R China PowerChina Hubei Elect Engn Co Ltd Wuhan 430040 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

In this paper, the distributed model predictive load frequency control problem for virtual power plants (VPPs) under the cloud-edge-terminal framework is addressed, where the data packets are transmitted under a novel dynamic event-triggered mechanism (DETM) with hybrid variables. The proposed DETM has the ability to flexibly manage packet releases and reduce network congestion, thus decreasing the communication delay of the VPP. A method of the DETM-based distributed model predictive control (DMPC) is proposed, which can shorten the data processing time and further decrease the communication delay. The DMPC problem is described as a "min-max" optimization problem (OP) with hard constraints on the system state. By utilizing a Lyapunov function with an internal dynamic variable, an auxiliary OP with matrix inequalities constraints is proposed to optimize the controller gain and the weighting matrix of the DETM. The effectiveness and superiority of the designed DETM and dynamic event-based DMPC algorithm are demonstrated through a case study on two-area VPPs.

关键词： load frequency control dynamic event-triggered mechanism distributed model predictive control cloud-edge-terminal virtual power plant

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Autonomous Vehicle Platoons In Urban Road Networks:A Joint distributed Reinforcement Learning and model predictive control Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 141-156页

作者： Luigi D’Alfonso Francesco Giannini Giuseppe Franzè Giuseppe Fedele Francesco Pupo Giancarlo Fortino IEEE the Department of Computer Engineering ModelingElectronics and SystemsUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy the Department of Mechanical Engineering Energy Engineering and ManagementUniversitàdella CalabriaVia Pietro BucciCubo 42-CRende(CS)87036Italy

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally 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: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the a

关键词： distributed model predictive control distributed reinforcement learning routing decisions urban road networks

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distributed model predictive control: An overview of features and research opportunities

Distributed Model Predictive Control: An overview of feature...

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IEEE 11th International Conference on Networking, Sensing and control (ICNSC)

作者： Negenborn, R. R. Maestre, J. M. Delft Univ Technol Dept Marine & Transport Technol Delft Netherlands Univ Seville Dept Sistemas Automat Seville Spain

ISBN: (纸本)9781479931064

This is a position paper on the current state of the art in distributed model predictive control (M PC) and our view on its future potential. We present results from a recent survey of 35 distributed MPC approaches. For this, we propose a way in which distributed MPC approaches can be categorized for comparison. We also link the potential that these approaches have to the domain of other fields such as integrated environmental optimal decision making and cyber-physical systems. Many challenges for realizing these links have to be faced. We present our views on how the advances in several highly active research domains could be used to overcome these challenges. As such, this paper is intended as a starting point for the exploration of various novel research cooperations.

关键词： distributed model predictive control emerging research areas algorithm categorization

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distributed model predictive control of Wind and Solar Generation System 33

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

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33rd Chinese control Conference (CCC)

作者： Jia, Yubin Liu, X. J. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

ISBN: (纸本)9789881563842

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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Resilient model predictive control of distributed Systems Under Attack Using Local Attack Identification

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SN Computer Science 2024年第1期5卷 101页

作者： Braun, Sarah Albrecht, Sebastian Lucia, Sergio Siemens AG Otto-Hahn-Ring 6 Munich 81739 Germany TU Dortmund University August-Schmidt-Straße Dortmund 44227 Germany

With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the threat of deliberate malicious attacks, which may become more prevalent with a growing number of distributed generation units. Also in other safety-critical technology sectors, control systems are becoming more and more decentralized, causing the targets for attackers and thus the risk of attacks to increase. It is thus essential that distributed controllers are robust toward these uncertainties and able to react quickly to disturbances of any kind. To this end, we present novel methods for model-based identification of attacks and combine them with distributed model predictive control to obtain a resilient framework for adaptively robust control. The methodology is specially designed for distributed setups with limited local information due to privacy and security reasons. To demonstrate the efficiency of the method, we introduce a mathematical model for physically coupled microgrids under the uncertain influence of renewable generation and adversarial attacks, and perform numerical experiments, applying the proposed method for microgrid control. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Attack identification distributed model predictive control Microgrids under attack Robust nonlinear control

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A stabilizing cooperative-distributed gradient-based economic model predictive control strategy for constrained linear systems

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JOURNAL OF PROCESS control 2022年 112卷 36-48页

作者： Santana, Daniel D. Odloak, Darci Santos, Tito L. M. Martins, Marcio A. F. Univ Fed Bahia Dept Engn Quim Escola Politecn Rua Aristides Novis 2 BR-40210630 Salvador BA Brazil Univ Fed Bahia Dept Engn Elect & Comp Escola Politecn Rua Aristides Novis 2 BR-40210630 Salvador BA Brazil Univ Sao Paulo Dept Engn Quim Escola Politecn Av Prof Luciano GualbertoTrv 3 380 BR-61548 Sao Paulo SP Brazil

This paper proposes a stabilizing distributed gradient-based economic MPC strategy for cooperative control of constrained linear systems. This strategy is based on quadratic programming to limit the computational cost, and one key feature is the evaluation of an economically optimal steady-state directly in the control law, leading the closed-loop system towards it. The control law includes three ingredients to enlarge its domain of attraction and potentially avoid feasibility problems, namely: artificial steady-state, slack variables, and terminal equality constraints solely on non-stable states. A four-tank system is used to address the properties of the formulation, especially its stability in the nominal case, the capacity to track the desired target for a nonlinear performance economic function, and tackle unmeasured and persistent unmeasured disturbances.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Economic model predictive control Optimizing control Target calculation Cooperative optimization

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