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distributed model predictive control approach for cooperative car-following with guaranteed local and string stability

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年第Oct.期128卷 69-86页

作者： Zhou, Yang Wang, Meng Ahn, Soyoung Univ Wisconsin Dept Civil & Environm Engn 1415 Engn Dr Madison WI 53706 USA Delft Univ Technol Fac Civil & Geospatial Engn Stevinweg 1 NL-2628 CN Delft Netherlands Univ Wisconsin Civil & Environm Engn 1415 Engn Dr Madison WI 53706 USA

In this paper, a serial distributed model predictive control (MPC) approach for connected automated vehicles (CAVS) is developed with local stability (disturbance dissipation over time) and multi-criteria string stability (disturbance attenuation through a vehicular string). Two string stability criteria are considered within the proposed MPC: (i) the l(infinity)-norm string stability criterion for attenuation of the maximum disturbance magnitude and (ii) l(2)-norm string stability criterion for attenuation of disturbance energy. The l(infinity)-norm string stability is achieved by formulating constraints within the MPC based on the future states of the leading CAV, and the l(2)-norm string stability is achieved by proper weight matrix tuning over a robust positive invariant set. For rigor, mathematical proofs for asymptotical local stability and multi-criteria string stability are provided. Simulation experiments verify that the distributed serial MPC proposed in this study is effective for disturbance attenuation and performs better than traditional MPC without stability guarantee. Published by Elsevier Ltd.

关键词： Connected automated vehicles Longitudinal control distributed model predictive control Local stability l(infinity)-norm string stability l(2)-norm string stability

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distributed model predictive control for continuous-time nonlinear systems based on suboptimal ADMM

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OPTIMAL control APPLICATIONS & METHODS 2019年第1期40卷 1-23页

作者： Bestler, Anja Graichen, Knut Ulm Univ Inst Measurement Control & Microtechnol D-89081 Ulm Germany

This paper presents a distributed model predictive control (DMPC) scheme for continuous-time nonlinear systems based on the alternating direction method of multipliers (ADMM). A stopping criterion in the ADMM algorithm limits the iterations and therefore the required communication effort during the DMPC solution at the expense of a suboptimal solution. Stability results are presented for the suboptimal DMPC scheme under two different ADMM convergence assumptions. In particular, it is shown that the required iterations in each ADMM step are bounded, which is also confirmed in simulation studies.

关键词： alternating direction multiplier method distributed model predictive control networked systems nonlinear systems stability suboptimality

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Coalitional distributed model predictive control Strategy for Vehicle Platooning Applications

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SENSORS 2022年第3期22卷 997-997页

作者： Maxim, Anca Caruntu, Constantin-Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi 700050 Romania

This work aims at developing and testing a novel Coalitional distributed model predictive control (C-DMPC) strategy suitable for vehicle platooning applications. The stability of the algorithm is ensured via the terminal constraint region formulation, with robust positively invariant sets. To ensure a greater flexibility, in the initialization part of the method, an invariant table set is created containing several invariant sets computed for different constraints values. The algorithm was tested in simulation, using both homogeneous and heterogeneous initial conditions for a platoon with four homogeneous vehicles, using a predecessor-following, uni-directionally communication topology. The simulation results show that the coalitions between vehicles are formed in the beginning of the experiment, when the local feasibility of each vehicle is lost. These findings successfully prove the usefulness of the proposed coalitional DMPC method in a vehicle platooning application, and illustrate the robustness of the algorithm, when tested in different initial conditions.

关键词： coalitional model predictive control distributed model predictive control robust positively invariant sets closed-loop stability vehicle platooning

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Robust distributed model predictive control of linear systems: Analysis and synthesis

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AUTOMATICA 2022年 137卷 110141-110141页

作者： Wang, Ye Manzie, Chris Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

To provide robustness of distributed model predictive control (DMPC), this work proposes a robust DMPC formulation for discrete-time linear systems subject to unknown-but-bounded disturbances. Taking advantage of the structure of certain classes of distributed systems seen in applications with inter-agent coupling, a novel robust DMPC is formulated. The proposed approach is characterised by separable terminal costs and locally robust terminal sets, with the latter sets adaptively estimated in the online optimisation problem. A constraint tightening approach based on a set-membership approach is used to guarantee constraint satisfaction for coupled subsystems in the presence of disturbances. Under this formulation, the closed-loop system is shown to be recursively feasible and input-to-state stable. To aid in the deployment of the proposed robust DMPC, a possible synthesis method and design conditions for practical implementation are presented. Finally, simulation results with a mass-spring-damper system are provided to demonstrate the proposed robust DMPC. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Robust constraint tightening Set-membership approach Robust adaptive terminal sets

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Robust distributed model predictive control for satellite cluster reconfiguration with collision avoidance

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年 130卷

作者： Li, Siyuan Ye, Dong Xiao, Yan Sun, Zhaowei Harbin Inst Technol Res Ctr Satellite Technol Harbin 150001 Peoples R China

This article investigates a reconfiguration problem of the satellite cluster with collision avoidance. Focusing on the uncertain disturbances with upper bound, a tube-based auxiliary controller is designed, which can improve the robustness of actual satellite cluster system. Then, a distributed model predictive controller (DMPC) is proposed for the nominal system with thrust constraints. In DMPC, the terminal constraints and terminal control law based on velocity obstacle are designed to satisfy the collision avoidance. Furthermore, the stability and feasibility can be ensured by using optimized slack variable. In addition, to reduce fuel consumption and the actions of collision, an optimal allocation strategy for target points based on Hungarian algorithm is adopted. Finally, the numerical simulations are presented to verify the effectiveness and advantage of the proposed scheme.(c) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Satellite cluster reconfiguration distributed model predictive control Tube -based Collision avoidance Optimal allocation

