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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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Intersection Crossing of Cooperative Multi-vessel Systems

Intersection Crossing of Cooperative Multi-vessel Systems

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15th International-Federation-of-Automatic-control (IFAC) Symposium on control in Transportation Systems (CTS)

作者： Chen, Linying Negenborn, Rudy R. Hopman, Hans Delft Univ Technol Dept Maritime & Transport Technol Delft Netherlands

A Cooperative Multi-Vessel System (CMVS) is a system consisting of multiple coordinated vessels. Vessels utilize Vessel-2-Vessel and Vessel-2-Infrastructure communication to making decisions with negotiating and\or collaborating with each other for a common goal. Due to the geographic limitations of banks and navigation rules and regulations, in straight waterways, the cooperation of vessels usually results in train-like formations. This behavior is similar to the highway platooning of vehicles. A particular challenge arises when such platoons have to cross waterway intersections. At the intersections, the vessel trains need to interact with others. However, research on the interaction between vehicle platoons is still lacking. This paper focuses on the cooperation of vessels at waterway intersections. We propose a framework for cooperative scheduling and control of CMVSs at intersections. The actions of the vessels are determined by solving two problems: Waterway Intersection Scheduling (WIS) and Vessel Train Formation (VTF). Firstly, the process of the vessels passing through an intersection is regarded as consumption of space and time. The WIS helps to find a conflict-free schedule for the vessels from different directions. By solving the WIS problem, each vessel's desired time of arrival can be determined. Then, the actions of vessels are determined using a distributed model predictive control algorithm in the VTF problem. Agreement among the vessels is achieved via serial iterative negotiations. Simulation experiments are carried out to illustrate the effectiveness of the proposed framework. We compare the passing time of each vessel, and the total passing time in three scenarios: non-cooperative case, partially-cooperative case, and fully-cooperative case. With the proposed cooperative framework, vessels can have smoother trajectories. The total passing time and the passing time for each vessel also benefit from the cooperation. Besides, the proposed framework

关键词： Cooperative Multi-vessel System Waterway Intersection control Vessel Train Formations distributed model predictive control

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An Adaptive Lane-Changing Strategy for Multi-CAV with Complex Platoon Geometric 7

An Adaptive Lane-Changing Strategy for Multi-CAV with Comple...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Wang, Jinlei Yuan, Xiaofang Xiao, Xianghui College of Electrical and Information Engineering Hunan University Changsha China School of Mechatronic Engineering and Automation Foshan University Foshan China

ISBN: (纸本)9798350340488

Lane-changing simultaneously with complex platoon geometric is a complex and critical task for multi-connected autonomous vehicle (CAV). To tackle this problem, an adaptive lane-changing strategy (ALCS) is proposed in this paper. The adaptive performance of the ALCS is realized via three parts: 1) A dynamic motion state inference method (DMSIM) is introduced to generate the platoon geometric based on dynamic safe distance, taking into account the various position and velocity of multi-CAV within the platoon. 2) A polynomial-based path planner (PBPP) is designed to provide a velocity-adjustable path for lane-changing. This path planner enhances the adaptive of various velocity for multi-CAV. 3) A model predictive-based distributed controller (MPBDC) is designed to avoid collision between multi-CAV. This controller improves the controller's capacity to handle velocity changes. Finally, the superiority of the strategy is demonstrated by simulation experiment. © 2023 IEEE.

关键词： Connected autonomous vehicle distributed model predictive control Lane-changing Path planning

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Joint DMPC and PID controller for Vehicle Platoon Based on PreScan and CarSim 7

Joint DMPC and PID Controller for Vehicle Platoon Based on P...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Xiang, Jie Li, Yongfu Huang, Longwang Huang, Xin Wang, Xingquan Key Laboratory of Intelligent Air Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350340488

This article proposes a hierarchical control framework based on a co-simulation platform in PreScan and CarSim for longitudinal control of connected automated vehicles platoon. First, an upper-level controller based on distributed model predictive control approach that takes into account terminal constraints is designed to output the desired acceleration. Then, with the inverse longitudinal dynamics model of the vehicle and PID tracking controller, the lower-level controller is proposed to output the throttle opening angle and brake pressure by tracking the output of upper-level controller. Besides, this article analyzes the closed-loop system stability of distributed model predictive controller. Finally, a platoon scene with five vehicles was built in PreScan, the co-simulation experiment was carried out jointly with PreScan and CarSim to validate the practical performance of the proposed control framework. © 2023 IEEE.

