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28th International Conference on System Theory control and Computing

作者： Maxim, Anca Pauca, Ovidiu Lazar, Razvan-Gabriel Amarandei, Cristian-Mihai Caruntu, Constantin-Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania Gheorghe Asachi Tech Univ Iasi Dept Comp Sci & Engn Iasi Romania

ISBN: (纸本)9798350364309;9798350364293

Cloud computing can be an useful tool when dealing with spatially distributed resources, which need to be simultaneously managed. In this work, a simulation environment is developed using OpenStack to apply a distributed model predictive control strategy for a platooning application consisting of multiple autonomous mobile robots. The idea is to develop the entire algorithm by taking advantage of the possibility of creating virtual machines. Thus, each autonomous mobile robot is treated as an individual agent and is modeled and controlled on a virtual machine, whereas the communication between the agents is performed in the cloud, using the TCP/IP protocol standard. The simulation results illustrate that the platooning application deployed in OpenStack outperforms the simulation performed on a single computer.

关键词： cloud computing distributed model predictive control autonomous mobile robots platooning application

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Virtually coupled train set control subject to space-time separation:A distributed economic MPC approach with emergency braking configuration

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High-Speed Railway 2024年第3期2卷 143-152页

作者： Xiaolin Luo Tao Tang Le Wang Hongjie Liu State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China Department of Electrical and Computer Engineering University of CaliforniaRiversideCA 92521USA Beijing Zongheng Electro-Mechanical Technology Development Co. Ltd.Beijing 100081China School of Automation and Intelligence Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China

The emerging virtual coupling technology aims to operate multiple train units in a Virtually Coupled Train Set(VCTS)at a minimal but safe *** guarantee collision avoidance,the safety distance should be calculated using the state-of-the-art space-time separation principle that separates the Emergency Braking(EB)trajectories of two successive units during the whole EB *** this case,the minimal safety distance is usually numerically calculated without an analytic ***,the constrained VCTS control problem is hard to address with space-time separation,which is still a gap in the existing *** solve this problem,we propose a distributed Economic model predictive control(DEMPC)approach with computation efficiency and theoretical ***,to alleviate the computation burden,we transform implicit safety constraints into explicitly linear ones,such that the optimal control problem in DEMPC is a quadratic programming problem that can be solved *** theoretical analysis,sufficient conditions are derived to guarantee the recursive feasibility and stability of DEMPC,employing compatibility constraints,tube techniques and terminal ingredient ***,we extend our approach with globally optimal and distributed online EB configuration methods to shorten the minimal distance among ***,experimental results demonstrate the performance and advantages of the proposed approaches.

关键词： Virtually coupled train set Space-time separation Economic model predictive control distributed model predictive control Emergency braking configuration

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Steffensen's acceleration method for ADMM for solving EV charging optimization problems 9

Steffensen's acceleration method for ADMM for solving EV cha...

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9th International Conference on Power and Renewable Energy

作者： Gong, Yu Yuan, Ye Wang, Zihao Wu, Chao State Grid Liaoning Elect Power Supply Co Ltd Dalian Elect Power Supply Co Dalian Peoples R China

ISBN: (纸本)9798350377477;9798350377460

Charging control strategies determine the charging schedules of electric vehicles (EVs) to meet the travel demands of residents. Due to the bandwidth of the communication system, the scalability, and the robustness of the controller, distributed model predictive control (DMPC) approaches are widely adopted for EV charging control. This paper proposes a novel Steffensen Alternating Direction Method of Multipliers (S-ADMM) algorithm to accelerate the solving process of DMPC charging control planning problems. Via a case study, the performance of the proposed S-ADMM algorithm and the traditional ADMM algorithm are compared. The S-ADMM algorithm shows better performance in terms of convergence speed.

