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Asynchronous distributed model predictive control for Multi-Agent Systems

IEEE ACCESS 2025年 13卷 9592-9605页

作者： Mi, Xiaoxiao Liao, Yuanjiang Zeng, Hongzheng Ningbo Univ Technol Robot Inst Ningbo 315211 Peoples R China Civil Aviat Flight Univ China Key Lab Flight Tech & Flight Safety Civil Aviat Adm China Mianyang 621052 Peoples R China

This paper primarily focuses on the asynchronous cooperation control problem of multi-agent systems with external disturbances. Here, all agents take control actions, such as sampling measurements, computing control inputs, and exchanging information based on their individual clocks rather than a global synchronized clock. Owing to the asynchronous nature, the information of each agent received by all its neighbors at the same time instant may be different and deviate from what this agent predicts, potentially resulting in poor decision-making and even instability. To ensure stability, dynamic compatibility constraints related to all the uncertain deviations are established and incorporated into the optimization problem. Additionally, the constraint restriction method is employed to satisfy physical constraints and optimize performance in the presence of external perturbations. Then, an asynchronous distributed model predictive control with a dual-mode strategy is developed and sufficient conditions on the design parameters are constructed to ensure recursive feasibility and system stability. Finally, simulation experiments on a multiple-vehicle system with asynchronous control actions are carried out to validate the theoretical results.

关键词： Multi-agent systems Optimization Formation control Vectors predictive control Clocks Trajectory Synchronization Sufficient conditions Stability criteria Asynchronous sampling cooperative control distributed model predictive control formation control multi-agent system

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Dynamic Self-Triggered Robust distributed model predictive control for Coupled Nonlinear Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Guo, Rui Feng, Jianwen Wu, Xiaoqun Wang, Jingyi Huang, Tingwen Zhu, Haibin Shenzhen Univ Sch Math Sci Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China Nipissing Univ Dept Comp Sci & Math North Bay ON P1B 8L7 Canada

This article proposes a dynamic self-triggered distributed model predictive control algorithm for coupled nonlinear systems facing external disturbances and constraints on state and input variables. A dynamic self-triggered mechanism that combines the advantages of event-triggered and self-triggered strategies is designed to simultaneously reduce the frequencies of both sampling and solving optimization problems. Particularly, the triggering threshold is adaptively adjusted using a dynamic variable, which can effectively balance control performance and computational resources. Furthermore, through the construction of a two-model optimal control problem and the analysis of input-to-state practical stability for the overall system, a single-mode distributed model predictive control framework is established for each subsystem within the proposed algorithm, which enables a fully distributed implementation. Sufficient conditions for recursive feasibility and robust stability are investigated, and conservatism is reduced by eliminating the requirement for the system state to reach the terminal region in finite time. Finally, the effectiveness of the developed algorithm is validated through two numerical examples with comparisons.

关键词： Heuristic algorithms Event detection predictive control Prediction algorithms Electronic mail Trajectory Economic indicators Couplings Computer science Circuit stability Coupled nonlinear systems distributed model predictive control dynamic self-triggered mechanism

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distributed model predictive control of Iron Precipitation Process by Goethite Based on Dual Iterative Method

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2019年第5期17卷 1233-1245页

作者： Chen, Ning Dai, Jiayang Zhou, Xiaojun Yang, Qingqing Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

Iron precipitation is a key process in zinc hydrometallurgy. The process consists of a series of continuous reactors arranged in descending order, overflowing zinc leach solution from one reactor to the next. In this paper, according to the law of mass conservation and the reaction kinetics, a continuously stirred tank reactor model of a single reactor is first established. Then, a distributed model of cascade reactors is built with coupled control based on the single reactor model, considering the unreacted oxygen in leaching solution. Secondly, four reactors in the iron precipitation process are considered as four subsystems, the optimization control problem of the process is solved by a distributed model predictive control strategy. Moreover, the control information feedback between successive subsystems is used to solve the optimization problem of each subsystem, because of the existing control coupling in their optimization objective function of pre and post subsystems. Next, considering the intractability of the optimization problem for subsystems with various constraints, a distributed dual iterative algorithm is proposed to simplify the calculation. With the consideration of its cascade structure and control couplings, the proposed algorithm iteratively solves the primal problem and the dual problem of each subsystem. The application case shows that distributed model predictive control based on dual iteration algorithm can handle coupled control effectively and reduce the oxygen consumption.

关键词： control coupling distributed model predictive control dual iterative method iron precipitation process

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distributed model predictive control for railway traffic management

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年 68卷 462-489页

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

Every day small delays occur in almost all railway networks. Such small delays are often called "disturbances" in literature. In order to deal with disturbances dispatchers reschedule and reroute trains, or break connections. We call this the railway management problem. In this paper we describe how the railway management problem can be solved using centralized model predictive control (MPC) and we propose several distributed model predictive control (DMPC) methods to solve the railway management problem for entire (national) railway networks. Furthermore, we propose an optimization method to determine a good partitioning of the network in an arbitrary number of sub-networks that is used for the DMPC methods. The DMPC methods are extensively tested in a case study using a model of the Dutch railway network and the trains of the Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods can solve the railway traffic management problem, with the same reduction in delays, much faster than the centralized MPC method. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Railway traffic management Dispatching support Mixed integer optimization distributed model predictive control Disturbance management

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distributed model predictive control based on adaptive sampling mechanism

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Chinese Journal of Chemical Engineering 2021年第11期34卷 193-204页

作者： Zhen Wang Aimin An Qianrong Li College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou 730050China National Experimental Teaching Center of Electrical and Control Engineering Lanzhou 730050China

