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SSRN 2024年

作者： Li, Yun Yorke-Smith, Neil Keviczky, Tamas Delft Center for Systems and Control Delft University of Technology Delft Netherlands Algorithmics Group Delft University of Technology Delft Netherlands

The way how the uncertainties are represented by sets plays a vital role in the performance of robust optimization (RO). This paper presents a novel approach leveraging machine learning (ML) techniques to construct data-driven uncertainty sets from historical uncertainty data for RO problems. The proposed method integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Principle Component Analysis (PCA) systematically to eliminate the influence of uncertainty scenarios with low occurrence probability and generate a nonconvex uncertainty set that is a union of multiple basic subsets (box or ellipsoid) without sacrificing its computational tractability. In addition to presenting a comprehensive algorithm for uncertainty set development, this paper offers detailed guidelines for parameter tuning and performance analysis. By harnessing the well-established ML packages scikit-learn, a Python-based toolkit for implementing the proposed approach is also provided. Furthermore, a computationally efficient solution for a two-stage linear RO problem with the proposed data-driven uncertainty set is derived, alongside establishing a probabilistic guarantee of constraint satisfaction for out-of-sample uncertainties. Extensive numerical experiments, conducted on both synthetic and real-world datasets as well as a robust model predictive control (MPC) problem, are performed to demonstrate the efficacy of the proposed methodology. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Unifying controller Design for Stabilizing Nonlinear systems with Norm-Bounded control Inputs

arXiv

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arXiv 2024年

作者： Li, Ming Sun, Zhiyong Weiland, Siep Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands

This paper revisits a classical challenge in the design of stabilizing controllers for nonlinear systems with a norm-bounded input constraint. By extending Lin-Sontag’s universal formula and introducing a generic (state-dependent) scaling term, a unifying controller design method is proposed. The incorporation of this generic scaling term gives a unified controller and enables the derivation of alternative universal formulas with various favorable properties, which makes it suitable for tailored control designs to meet specific requirements and provides versatility across different control scenarios. Additionally, we present a constructive approach to determine the optimal scaling term, leading to an explicit solution to an optimization problem, named optimization-based universal formula. The resulting controller ensures asymptotic stability, satisfies a norm-bounded input constraint, and optimizes a predefined cost function. Finally, the essential properties of the unified controllers are analyzed, including smoothness, continuity at the origin, stability margin, and inverse optimality. Simulations validate the approach, showcasing its effectiveness in addressing a challenging stabilizing control problem of a nonlinear system. Copyright © 2024, The Authors. All rights reserved.

关键词： Nonlinear systems

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Green Routing Game: Pollution-Aware Mixed Fleet Logistics with Shared Charging Facilities

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IEEE Transactions on Automatic control 2025年

作者： Sasahara, Hampei Dan, Gyorgy Amin, Saurabh Sandberg, Henrik Institute of Science Tokyo Department of Systems and Control Engineering Tokyo152-8552 Japan KTH Royal Institute of Technology Division of Network and Systems Engineering StockholmSE-100 44 Sweden Massachusetts Institute of Technology Laboratory of Information and Decision Systems CambridgeMA02139 United States KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-100 44 Sweden

Eco-friendly freight operations are crucial for decarbonizing the transportation sector. Systematic analysis of policy measures requires a principled modeling approach. While the commonly used model referred to as routing game considers the congestible nature of transportation facilities, exiting models fail to account for environmental factors. This paper aims at providing a mathematical framework to study strategic interaction between owners of mixed fleets comprising of both internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. This study introduces a "green"routing game with incomplete information that models strategic interaction among multiple logistic operators. These players face a pollution tax imposed on ICEVs and a potential delayed delivery cost due to EV charging requirements with uncertainty. In contrast to existing models, this novel model captures the players' trade-off between lengthier congestion delay at charging stations as the share of EV trucks increases and higher pollution costs with increased ICEVs usage, with uncertainty determined by a latent state. We first provide equilibrium characterization and present a condition for essential uniqueness. We show that this equilibrium can be computed in a distributed manner using a gradient projection method. We then introduce a public information system that broadcasts real-time information about the latent state. Importantly, we analyze value of information for providing a condition for the public information to be beneficial. Finally, we present numerical examples to illustrate settings where environmental taxation and information dissemination can improve social welfare. © 1963-2012 IEEE.

关键词： Trucks

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

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

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

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

ISBN: (纸本)9798331515966

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. © 2024 IEEE.

关键词： Deep reinforcement learning

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Combined Path and Trajectory Planning for Energy-Efficient Coordination of Automated Vehicles in Confined Areas

Combined Path and Trajectory Planning for Energy-Efficient C...

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European control Conference (ECC)

作者： Stefan Kojchev Jonas Hellgren Robert Hult Jonas Fredriksson Volvo Autonomous Solutions and the Mechatronics Group Systems and Control Chalmers University of Technology Research Institutes of Sweden (RISE) Göteborg Sweden Volvo Autonomous Solutions Göteborg Sweden Mechatronics Group Systems and Control Chalmers University of Technology Göteborg Sweden

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, we present a framework for combined path and motion trajectory planning for the purpose of coordinating fully automated vehicles in confined sites. The path planning component utilizes a Monte-Carlo tree search approach for computing the vehicle paths and the motion trajectory component utilizes a two-stage optimization-based algorithm that optimizes the state and input trajectories for all vehicles while avoiding inter-vehicle conflicts. The motion trajectories are tracked by a low-level controller and both the path and motion trajectories are recomputed based on the feedback signals. The performance of the framework is validated through numerical simulations and results show both improved energy efficiency and productivity.

