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distributed model predictive control for nonholonomic multivehicle formation tracking

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

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 of fan coil system

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JOURNAL OF BUILDING ENGINEERING 2024年 94卷

作者： Zhao, Anjun Wei, Yuan Quan, Wei Xi, Jiangtao Dong, Feifei Xian Univ Architecture & Technol Sch Bldg Serv Sci & Engn Xian 710055 Peoples R China China Northwest Architecture Design & Res Instite Xian 744000 Peoples R China

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae SearchParticle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

关键词： distributed model predictive control Nash optimization Beetle antennae search -particle swarm optimi zation (BAS-PSO)

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distributed model predictive control with hierarchical architecture for communication: application in automated irrigation channels

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INTERNATIONAL JOURNAL OF control 2016年第8期89卷 1725-1741页

作者： Farhadi, Alireza Khodabandehlou, Ali Sharif Univ Technol Dept Elect Engn Tehran Iran

This paper is concerned with a distributed model predictive control (DMPC) method that is based on a distributed optimisation method with two-level architecture for communication. Feasibility (constraints satisfaction by the approximated solution), convergence and optimality of this distributed optimisation method are mathematically proved. For an automated irrigation channel, the satisfactory performance of the proposed DMPC method in attenuation of the undesired upstream transient error propagation and amplification phenomenon is illustrated and compared with the performance of another DMPC method that exploits a single-level architecture for communication. It is illustrated that the DMPC that exploits a two-level architecture for communication has a better performance by better managing communication overhead.

关键词： distributed model predictive control large-scale linear systems networked control system optimisation

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distributed model predictive control of buildings and energy hubs

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ENERGY AND BUILDINGS 2022年 259卷

作者： Lefebure, Nicolas Khosravi, Mohammad Hudoba de Badyn, Mathias Buenning, Felix Lygeros, John Jones, Colin Smith, Roy S. Ecole Polytech Fed Lausanne EPFL Automat Control Lab Dept Mech Engn Lausanne Switzerland Swiss Fed Inst Technol Automat Control Lab Dept Elect Engn & Informat Technol Zurich Switzerland Empa Urban Energy Syst Lab Swiss Fed Labs Mat Sci & Technol Dubendorf Switzerland

model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact that energy demands are often assumed to be fixed quantities rather than controlled dynamic variables. The joint optimization of energy hubs and buildings' energy management systems can result in higher energy savings. This paper investigates how different MPC strategies perform on energy management systems in buildings and energy hubs. We first discuss two MPC approaches;centralized and decentralized. While the centralized control strategy offers optimal performance, its implementation is computationally prohibitive and raises privacy concerns. On the other hand, the decentralized control approach, which offers ease of implementation, displays significantly lower performance. We propose a third strategy, distributed control based on dual decomposition, which has the advantages of both approaches. Numerical case studies and comparisons demonstrate that the performance of distributed control is close to the performance of the centralized case, while maintaining a significantly lower computational burden, especially in large-scale scenarios with many agents. Finally, we validate and verify the reliability of the proposed method through an experiment on a full-scale energy hub system in the NEST demonstrator in Dubendorf, Switzerland. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： distributed model predictive control Energy hubs Buildings

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Cooperative distributed model predictive control for robot in-hand manipulation

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2023年第1期43卷 65-74页

作者： Chen, Yang Luo, Yu Sun, Fuchun Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

Purpose This study aims to process multi-agent system with kinds of limitations and constraints, and consider the robot in-hand manipulation as a problem of coordination and cooperation of multi-fingered hand. Design/methodology/approach A cooperative distributed model predictive control (MPC) algorithm is proposed to perform robot in-hand manipulation. Findings A cooperative distributed MPC approach is formulated for robot in-hand manipulation problem, which enables address complex limitation and constraint conditions in object motion planning, and realizes tracking trajectory of the object more than tracking position of the object. Originality/value This method to implement the moving object task uses the kinematic parameters without the knowledge of dynamic properties of the object. The cooperative distributed MPC scheme is designed to guarantee the movement of the object to a desired position and trajectory at algorithmic level.