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Formation control of multiple underwater robots based on ADMM distributed model predictive control

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OCEAN ENGINEERING 2022年 257卷

作者： Zhao, Ran Miao, Maozhi Lu, Jiamin Wang, Yang Li, Daoliang China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China China Agr Univ Key Lab Smart Farming Technol Aquat Anim & Livest Minist Agr & Rural Affairs Beijing 100083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Ag Beijing Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China

In this paper, we propose a distributed model predictive control approach based on the alternating direction multiplier method (ADMM) to solve the formation control problem of multiple autonomous underwater vehicles (AUV). First, a kinematic model with six degrees of freedom is established for underwater vehicles. This is a linear error model which can accelerate the calculation speed of the controller while still ensuring accuracy. This model also incorporates the design of a local distributed controller for each underwater robot. The formation factor is transformed into the state coupling constraint between robots, and ADMM is used for iterative calculations, thus solving the problem of robot formation control. Considering external interference, a feedforward compensation control link is added to ensure stable tracking of the reference trajectory by multiple robots under certain disturbances. Finally, results from simulations verify the effectiveness of the proposed algorithm.

关键词： Formation control distributed model predictive control ADMM Underwater robots

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Nonlinear distributed model predictive control for multi-zone building energy systems

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ENERGY AND BUILDINGS 2022年第0期264卷 1页

作者： Mork, Maximilian Xhonneux, Andre Mueller, Dirk Forschungszentrum Julich Inst Energy & Climate Res Energy Syst Engn IEK 10 Wilhelm Johnen Str D-52425 Julich Germany Rhein Westfal TH Aachen Inst Energy Efficient Bldg & Indoor Climate EON Energy Res Ctr Mathieustr 10 D-52074 Aachen Germany

This paper presents a distributed model predictive control (MPC) approach for multi-zone building energy systems based on nonlinear modelica controller models. The method considers both thermal and hydraulic coupling among different building zones. The iterative and parallel distributed optimization approach builds upon an uncooperative approach for thermal coupling using the Nash equilibrium approach and a cooperative approach for the hydraulic coupling using the Alternating Direction Method of Multipliers (ADMM). Apart from thermal coupling through walls, the modeling takes thermal coupling through doors into account using a data-driven approach, which calculates the inter-zone air exchanges based on temperature differences between door-coupled zones. The hydraulic coupling enables consideration of interactions between the zones introduced by a shared, central Heating, Ventilation and Air Conditioning (HVAC) system. The distributed MPC framework is structured in an easy-scalable, plug-and-play composition, where local systems are automatically assigned to the global coordination scheme. The distributed MPC method is applied to a simulative nonlinear case study, consisting of a six-room-building modelica model considering both thermal and hydraulic interactions. The benefits of the proposed approach are demonstrated and compared against centralized and decentralized control concepts in terms of energy consumption, discomfort and computation time. (c) 2022 Elsevier B.V. All rights reserved.

关键词： distributed model predictive control Multi-zone building modelica Building energy system optimization HVAC Alternating Direction Method of Multipliers (ADMM) Nash equilibrium Nonlinear optimization Thermally Activated Building Systems (TABS)

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A distributed model predictive control strategy for finite-time synchronization problems in multi-agent double-integrator systems

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EUROPEAN JOURNAL OF control 2020年 55卷 56-67页

作者： Franze, Giuseppe Fedele, Giuseppe Univ Calabria DIMES Via Pietro BucciCubo 42C I-87036 Arcavacata Di Rende CS Italy

In this paper, a synchronization problem for a group of agents described by double-integrator dynamics and time-varying communication network is formally addressed. Specifically, the main aim consists in ensuring that the agents, organized as a swarm, are capable to synchronize in finite-time the swarm centroid to a given reference trajectory and to keep this property for all future time instants. To this end, an ad-hoc distributed control architecture is developed by taking advantage from two key ingredients: a novel swarm formation modelling that guarantees finite-time convergence properties of the agent kinematics and a hybrid model predictive control scheme in charge to adequately exploit such a kinematics description. One of the relevant features of the resulting distributed algorithm relies on the fact that both parallel and sequential paradigms are used with the aim to ensure collision avoidance requirements amongst the involved agents. (C) 2019 European control Association. Published by Elsevier Ltd. All rights reserved.

关键词： Swarm models Multi-agent systems Synchronization distributed model predictive control Constraints

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Feedback Linearization Based distributed model predictive control for Secondary control of Islanded Microgrid

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ASIAN JOURNAL OF control 2020年第1期22卷 460-473页

作者： Guo, Zhuoyu Li, Shaoyuan Zheng, Yi Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

In this paper, a distributed model predictive control (DMPC) is proposed for the secondary voltage and frequency control of islanded microgrid, where each distributed generator (DG) is controlled by a model predictive control (MPC) in the secondary control layer, individually. With considering the nonlinear dynamics of DG with primary control, input-output feedback schemes are developed for voltage and frequency control separately. Then, all MPCs use the local and neighboring nodes information to solve the optimization problem instead of communicating with a central controller. In this way, the control of the whole system is fully distributed, which allows for a plug-and-play. The convergence and stability analysis of the overall closed-loop system are provided. The simulation result shows the effectiveness of the proposed method.

关键词： distributed model predictive control input-output feedback linearization microgrid secondary control

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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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