检索结果-内蒙古大学图书馆

Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

学校读者我要写书评

暂无评论

Model predictive control for tracking using artificial refer...

IEEE Conference on Decision and control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

DAFNI: A computational platform to support infrastructure systems research

学校读者我要写书评

暂无评论

Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

Traction powered multiport DC-DC converter for bidirectional EV charging application – HIL simulation results

学校读者我要写书评

暂无评论

Traction powered multiport DC-DC converter for bidirectional...

Compatibility in Power Electronics (CPE)

作者： Mykola Lukianov Enrique Romero Cadaval Oleksandr Matiushkin Ryszard Strzelecki Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland Power Electrical and Electronic Systems R+D Group University of Extremadura Badajoz Spain Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Tallinn Estonia Faculty of Electrical Engineering Gdynia Maritime University Gdynia Poland

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

The limited availability of public electric vehicle (EV) charging infrastructure poses a significant challenge to EV adoption in urban areas. This underscores the need for cost-effective solutions that enable charging for a large number of EVs, particularly for multiterminal charging stations. One promising approach to reducing installation costs is the implementation of a current-fed multi-active bridge multiport converter, powered by existing DC traction infrastructure. The use of a multiport converter allows for sharing electric components between several ports, reducing total number of elements required, compared to conventional systems, where each EV charger relies on a separate converter. Additionally, connection to the DC traction grid enables sharing some of the substation equipment between the traction network and the EV charger, further lowering installation costs while providing additional benefits, such as DC grid voltage stabilization and transit transport braking energy recuperation. In the case studied in the paper, the use of the converter reduced catenary voltage drops by $10 \%$ and voltage surges by $30 \%$, compared to $\pm 35 \%$ in the base scenario (without a converter).

关键词： Power engineering Costs Substations Hardware-in-the-loop simulation Urban areas Voltage DC-DC power converters Electric vehicle charging Traction power supplies Surges

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection

学校读者我要写书评

暂无评论

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

International Conference on Parallel and Distributed systems (ICPADS)

作者： Long Cheng Haoyang He Yan Gu Qingzhi Liu Zhiming Zhao Fang Fang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China Information Technology Group Wageningen University and Research Netherlands Research Group of Multi-Scale Networked Systems University of Amsterdam Netherlands

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of $53.18 \%$ and costs by $61.98 \%$ compared to basic techniques, and achieving $20.26 \%$ and $25.71 \%$ improvements over the latest advanced methods, respectively.

关键词： Training Cloud computing Privacy Costs Scalability Deep reinforcement learning Dynamic scheduling Real-time systems Complexity theory Protection

Game Theory in Formula 1: Multi-agent Physical and Strategical Interactions

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Fieni, Giona Neumann, Marc-Philippe Furia, Francesca Caucino, Alessandro Cerofolini, Alberto Ravaglioli, Vittorio Onder, Christopher H. Institute for Dynamic Systems and Control ETH Zürich Zürich8092 Switzerland Department of Industrial Engineering Università di Bologna Forlì47121 Italy Power Unit Performance Group Ferrari S.p.A. Maranello41053 Italy

This paper presents an optimization framework for Formula 1 racing that integrates multi-agent interactions, aerodynamic wake effects, trajectory optimization, and energy management. By employing game-theoretic methods, we formulate the minimum lap time problem as either a Nash or a Stackelberg game. Exploiting their structural similarities, we compare symmetric and hierarchical strategies to analyze competitive racing dynamics and strategic dominance. Additionally, we introduce an algorithm to refine local Stackelberg solutions. Our findings underscore the importance of jointly optimizing physical interactions, energy management, and trajectory, highlighting their strong interdependence. We examine the impact of slipstreaming on trajectory selection in corners, straights, and high-speed sections, while also identifying optimal overtaking locations based on energy allocation strategies. By incorporating a physically accurate interaction model and accounting for the optimal responses of competing agents, our approach reveals characteristic strategic behaviors observed in real-world racing. The proposed methodology contributes towards realistic Formula 1 race strategy optimizations, with potential applications in motorsport engineering and autonomous racing. Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

OptiTwin: Data-Driven Machining Process Optimization Platform for SMEs

学校读者我要写书评

暂无评论

OptiTwin: Data-Driven Machining Process Optimization Platfor...

International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： José Joaquín Peralta Abadía Felix Larrinaga Barrenechea Mikel Cuesta Zabaljauregui Xabier Badiola Aiestaran Aitor Duo Zubiaurre Gorka Olalde Mendia Txema Perez Lazare Software and System Engineering Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain High Performance Machining Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Electronic Power Systems Applied to the Control Electric Energy Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain Data Analysis and Cybersecurity Research Group Mondragon Goi Eskola Politeknikoa Arrasate Spain

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.

关键词： Manufacturing industries Productivity Drilling Condition monitoring Quality assurance Data acquisition Real-time systems Data models Optimization Manufacturing automation

Interval Markov Decision Processes with Continuous Action-Spaces

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Delimpaltadakis, Giannis Lahijanian, Morteza Mazo, Manuel Laurenti, Luca Control Systems Technology Group Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Aerospace Engineering Sciences & Computer Science University of Colorado Boulder BoulderCO United States Delft Center for Systems and Control Mechanical Engineering Delft University of Technology Delft Netherlands

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |Q | max problems, where |Q | is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set A is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of A, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis. © 2022, CC BY.

关键词： Markov processes

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Benítez-Andrades, José Alberto Alija-Pérez, José-Manuel Vidal, Maria-Esther Pastor-Vargas, Rafael García-Ordás, María Teresa SALBIS Research Group Department of Electric Systems and Automatics Engineering University of León León Spain SECOMUCI Research Group Escuela de Ingenierías Industrial e Informática Universidad de León León Spain Leibniz University of Hannover Hannover Germany Communications and Control Systems Department Spanish National University for Distance Education Madrid Spain

Background: Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer–based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions: Bidirectional encoder representations from transformer–based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder–related tweets. © 2024, CC BY-NC-ND.

关键词： Natural language processing systems

Identifiability in Dynamic Acyclic Networks with Partial Excitations and Measurements

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Cheng, Xiaodong Shi, Shengling Lestas, Ioannis Van den Hof, Paul M.J. Department of Plant Science Wageningen University & Research Wageningen6700 AA Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands Control Group Department of Engineering University of Cambridge CB2 1PZ United Kingdom Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands

This paper deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considering an acyclic network where only a subset of its vertices are measured and a subset of the vertices are excited, we explore conditions under which all the modules are identifiable on the basis of measurement data. Two sufficient conditions are presented in the paper, where the first condition concerns an identifiability analysis that needs to be performed for each vertex, while the second condition, based on the concept of tree/anti-tree covering, results from a graphical synthesis approach to allocate actuators and sensors in an acyclic network for achieving generic identifiability. Copyright © 2022, The Authors. All rights reserved.

关键词：