关键词： CarSim distributed model predictive control Hierarchical control Longitudinal control of vehicle platoon PID PreScan

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Robust distributed model predictive consensus of discrete-time multi-agent systems:a self-triggered approach

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Frontiers of Information Technology & Electronic Engineering 2021年第8期22卷 1068-1079页

作者： Jiaqi LI Qingling WANG Yanxu SU Changyin SUN School of Cyber Science and Engineering Southeast UniversityNanjing 210096China School of Automation Southeast UniversityNanjing 210096China

This study investigates the consensus problem of a nonlinear discrete-time multi-agent system(MAS)under bounded additive *** propose a self-triggered robust distributed model predictive control consensus algorithm.A new cost function is constructed and MAS is coupled through this *** on the proposed cost function,a self-triggered mechanism is adopted to reduce the communication ***,to overcome additive disturbances,a local minimum-maximum optimization problem under the worst-case scenario is solved iteratively by the model predictive controller of each *** conditions are provided to guarantee the iterative feasibility of the algorithm and the consensus of the closed-loop *** each agent,we provide a concrete form of compatibility constraint and a consensus error terminal *** examples are provided to illustrate the effectiveness and correctness of the proposed algorithm.

关键词： Consensus Self-triggered control distributed model predictive control

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A Dual Decomposition Algorithm for Separable Nonconvex Optimization Using the Penalty Function Framework

A Dual Decomposition Algorithm for Separable Nonconvex Optim...

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52nd IEEE Annual Conference on Decision and control (CDC)

作者： Quoc Tran Dinh Necoara, Ion Diehl, Moritz Katholieke Univ Leuven Dept Elect Engn ESAT SCD Leuven Belgium Katholieke Univ Leuven Optimizat Engn Ctr OPTEC Leuven Belgium Univ Politehn Bucuresti Automat Control & Syst Engn Dept Bucharest Romania Ecole Polytech Fed Lausanne Lab Informat & Inference Syst LIONS Lausanne Switzerland

ISBN: (纸本)9781467357173

We propose a dual decomposition method for solving separable nonconvex optimization problems that arise e.g. in distributed model predictive control over networks. We first derive a new sequential convex programming (SCP) scheme based on penalty function approach to handle nonconvexity. Then, we combine this SCP scheme with a dual decomposition algorithm to obtain a two-level decomposition algorithm. The global convergence of this algorithm is analyzed under standard assumptions. Some preliminary numerical results are also given to illustrate the theoretical results.

关键词： distributed model predictive control penalty function algorithms decomposition Separable Dual SOUTH CELESTIAL POLE Single cell protein

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Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 216卷

作者： Hou, Jianmin Yu, Weijie Xu, Zhihao Ge, Quanbo Li, Zhi Meng, Ying Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

关键词： Multi -time scale Microgrid Optimal scheduling Distributionally robust optimization distributed model predictive control

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A Composite model predictive control Strategy for Furnaces

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Chinese Journal of Chemical Engineering 2014年第7期22卷 788-794页

作者：臧灏李宏光黄静雯王佳 Automation Department Beijing University of Chemical Technology

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

关键词： Furnace Tracking nonlinear model predictive control Economic nonlinear model predictive control distributed model predictive control

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Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

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control ENGINEERING PRACTICE 2023年第1期138卷

作者： Stomberg, Goesta Ebel, Henrik Faulwasser, Timm Eberhard, Peter TU Dortmund Univ Inst Energy Syst Energy Efficiency & Energy Econ D-44227 Dortmund Germany Univ Stuttgart Inst Engn & Computat Mech D-70569 Stuttgart Germany

distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While the stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion with nominal convergence guarantees. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

关键词： distributed model predictive control Mobile robots Decentralized optimization Alternating direction method of multipliers Formation control Hardware experiment

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distributed MPC based on robustly controllable sets for PWA systems

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AUTOMATICA 2023年第1期154卷

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manage Dept Automat Key Lab Syst Control & Informat ProcMinist Educ C Shanghai 200240 Peoples R China

The optimization problem in model predictive control (MPC) algorithm for piecewise affine (PWA) systems often contains substantial logic variables, which requires extensive computing power for online implementation. This paper proposes a one-step distributed MPC algorithm for spatially interconnected PWA systems with both input and state constraints, where the algorithm is based on a series of r-step robustly controllable sets. Algorithms for offline computing terminal sets and a series of r-step robustly controllable sets are developed using the step-by-step backward approach. The states that lie in the robustly controllable sets are steered into the terminal set in finite steps and ultimately converge into a robustly positively invariant set. The proposed algorithm is demonstrated to significantly reduce the online computational burden. Simulation results are provided to showcase the effectiveness of the algorithm. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： distributed model predictive control Spatially interconnected systems Piecewise affine systems Robustly controllable sets

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