In this work,an adaptive sampling control strategy for distributed predictive control is *** to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation ***,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period *** the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is *** an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled *** can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

关键词： Chemical process distributed model predictive control Adaptive sampling mechanism Optimal sampling interval System dynamic behavior

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distributed model predictive control of nonlinear systems subject to asynchronous and delayed measurements

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AUTOMATICA 2010年第1期46卷 52-61页

作者： Liu, Jinfeng Munoz de la Pena, David Christofides, Panagiotis D. Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Seville Dept Ingn Sistemas & Automat Seville 41092 Spain Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

In this work, we design distributed Lyapunov-based model predictive controllers for nonlinear systems that coordinate their actions and take asynchronous measurements and delays explicitly into account. Sufficient conditions under which the proposed distributed control designs guarantee that the state of the closed-loop system is ultimately bounded in a region that contains the origin are provided. The theoretical results are demonstrated through a chemical process example. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Nonlinear systems Asynchronous measurements Delayed measurements

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distributed model predictive control for reconfigurable systems based on Lyapunov analysis

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JOURNAL OF PROCESS control 2023年 123卷 1-11页

作者： Zheng, Yi Li, Shaoyuan Wan, Ruoxiao Wu, Zhe Zhang, Yueyan Shanghai Jiao Tong Univ Minist Educ China Dept Automat Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Calif Los Angeles Dept Chem & Biomol Engn Los Angeles CA 90095 USA

This paper focuses on distributed model predictive control (DMPC) for systems composed of many interacting subsystems, in the case that process topology changes, e.g., the isolation of a part of subsys-tems due to system maintenance, or processing change due to production change. The reconfiguration of the process may cause the model predictive control (MPC) optimization problem infeasible for the updated system. The proposed DMPC is based on Lyapunov techniques. After discussing the reasons causing infeasibility of the reconfigured system (violation of constraints), a transition optimization problem of each subsystem-based MPC is designed for the case that the topology change cannot be conducted immediately, which focuses on steering the states that will remain in the updated system into the region that makes the updated system feasible. Besides, to enlarge the attraction region of each subsystem-based MPC, an auxiliary variable is added to the optimization problem as a decision variable. The auxiliary variable is a steady state and the MPC lets the state track this auxiliary variable. The distance between the auxiliary variable and the set-point is also minimized in each subsystem-based optimization problem. The Lyapunov constraints which consider the auxiliary variable are designed to guarantee that the state converges to a small region around the set-point. The stability analysis and an application to a chemical process example are presented to show the effectiveness of the proposed method.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Lyapunov based control Large scale system model predictive control

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distributed model predictive control for plant-wide hot-rolled strip laminar cooling process

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JOURNAL OF PROCESS control 2009年第9期19卷 1427-1437页

作者： Zheng, Yi Li, Shaoyuan Wang, Xiaobo Shanghai Jiao Tong Univ Inst Automat Shanghai 200240 Peoples R China Baosteel Iron & Steel Ltd Co Inst Res & Dev Shanghai 201900 Peoples R China

Since hot-rolled strip laminar cooling (HSLC) process is a large-scale, nonlinear system, a distributed model predictive control (DMPC) framework is proposed for computational reason and enhancing the precision and flexibility of control system. The overall system is divided into several interconnected subsystems and each subsystem is controlled by local model predictive control (MPC). These local MPCs cooperate with its neighbours through the scheme of neighbourhood optimization for the improvement of global performance. The state space representation of each subsystem's prediction model is designed by finite volume method firstly, and then is linearized around the current operating point at each step to overcome the computational obstacle of nonlinear model. Moreover, since the strip temperature is measurable only at a few positions in water cooling section due to the difficult ambient conditions, an Extended Kalman Filter (EKF) is used to estimate the transient temperature of strip. Both simulation and experiment results prove the efficiency of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Laminar cooling Hot-rolled strip

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distributed model predictive control for Linear Constrained Systems Based on Time-Varying Terminal Sets

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IEEE ACCESS 2021年 9卷 119675-119683页

作者： Zhu, Jialin Xue, Binqiang Yu, Haisheng Qingdao Univ Coll Automat Qingdao 266071 Peoples R China Qingdao Univ Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

A novel distributed model predictive control (DMPC) strategy with time-varying terminal set for linear constrained systems is presented in this paper. To decrease the load of computation of DMPC while ensuring the global optimization, the nominal system is introduced by treating the influence of neighboring subsystems as a bounded disturbance. Then, under the distributed control structure, a distributed predictive control optimization problem containing the nominal state and input can be designed for each subsystem. Furthermore, different from most DMPC approaches, a novel approach to design a terminal constraint set that can be updated in every update time based on the predicted state of the system is proposed. Additionally, the analysis of feasibility and the stability of the proposed DMPC algorithm are described under kinds of the system constraints. Finally, experimental simulation is shown to prove validity by the control scheme in this paper.

关键词： predictive control Optimization predictive models Couplings Mathematical model Time-varying systems System performance distributed model predictive control time-varying terminal constraint set linear constrained 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： Optimisation techniques distributed MPC consensus strategy predictive control state exchange Aerospace control double-integrator dynamics control system synthesis control system analysis and synthesis methods multi-robot systems distributed control reverse iterative algorithm sampling period Interpolation and function approximation (numerical analysis) sampled-data double-integrator dynamics Optimal control Discrete control systems sampled-data multiagent system consensus iterative methods autonomous aerial vehicles mobile robots Telerobotics optimisation receding horizon optimisation distributed model predictive control sampled data systems Mobile robots

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