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

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 nonholonomic multivehicle formation tracking

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第18期31卷 8961-8973页

作者： Liu, Kun Zhang, Qirui Han, Dongyu Ma, Aoyun Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

A distributed model predictive control strategy is proposed for the formation tracking problem of nonholonomic multivehicle systems, where each vehicle is modeled as a unicycle and is affected by disturbances. We design the optimization problems that followers need to solve, and the cooperation among the followers are achieved by the coupling costs between the followers. Meanwhile, the tightened constraints are used to ensure the actual tracking errors satisfy the constraints. The recursive feasibility of the optimization problems and the closed-loop stability of the tracking error systems are established. Finally, the effectiveness of the proposed strategy is verified via an example which contains three followers.

关键词： distributed model predictive control formation tracking nonholonomic multivehicle systems unicycle model

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distributed model predictive control-oriented network decomposition based on full dynamic response

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AICHE JOURNAL 2023年第1期69卷 e17951-e17951页

作者： Wang, Jiaorao Song, Chunyue Zhao, Jun Mo, Ziyan Xu, Zuhua Zhejiang Univ Inst Ind Intelligence & Syst Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

This article addresses network decomposition for distributed model predictive control (DMPC), which includes two improvements. First, in the weighted input-output bipartite graph construction of a process network, a new measure called frequency affinity is proposed to characterize the input-output interaction considering the full dynamic response and structural information of a process. Then, in community detection, which is used to decompose the process network, the gap metric is added to quantify stability and the loss of control performance of each subsystem. Through the proposed decomposition, the obtained subsystems can be dynamically well-decoupled since both transient and steady-state responses are measured by the frequency affinity. As structural information is considered, the decomposition is consistent with the process physical topology. Furthermore, the utilization of gap metric can facilitate controller design for DMPC. Case studies on a reactor separator process and an air separation process demonstrate the effectiveness of the proposed decomposition method.

关键词： community detection distributed model predictive control input-output interactions network decomposition

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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 asynchronous multiagent systems with self-triggered coordinator

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ASIAN JOURNAL OF control 2024年第0期

作者： Chen, Qianqian Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Shanghai Peoples R China

This paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self-triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade-off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed-loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

关键词： asynchronous multiagent systems distributed model predictive control nonlinear predictive control self-triggered control

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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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distributed model predictive control for reconfigurable systems based on Lyapunov analysis

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JOURNAL OF PROCESS control 2023年第1期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 Virtually Coupled Heterogeneous Trains: Comparison and Assessment

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 20753-20766页

作者： Liu, Xiaoyu Dabiri, Azita Wang, Yihui Xun, Jing De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China

Virtual coupling is regarded as an efficient way to improve the line capacity of rail transportation systems by reducing the spacing between consecutive trains. This paper is the first to compare and assess different distributed model predictive control (MPC) approaches, i.e., cooperative distributed MPC, serial distributed MPC, and decentralized MPC, for virtually coupled trains with a nonlinear train dynamic model. To make a balanced trade-off between computational complexity and efficiency, we also propose and assess convex approximations of the above control approaches. Furthermore, we are the first to introduce the relaxed dynamic programming approach to analyze the stability of the MPC-based nonlinear train control problem. By using the relaxed dynamic programming approach, a distributed stopping criterion with a stability guarantee is developed for the cooperative distributed MPC approach. In real life, masses of trains are different and can change at stations due to changes in passenger loads. This change in mass can significantly affect the dynamics and control of the virtually coupled trains when not taken into account in the control design. Therefore, we explicitly consider heterogeneous train masses when designing MPC approaches. We evaluate the different distributed MPC approaches through case studies based on the data of the Beijing Yizhuang Line. Simulation results indicate that the cooperative distributed MPC approach has the best tracking performance, while the serial distributed MPC approach can reduce communication requirements and computation capabilities with sacrifices of tracking performance.

关键词： Virtual coupling train speed control distributed model predictive control heterogeneous train masses relaxed dynamic programming

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distributed model predictive control for load frequency control with dynamic fuzzy valve position modelling for hydro-thermal power system

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IET control THEORY AND APPLICATIONS 2016年第14期10卷 1653-1664页

作者： Liu, Xiangjie Kong, Xiaobing Lee, Kwang Y. North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Baylor Univ Dept Elect & Comp Engn Waco TX 76798 USA

Reliable load frequency control (LFC) is very important for a modern power system with multi-source power generation and has been the primary focus of studies on advanced control theory and applications. In the LFC of a power system, the generation rate constraints (GRC) and position limit of the governor valve present major challenges to the control scheme because they significantly affect the dynamic responses of the system, resulting in larger overshoot and longer settling time. model predictive control (MPC) is an attractive control strategy that systematically considers the constraints on the process inputs, states, and outputs. It is employed in LFC to cope with the GRC problem. This study proposes a distributed MPC (DMPC) for a four-area hydro-thermal interconnected power system. In the proposed scheme, the limit position of the governor valve is modelled by a fuzzy model and the local predictive controllers are incorporated into the non-linear control system. The effectiveness of the proposed non-linear constraint DMPC was demonstrated by simulations.

关键词： hydrothermal power systems power generation control predictive control distributed control load regulation frequency control valves fuzzy control position control power system interconnection fuzzy set theory distributed model predictive control dynamic fuzzy valve position modelling reliable load frequency control LFC power system multisource power generation advanced control theory generation rate constraints governor valve position limit DMPC four-area hydro-thermal interconnected power system local predictive controllers nonlinear control 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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