European Journal of Control最新文献

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An opinion dynamics approach to model and analyze the behavior of consumers in an energy network 意见动力学方法在能源网络中对消费者行为建模和分析

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ejcon.2025.101370

Vaibhav Kumar Singh , Lorenzo Zino , Gabriel Muinos , Jacquelien M.A. Scherpen , Michele Cucuzzella

Motivated by theories and evidence from the social psychology literature, we propose a novel continuous-time mathematical model that captures the evolution of motivation and behavior of energy consumers in a social network. In our model, consumers are connected to the energy grid and their energy demand (which we shall refer to as their behavior) is affected by their personal motivation on reducing (or increasing) their energy consumption. The motivation-behavior dynamics of each consumer is modeled using a second-order continuous-time bilinear differential equation. Each consumer has the ability to share their motivation on a social network and observe the behavior of other consumers. Using the information gathered from their peers and their personal bias about the energy consumption behavior, consumers have the ability to revise their own motivation about energy consumption and, ultimately, their behavior. Moreover, we incorporate into the model an external control action that captures the implementation of external behavioral interventions that influence the weight each consumer assigns to their own bias. Then, we use the proposed framework to shed light on the collective motivation-behavior dynamics of all the consumers, establishing conditions for the existence of equilibria, characterizing them, and performing a sensitivity analysis of such equilibria with respect to variations in the steady-state interventions provided to each consumer.

受社会心理学文献的理论和证据的启发，我们提出了一个新的连续时间数学模型，以捕捉社会网络中能源消费者的动机和行为的演变。在我们的模型中，消费者连接到能源网，他们的能源需求（我们称之为他们的行为）受到他们减少（或增加）能源消耗的个人动机的影响。利用二阶连续双线性微分方程对每个消费者的动机-行为动力学进行建模。每个消费者都有能力在社交网络上分享他们的动机，并观察其他消费者的行为。消费者有能力利用从同伴那里收集到的信息和他们对能源消费行为的个人偏见来修正自己的能源消费动机，并最终改变他们的行为。此外，我们将外部控制行动纳入模型，该行动捕获了影响每个消费者分配给自己偏见的权重的外部行为干预的实施。然后，我们使用所提出的框架来阐明所有消费者的集体动机-行为动力学，建立均衡存在的条件，描述它们，并对提供给每个消费者的稳态干预的变化进行此类均衡的敏感性分析。

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引用次数: 0

Student usage of Lund University Pole-Zero Explorer interactive tool in automatic control teaching 学生在自动控制教学中使用隆德大学Pole-Zero Explorer交互工具

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-07-20 DOI: 10.1016/j.ejcon.2025.101313

Frida Heskebeck , Pex Tufvesson , Ask Hällström

This study examines the effectiveness of LU-PZE³, a web-based interactive tool designed to help students visualize and interact with fundamental concepts in automatic control. A total of 200 students enrolled in an introductory course on automatic control had the option to use LU-PZE as an additional resource for learning. LU-PZE offers users randomized quizzes, structured assignments, and real-time visualizations of theoretical concepts. An analysis of student usage shows that LU-PZE successfully increased student engagement and improved their understanding of automatic control principles. Student feedback also reveals that the students appreciate interactive tools for learning. These findings suggest that LU-PZE can be a valuable tool for teachers to promote active learning and enhance student outcomes in introductory courses in automatic control.

本研究考察了LU-PZE3的有效性，这是一个基于网络的互动工具，旨在帮助学生可视化和互动自动控制的基本概念。共有200名学生参加了自动控制入门课程，他们可以选择使用LU-PZE作为额外的学习资源。LU-PZE为用户提供随机测验、结构化作业和理论概念的实时可视化。对学生使用情况的分析表明，LU-PZE成功地提高了学生的参与度，提高了他们对自动控制原理的理解。学生的反馈也表明，学生们喜欢互动的学习工具。这些研究结果表明，LU-PZE可以成为教师促进主动学习和提高学生在自动控制入门课程中的成果的有价值的工具。

引用次数: 0

Feedback design for biological control by the sterile insect release technique exploiting monotone system theory 利用单调系统理论的昆虫不育释放技术生物防治反馈设计

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-07-31 DOI: 10.1016/j.ejcon.2025.101292

Amit Bhaya , Pierre-Alexandre Bliman

The Sterile Insect Technique (SIT) is a promising control method against insect pests and insect vectors. It consists in releasing males previously sterilized in laboratory, in order to reduce or eliminate a specific wild population. We study the implementation of SIT-based elimination campaign of Aedes mosquitoes using feedback control. We provide state-feedback and output-feedback control laws and establish their convergence, as well as their robustness properties. In this design procedure, a pivotal role is played by the use of properties of monotone systems. Simple illustrative simulations are provided.