关键词： Productivity Monte Carlo methods Trajectory planning Tracking Europe Numerical simulation Energy efficiency

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Experimentally Validated Reduced-Order Models for Grid-Connected Inverters Using Balanced Residualization

Experimentally Validated Reduced-Order Models for Grid-Conne...

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IEEE Electronic Power Grid (eGrid)

作者： Hans Würfel Nicolai Lorenz-Meyer Johannes Schiffer Chair of Control Systems and Network Control Technology BTU Cottbus-Senftenberg Cottbus Germany Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems (IEG) Cottbus Germany

With an increasing share of power-electronics inter-faced generation units in the energy mix, the demand for accurate dynamic transient simulations of power grids rises. Up to now, such simulations often employ highly simplified inverter models that neglect the inner control loops by replacing them with ideal voltage or current sources. In this paper, we present inner loop models for both voltage and current control modes based on first principles. We use the balanced residualization approach to derive reduced-order models, which cover a middle ground between highly simplistic ideal sources and computationally expensive full-order models. The models are validated against experimental data in a Power-Hardware-in-the-Loop laboratory.

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Energy Efficient Optimization-Based Coordination of Electric Automated Vehicles in Confined Areas

Energy Efficient Optimization-Based Coordination of Electric...

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IEEE Conference on Decision and control

作者： Stefan Kojchev Robert Hult Jonas Fredriksson Volvo Autonomous Solutions and the Mechatronics Group Systems and Control Chalmers University of Technology Volvo Autonomous Solutions Göteborg Sweden Mechatronics Group Systems and Control Chalmers University of Technology Göteborg Sweden

In this paper, we present an optimization-based control strategy for coordinating multiple electric automated vehicles (AVs) in confined sites. The approach focuses on obtaining and keeping energy-efficient driving profiles for the AVs while avoiding collisions in cross-intersections, narrow roads, and merge crossings. Specifically, the approach is composed of two optimization-based components. The first component obtains the energy-efficient profiles for each individual AV by solving a Nonlinear Program (NLP) for the vehicle's complete mission route. The conflict resolution, which is performed by the second component, is accomplished by solving a time-scheduling Mixed Integer Linear Programming (MILP) problem that exploits the application characteristics. We demonstrate the performance of the algorithm through a non-trivial comparative simulation example with an alternative optimization-based heuristic.

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Electrical Fault Localisation Over a Distributed Parameter Transmission Line

Electrical Fault Localisation Over a Distributed Parameter T...

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IEEE Conference on Decision and control

作者： Daniel Selvaratnam Amritam Das Henrik Sandberg Division of Decision and Control Systems EECS KTH Royal Institute of Technology Stockholm Sweden EE Dept. Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands

Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable. Since the time of the fault is unknown, and voltages and currents are measured at only one end of the line, distance information must be extracted from the post-fault transients. To properly account for high-frequency transient behaviour, the line dynamics is modelled directly by the Telegrapher's equation, rather than the more commonly used lumped-parameter approximations. First, the governing equations are non-dimensionalised to avoid ill-conditioning. A closed-form expression for the transfer function is then derived. Finally, nonlinear least-squares optimisation is employed to search for the fault location. Requirements on fault bandwidth, sensor bandwidth and simulation time-step are also presented. The result is a novel end-to-end algorithm for data generation and fault localisation, the effectiveness of which is demonstrated via simulation.

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An event-triggered tracking control scheme based on predictive control method and its application

An event-triggered tracking control scheme based on predicti...

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第42届中国控制会议

作者： Lei Cao Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Dynamical Systems and Control Research Group Department of Electromechanical Systems and Metal Engineering Ghent University Department of Control and Automation Engineering Istanbul Technical University Center for Control Science and Technology Southern University of Science and Technology

In this paper, the tracking control problem for networked control system(NCS) under communication delays is investigated. In order to realize the tracking of the NCS, an event-triggered predictive control strategy is provided to compensate for the delays and achieve stability. Both tracking performance and input increments are considered in the proposed cost *** on the incremental inputs, an event-triggered mechanism is established to reduce the communication costs. The efforts of different parameters are illustrated through the results of the simulation, and the effectiveness of the proposed control scheme is also validated from the application of the networked lighting setup.

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Adaptive identification of SISO linear infinite-dimensional systems

arXiv

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arXiv 2023年

作者： Chattopadhyay, Sudipta Sukumar, Srikant Natarajan, Vivek The Systems and Control Engineering Group Indian Institute of Technology Bombay Mumbai400076 India

We propose an adaptive algorithm for identifying the unknown parameter in a linear exponentially stable single-input single-output infinite-dimensional system. We assume that the transfer function of the infinite-dimensional system can be expressed as a ratio of two infinite series in s (the Laplace variable). We also assume that certain identifiability conditions, which include a persistency of excitation condition, hold. For a fixed integer n, we propose an update law driven by real-time input-output data for estimating the first n + 1 coefficients in the numerator and the denominator of the transfer function. We show that the estimates for the transfer function coefficients generated by the update law are close to the true values at large times provided n is sufficiently large (the estimates converge to the true values as time and n tend to infinity). The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter. We also provide a numerical scheme for verifying the identifiability conditions and for choosing n sufficiently large so that the value of the reconstructed parameter is close to the true value. The class of systems to which our approach is applicable includes many partial differential equations with constant/spatially-varying coefficients and distributed/boundary input and output. We illustrate the efficacy of our approach using three examples: a delay system with four unknown scalars, a 1D heat equation with two unknown scalars and a 1D wave equation with an unknown spatially-varying coefficient. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

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