关键词： distributed model predictive control Robot in-hand manipulation Position and trajectory tracking control

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STdi4DMPC: distributed model predictive control for Connected and Automated Truck Platoon With Mixed Traffic Flow Based on Spatiotemporal Trajectory Prediction

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第10期73卷 14563-14579页

作者： Li, Liyou Lyu, Hao Wang, Ting Cheng, Rongjun Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Tongji Univ Coll Transportat Engn Shanghai 201804 Peoples R China

Trucking transportation has brought great convenience to social life, but it also faces many challenges. The rapid development of autonomous driving technology has the potential to alleviate some bottleneck issues and lead to new changes in truck transportation. Ensuring the high-quality operation of connected and automated trucks (CAT) and connected and automated trucks platoon (CATP) in mixed traffic flow has sparked a wave of research. With the goal of achieving efficient and stable longitudinal control of CATP in mixed traffic flow, this paper develops a novel data-driven longitudinal control framework for CATP based on trajectory prediction (STdi4DMPC). Firstly, a spatiotemporal dynamic attention model for trajectory prediction using a Transformer style architecture that integrates driving intentions (STdi) is constructed, capturing trajectory features of different dimensions and dependencies at different scales. Additionally, processing trajectories using empirical mode decomposition and wavelet denoising. Furthermore, a CATP longitudinal controller that conforms to the truck dynamics constraints based on distributed model predicted control (DMPC) is developed. Finally, the reconstituted noise-reduced trajectory of human-driven vehicle (HDV) is employed as a reference trajectory for DMPC, thereby achieving the integration of trajectory prediction model and longitudinal control algorithms. The simulation results based on the CitySim dataset are conducted and results demonstrate the superiority of the proposed longitudinal control strategy.

关键词： Trajectory predictive models Transformers Safety Long short term memory Spatiotemporal phenomena Prediction algorithms Connected automated truck platoon deep learning trajectory prediction longitudinal control distributed model predictive control

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Enhancing cooperative distributed model predictive control for the water distribution networks pressure optimization

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JOURNAL OF PROCESS control 2019年 84卷 70-88页

作者： Zhang, Yuan Zheng, Yi Li, Shaoyuan 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 water distribution networks (WDNs), the supply pressure to users should be homogeneous and close to the lower bound so as to reduce leakages and energy consumption while meeting users' water demand. The large-scale features and incomplete structure information of modern WDNs increase the difficulty in modeling and pressure optimization. In this paper, an enhancing cooperative distributed model predictive control (EC-DMPC) strategy is proposed to solve this problem with enhanced operating performance. Firstly, an efficient modeling method and a partitioning method based on operation data are proposed to decompose the WDNs and construct the subsystem models. These methods are based on k-Shape clustering and canonical correlation analysis with considering the mechanism of WDNs. In order to increase the flexibility of coordination while guaranteeing the global optimization performance, a neighbourhood optimization coordination strategy is adopted, where each subsystem considers the performance of itself and that of strongly affected subsystems. Moreover, a robust invariant set and a robust invariant set control law are utilized to handle the disturbance caused by the ignored weak couplings. Part of Shanghai's WDNs is used as the study case to demonstrate the application of the proposed methods. A comparative experiment study is developed. Results have shown the efficiency of the proposed methods. By the coordination of the EC-DMPC strategy, when the water demand of users changes, the users' pressure can be maintained homogeneous, and the average pressure decreases, which is beneficial in the reduction of leakages and energy consumption. The distributed strategy could also improve the flexibility and scalability for large-scale WDNs. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Large-scale water distribution networks Pressure optimization modeling and partitioning

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Assessment and comparison of distributed model predictive control schemes: Application to a natural gas refrigeration plant

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COMPUTERS & CHEMICAL ENGINEERING 2016年 89卷 192-203页

作者： Farina, Marcello Ferrari, Giacomo P. Manenti, Flavio Pizzi, Emiliano Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo da Vinci 32 I-20133 Milan Italy Politecn Milan Dipartimento Chim Mat & Ingn Chim Giulio Natta Piazza Leonardo da Vinci 32 I-20133 Milan Italy

A number of decentralized and distributed control schemes based on model predictive control (MPC) have been introduced in the last years. They have been proposed as viable solutions to the computational, transmission and robustness issues arising in the centralized context in case of large-scale and/or distributed plants. Such MPC-based control schemes are very heterogeneous, based on different model structures and realizations, with different features and infrastructural/memory/computational requirements. In this paper, we test and compare, with a realistic case study, a robust non-cooperative scheme and a cooperative iterative one. The main scope is to analyze and unravel, in a fair comparison scenario, these methods from different viewpoints, spanning from the model realization issues to the communication and computational requirements, to the control performances. The benchmark case study consists of an existing natural gas refrigeration plant. Realistic simulations and validation tests are obtained through in the DynSim industrial process simulation environment. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Gas refrigeration plant control of chemical processes Dynamic simulation

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distributed model predictive control Based on Multi-agent model for Electric Multiple Units

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Acta Automatica Sinica 2014年第11期40卷 2625-2631页

作者： Zhong-Qi LI Hui YANG Kun-Peng ZHANG Ya-Ting FU School of Mechanical and Electrical Engineer Nanchang University Nanchang 330031 China School of Electrical and Electronic Engineering East China Jiaotong University Nanchang 330013 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China

The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.

关键词： Electric multiple units (EMUs) multi-agent nonlinear multi-models distributed model predictive control synchronous tracking

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