关键词： EV charging control distributed model predictive control Steffensen's method Alternating Direction Method of Multipliers

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Adaptive Learning control for Smart Local Energy Community

Adaptive Learning Control for Smart Local Energy Community

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Brahmia, Ibrahim Klöckl, Bernd Vienna University of Technology Institute for Energy Systems and Electric Drives Vienna Austria

ISBN: (纸本)9789531842976

This paper presents a novel Adaptive Learning control (ALC) approach for optimizing energy management in Smart Local Energy Communities (LECs). The framework combines advanced Reinforcement Learning (RL) with distributed model predictive control (DMPC), enabling real-time adaptation to fluctuations in energy demand, generation, and pricing. A key feature of this method is the use of data-driven algorithms to handle uncertainties in renewable energy sources, such as generation. While optimizing energy dispatch among prosumers. The energy management system employs an on-line trained neural network with stochastic weights that are continuously trained and updated in real-time using incoming data such as load, renewable output, and electricity prices, which are then used to optimize control actions. Additionally, a Hyperparameter Optimization (HPO) was integrated to ensure a balance between the rapid learning rate and the model stability. Decision making across LECs enhances overall system efficiency and resilience. Simulations and case studies demonstrate that this approach improves energy demand predictions and robust self learning, outperforming the conventional RL method. It maintains stable grid performance under dynamic, stochastic conditions and significantly advances self learning. A comparison between the robust prediction capabilities of ALC-EMS versus the conventional RL-EMS approach highlights the benefits of this innovative method. © 2024 IEEE.

关键词： and Real-Time Computing distributed model predictive control Energy Management Local Energy Communities Machine Learning Reinforcement Learning

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Fast Clustering for Multi-agent model predictive control

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IEEE TRANSACTIONS ON control OF NETWORK SYSTEMS 2022年第3期9卷 1544-1555页

作者： Chanfreut, Paula Maestre, Jose Maria Hatanaka, Takeshi Camacho, Eduardo F. Univ Seville Dept Syst & Automat Engn Seville 41009 Spain Tokyo Inst Technol Dept Mech & Control Engn Tokyo 1528550 Japan

In coalitional model predictive control, the overall system is controlled by a set of networked agents that are dynamically arranged into clusters of connected agents that coordinate their actions, also called coalitions. In this way, the overall coordination burden and the need for sharing information are reduced. In this article, the clustering problem is formulated as a binary quadratic program (BOP), where each variable represents one agentto-agent connection. A supervisory layer decides periodically the number and composition of the coalitions by solving the BOP while, at a bottom layer, each cluster computes the control inputs. The performance of this method is illustrated through numerical examples.

关键词： Coalitional control control by clustering distributed model predictive control network topologies

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Convergence analysis on control for traffic signals in urban road network

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2022年 165卷 35-62页

作者： Hao, Zhenzhen Boel, Rene Xian Univ Architecture & Technol Coll Informat & Control Engn Xian 710055 Peoples R China Univ Ghent SYSTeMS Res Grp Technol Pk Zwijnaarde 914 B-9052 Ghent Belgium

This paper presents an analysis on the convergence to a steady state for the traffic dynamics in a network of signalized intersections. The links carrying the traffic from one intersection to another in the network are partitioned into a series of cells whose dynamics is modelled as a nonlinear cell transmission model. A microscopic convergence analysis is presented for the network where the traffic lights at the intersections are controlled by pre-timed control in which the average flow rate along each link is assumed to be known accurately. A macroscopic convergence analysis is applied to the traffic network where the traffic lights are controlled by coordinated model predictive control. In coordinated model predictive control, each local agent optimizes the switching times of traffic light at one intersection online with sampled local traffic state around this intersection and exchanged information from neighbouring intersections. A unique periodic function for the microscopic steady state is proved to exist under pre-timed control while a unique macroscopic steady state is achieved under coordinated model predictive control for the traffic lights. This paper validates by simulations that the ability of coordinated model predictive control to stabilize the traffic demand is not worse than pre-timed control even though the average flow rate along each link is not assumed to be known and further no explicit cycle time for the traffic light is suggested. Moreover, the incurred delay from the steady state is lower on the average under coordinated model predictive control compared to pre-timed control.

关键词： Urban traffic signal control distributed model predictive control Stability Cell transmission model

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Improved longitudinal control strategy for connected and automated truck platoon against cyberattacks

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第12期16卷 1710-1725页

作者： Lyu, Hao Wang, Ting Cheng, Rongjun Ge, Hongxia Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Tongji Univ Coll Transportat Engn Shanghai Peoples R China

