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ROBUST distributed model predictive control: A REVIEW AND RECENT DEVELOPMENTS

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2011年第5期89卷 1176-1190页

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

This study presents a review of distributed model predictive control (DMPC) strategies followed by recent studies conducted by the authors on the robustness of these strategies to model errors and a summary of future challenges in this area. The review identifies three key challenges for the successful application of DMPC: (i) the selection of optimal control structure for DMPC;(ii) the choice of a suitable coordination strategy among the controllers;and (iii) the robustness of DMPC strategies to model errors. Then, the study summarises recent developments related to the robustness of unconstrained and constrained DMPC algorithms. For the unconstrained case, a methodology that is based on the calculation of a performance index is proposed to balance the trade-off between performance and structure simplicity in the presence of model errors. For the constrained case, a Jacobi iterative-based method is used to design a robust DMPC algorithm. The proposed techniques are illustrated through case studies involving a high purity binary distillation problem.

关键词： distributed model predictive control robust control linear matrix inequalities

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A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

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control ENGINEERING PRACTICE 2013年第11期21卷 1594-1605页

作者： Minh Dang Doan Giselsson, Pontus Keviczky, Tamas De Schutter, Bart Rantzer, Anders Cantho Univ Technol Fac Elect Elect & Telecommun Engn Ho Chi Minh City Vietnam Lund Univ Dept Automat Control S-22100 Lund Sweden Delft Univ Technol Delft Ctr Syst & Control NL-2600 AA Delft Netherlands

A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k(2)), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Hydro power control distributed optimization Accelerated gradient algorithm model predictive control distributed model predictive control

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Event-triggered asynchronous distributed model predictive control with variable prediction horizon for nonlinear systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第6期33卷 3764-3789页

作者： Wang, Pengbiao Ren, Xuemei Zheng, Dongdong Beijing Inst Technol Sch Automat Beijing Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

In this article, we develop an event-triggered asynchronous distributed model predictive control (ETADMPC) algorithm with the adaptive prediction horizon for distributed nonlinear systems with weakly dynamics couplings, bounded disturbances, and system constraints. First, we focus on designing a novel adaptive event-triggered mechanism with Zeno-free phenomenons in order to reduce computational burdens, whose triggering threshold can adapt to the real-time changes of the system and make necessary adjustments. Then, a robust time-varying tightened state constraint is tailored for the optimization problem with respect to distributed model predictive control, and it can provide robustness to external disturbances and system coupling parts. And an adaptive prediction horizon update scheme is deliberately designed to decrease the length of the prediction horizon when the system state is close to the terminal set, reducing the computational complexity in the optimization problem. Furthermore, we strictly prove that under the given sufficient conditions, the proposed ETADMPC algorithm is recursively feasible and the closed-loop system is stable. Finally, a numerical example is provided to show that our scheme can achieve satisfactory control performances with less calculation and a shorter calculation time than the existing results.

关键词： adaptive event-triggered mechanism adaptive prediction horizon update scheme distributed model predictive control distributed nonlinear systems

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Dynamic consensus of linear multi-agent system using self-triggered distributed model predictive control

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ISA TRANSACTIONS 2023年 142卷 177-187页

作者： Resmi, R. Mija, S. J. Jacob, Jeevamma TKM Coll Engn Kollam 691005 India Natl Inst Technol Calicut Elect Engn Dept Kozhikode 673601 India

This article discusses self-triggering algorithm using distributed model predictive control (DMPC) to achieve dynamic consensus in linear multi-agent systems (MASs). The iterative computations and communications required at each time step in traditional consensus algorithms cause escalation of the energy consumption and shorten the life span of the MAS. An attempt to solve this problem is made by proposing a sequential self-triggering consensus algorithm, where each agent computes its own triggering instants. A Laguerre based DMPC design is adopted that notably reduces the computational complexity of conventional DMPC. The proposed self-triggered DMPC algorithm optimizes the control input and triggering interval while guaranteeing the dynamic consensus of the agents. By virtue of the Laguerre function based control architecture, the additional computations owing to the self-triggered algorithm do not impose stress on the controller;yet reduce the load on communication resources. The equality constraint on the terminal state of the agents is utilized along with Lyapunov criteria to establish the closed loop stability of the MAS. The proposed scheme achieves a considerable drop in controller design computations as well as data transmissions among agents, and the same is established by comparing these traits of existing schemes while achieving comparable performance. The proposed algorithm is verified through simulation of platoon configuration of vehicles, each of which is modeled as a linear multi-input multi-output (MIMO) system.(c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent system Dynamic consensus distributed model predictive control Self-triggered control

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Cooperative distributed model predictive control for wind farms

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OPTIMAL control APPLICATIONS & METHODS 2015年第3期36卷 333-352页

作者： Spudic, V. Conte, C. Baotic, M. Morari, M. Univ Zagreb Fac Elect Engn & Comp HR-10000 Zagreb Croatia ETH Automat Control Lab CH-8092 Zurich Switzerland

This paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm-wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite-horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm-wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： wind energy distributed model predictive control distributed optimization

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Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems

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JOURNAL OF PROCESS control 2011年第9期21卷 1231-1242页

作者： Heidarinejad, Mohsen Liu, Jinfeng Munoz de la Pena, David Davis, James F. 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 Univ Seville Dept Ingn Sistemas & Automat Seville 41092 Spain

In this work, we consider the design of a network-based distributed model predictive control system using multirate sampling for large-scale nonlinear uncertain systems composed of several coupled subsystems. Specifically, we assume that the states of each local subsystem can be divided into fast sampled states (which are available every sampling time) and slowly sampled states (which are available every several sampling times). The distributed model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion at time instants in which full system state measurements (both fast and slow) are available, to guarantee closed-loop stability. When local subsystem fast sampled state information is only available, the distributed controllers operate in a decentralized fashion to improve closed-loop performance. In the proposed control architecture, the controllers are designed via Lyapunov-based model predictive control techniques taking into account bounded measurement noise, process disturbances and communication noise. Sufficient conditions under which the state of the closed-loop system is ultimately bounded in an invariant region containing the origin are derived. The theoretical results are demonstrated through a nonlinear chemical process example. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Nonlinear systems Multirate sampling Measurement noise Communication network noise Chemical process control

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A distributed model predictive control Strategy for Constrained Multi-Vehicle Systems Moving in Unknown Environments

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2021年第2期6卷 343-352页

作者： Franze, Giuseppe Lucia, Walter Venturino, Antonello Univ Calabria DIMES Via Pietro BucciCubo 42-C I-87036 Arcavacata Di Rende CS Italy Concordia Univ CIISE Montreal PQ H3G 1M8 Canada Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay ONERA Traitement Informat & Syst F-91123 Palaiseau France

In this paper, we propose a distributed model predictive control strategy for a team of vehicles moving in an unknown obstacle scenario. In particular, we combine receding horizon control arguments with leader-follower formations in order to design a flexible architecture where the vehicle topology, if needed, can be rearranged to better cope with the presence of obstacles. Each vehicle is equipped with an ad-hoc dual-mode controller in charge of tracking along the leader-follower chain the predecessor vehicle (namely the target for the leader) and ensuring absence of collisions. Moreover, whenever the leader recognizes that the obstacle scenario obstructs its navigation, the current formation is properly reconfigured by preserving recursive feasibility and obstacle avoidance properties. Final numerical results on a team of seven point mobile robots are instrumental to show the main features of the proposed distributed model predictive control algorithm.

关键词： Collision avoidance Topology Robot kinematics predictive control Task analysis Intelligent vehicles Collision avoidance distributed model predictive control leader-follower formations multi-vehicle systems unmanned autonomous vehicles

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Enhanced information reconfiguration for distributed model predictive control for cyber-physical networked systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2020年第1期30卷 198-221页

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

This paper considers a class of cyber-physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed-loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed-loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed-loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.

关键词： coupling systems cyber-physical networked systems distributed model predictive control model predictive control

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distributed model predictive control of Nonlinear Process Systems Subject to Asynchronous Measurements

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IFAC Proceedings Volumes 2009年第11期42卷 147-152页

作者： Jinfeng Liu David Muñoz de la Peña Panagiotis D. Christofides Department of Chemical and Biomolecular Engineering University of California Los Angeles CA 90095-1592 USA Departamento de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos S/N 41092 Sevilla Spain Department of Electrical Engineering University of California Los Angeles CA 90095-1592 USA

Abstract In this work, we address distributed model predictive control of nonlinear process systems subject to asynchronous measurements. Assuming that there exists an upper bound on the interval between two successive measurements of the process state, two separate Lyapunovbased model predictive controllers that coordinate their actions and take asynchronous measurements explicitly into account are designed. The proposed distributed control design only requires one directional communication between the two distributed controllers and provides the potential of maintaining stability and performance in the face of new or failing actuators. The results are illustrated through a chemical process example.

关键词： distributed model predictive control Nonlinear systems Networked control systems Process control Asynchronous measurements

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Real-time active power dispatch of virtual power plant based on distributed model predictive control

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ELECTRONICS LETTERS 2022年第23期58卷 872-875页

作者： Feng, Shuai Funding, Dongsheng Yang Zhou, Bowen Luo, Yanhong Li, Guangdi Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

With the increasing penetration of renewable energy, virtual power plants (VPP) reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in VPP has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control (DMPC), so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbour information. Simulation results show the feasibility and effectiveness of the proposed method.

关键词： scheduling multiagent system power grid power generation dispatch optimization function multi-agent systems optimisation power plants virtual power plant neighbour information distributed control real-time active power dispatch scheme real-time optimal scheduling problem power engineering computing power grids predictive control renewable energy distributed model predictive control

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