With the conception of internet of vehicle, the longitudinal control strategy of connected and automated truck platoon (CATP) to effectively deal with threat of cyberattacks has become an urgent challenge. Based on this, by flexibly changing the communication topology of truck platoon, this paper designs a communication topology safety response system (CTSRS), and further combined with the distributed model predictive control (DMPC) method, an improved longitudinal control strategy of CATP is tentatively proposed. The sufficient conditions for the asymptotic stability of truck platoon are derived by taking the cost function of CATP as the Lyapunov candidate function. Besides, the Next Generation Simulation (NGSIM) data is used to simulate the motion trajectory of the leading truck, which verifies the superior performance of DMPC in controlling the truck platoon to adjust the following behavior according to the different driving behavior of the leading truck. Finally, the cyberattacks scenario is set to conduct the comparative numerical simulation of platoon evolution. Numerical results show that DMPC can still ensure the stability and security of the truck platoon even if the trucks are suffered cyberattacks while CTSRS is enabled, which proves the feasibility of the improved longitudinal control strategy in resisting the threat of cyberattacks.

关键词： Lyapunov candidate function mobile robots DMPC motion trajectory road vehicles numerical simulation intelligent transportation systems platoon evolution trajectory control numerical analysis cost function next generation simulation CTSRS predictive control distributed model predictive control security NGSIM Lyapunov methods distributed control road traffic control following behavior cyberattacks vehicular ad hoc networks asymptotic stability communication topology safety response system sufficient conditions motion control driving behavior longitudinal control connected and automated truck platoon multi-robot systems computer crime Internet of Vehicle CATP

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distributed predictive control for capacitor voltage balancing in a Cascaded H-Bridge Based STATCOM 25

Distributed predictive control for capacitor voltage balanci...

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25th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Navas-Fonseca, Alex Burgos-Mellado, Claudio Arias-Esquivel, Yeiner Verma, Anant Kumar Mora, Andres Munoz-Carpintero, Diego Cardenas, Roberto Univ Andres Bello Engn Fac Energy Transformat Ctr Santiago 7500971 Chile Univ OHiggins Inst Engn Sci Rancagua 2841959 Chile Inst Tecnol Costa Rica Dept Mechatron Engn Cartago 30101 Costa Rica Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 30332 Chile Univ Chile Dept Elect Engn Santiago 8370451 Chile

ISBN: (纸本)9789075815412

This paper proposes a distributed model predictive control (DMPC) scheme for inter-cell and inter-cluster capacitor voltage balancing control of a cascaded H-bridge multilevel static synchronous compensator (STATCOM). The proposed DMPC does not require a central controller to perform these control tasks as they are cooperatively performed by local controllers placed in the STATCOM cells. Three case scenarios are analysed to highlight the features of the proposed control scheme. The results demonstrate that the DMPC scheme is able to manage large communication delays and communication failures.

关键词： STATCOM distributed model predictive control cooperative control Voltage Balancing cascaded H-bridge multilevel

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Resilient predictive control of Constrained Connected and Automated Vehicles under Malicious Attacks 6

Resilient Predictive Control of Constrained Connected and Au...

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IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)

作者： Wei, Henglai Wang, Yan Chen, Jicheng Zhang, Hui Nanyang Technol Univ Ctr Excellence Testing Res Autonomous Vehicles NT Singapore 639798 Singapore Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9798350311259

In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the constrained Connected and Automated Vehicles (CAV) in the presence of F-local malicious attacks. The proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the "resilience set." Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.

关键词： distributed model predictive control platoon control connected and automated vehicle malicious attacks

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Discrete Laguerre-based model predictive control for dynamic consensus of a vehicle platoon with time delay

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2022年第12期53卷 2566-2583页

作者： Resmi, R. Mija, S. J. Jacob, Jeevamma Natl Inst Technol Calicut Elect Engn Dept Calicut 673601 Kerala India

Dynamic consensus of linear time-invariant multi-agent systems (MASs) using distributed model predictive control under the influence of input delay is addressed in this technical note. model predictive control (MPC) is well suited to solve the consensus problem with its ability to handle multivariable systems and constraints. However, the increased computational complexity of MPC restricts its area of applications. An attempt to bridge this gap is made by proposing a Laguerre-based MPC design to bring down the computational load and make it viable for implementation. The effect of input delay on consensus is examined and the stability margin of a MAS with input delay is computed using the location of the closed-loop poles. A comparison of the proposed algorithm with conventional MPC establishes its superiority in convergence time, robustness to input delay and smoothness of the trajectories. The effectiveness of the proposed design is validated through the simulation of dynamic consensus on the benchmark problem of platoon configuration of vehicles.

关键词： distributed model predictive control dynamic consensus input delay Laguerre functions multi-agent systems

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