昆虫不育技术是一种很有前途的防治害虫和病媒昆虫的方法。它包括释放先前在实验室绝育的雄性，以减少或消除特定的野生种群。我们研究了基于信息技术的伊蚊消灭运动的实施，并采用反馈控制。给出了状态反馈和输出反馈控制律，并建立了它们的收敛性和鲁棒性。在这个设计过程中，使用单调系统的特性起着关键作用。给出了简单的说明性仿真。

引用次数: 0

Distributed time-varying Gaussian process regression via Kalman filtering 基于卡尔曼滤波的分布时变高斯过程回归

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ejcon.2025.101371

Nicola Taddei , Riccardo Maggioni , Jaap Eising , Giulia De Pasquale , Florian Dörfler

We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control applications, whenever real-time and robust estimation of dynamic cost/reward functions in safety critical settings has to be performed. In this paper, we adopt a finite-dimensional approximation of a Gaussian process, corresponding to a Bayesian linear regression in an appropriate feature space, and propose a new algorithm, DistKP, to track the time-varying coefficients via a distributed Kalman filter. The proposed method works for arbitrary kernels and under weaker assumptions on the time-evolution of the function to learn compared to the literature. We validate our results using a simulation example in which a fleet of Unmanned Aerial Vehicles (UAVs) learns a dynamically changing wind field.

我们考虑以分布式方式学习时变函数的问题，其中代理收集本地信息以协作实现共享估计。这项任务在控制应用中尤其重要，因为必须在安全关键设置中执行动态成本/奖励函数的实时和鲁棒估计。在本文中，我们采用高斯过程的有限维近似，对应于适当特征空间中的贝叶斯线性回归，并提出了一种新的算法DistKP，通过分布式卡尔曼滤波器跟踪时变系数。与文献相比，所提出的方法适用于任意核，并且对函数的时间演化假设较弱。我们使用一个仿真示例验证了我们的结果，其中一个无人驾驶飞行器（uav）车队学习动态变化的风场。

引用次数: 0

Input-to-task redundancy and dynamic control allocation for multi-agent systems 多智能体系统的输入任务冗余与动态控制分配

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-07-21 DOI: 10.1016/j.ejcon.2025.101287

Lorenzo Govoni, Andrea Cristofaro

This paper introduces the notion of input-to-task redundancy in multi-agent systems and extends the dynamic control allocation paradigm to input-constrained multi-agent systems. In particular, it is shown that a proper selection of a subset of the system outputs can entail a weak redundancy condition with respect to a given task to be executed, thereby providing additional degrees of freedom to cope with potential input constraints. Moreover, we propose a way of quantitatively characterizing the additional freedom in the controllability of the system by means of a redundancy degree suitably defined. The efficacy of the approach has been tested and validated by numerical simulations in a platooning application.

本文引入了多智能体系统中输入任务冗余的概念，并将动态控制分配范式扩展到输入约束的多智能体系统中。特别是，它表明，适当选择系统输出的子集可能导致相对于要执行的给定任务的弱冗余条件，从而提供额外的自由度来处理潜在的输入约束。此外，我们还提出了一种通过适当定义冗余度来定量表征系统可控性中的附加自由度的方法。仿真结果表明，该方法的有效性得到了验证。

引用次数: 0

Homogeneous predictor feedback for a 1D reaction–diffusion equation with input delay 具有输入延迟的一维反应扩散方程的齐次预测反馈

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-02 DOI: 10.1016/j.ejcon.2025.101363

Mericel Ayamou , Nicolas Espitia , Andrey Polyakov , Emilia Fridman

This paper deals with nonlinear boundary stabilization of a 1D reaction–diffusion equation with input delay. Using the modal decomposition approach, we propose a homogeneous-based predictor feedback for stabilizing the unstable modes. We prove the stability of the closed-loop system via the construction of a suitable Lyapunov functional. We present numerical simulations to support the analytical results and compare our proposed controller to linear predictor feedback regarding closed-loop performance and peaking effect.

研究一类具有输入时滞的一维反应扩散方程的非线性边界镇定问题。利用模态分解方法，我们提出了一种基于齐次的预测器反馈来稳定不稳定模态。通过构造一个合适的Lyapunov泛函，证明了闭环系统的稳定性。我们提出了数值模拟来支持分析结果，并将我们提出的控制器与线性预测器反馈在闭环性能和峰值效应方面进行了比较。

引用次数: 0

Convergence rate of payoff-based generalized Nash equilibrium learning 基于收益的广义纳什均衡学习的收敛速度

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ejcon.2025.101372

Tatiana Tatarenko , Maryam Kamgarpour

We consider generalized Nash equilibrium (GNE) problems in games with strongly monotone pseudo-gradients and jointly linear coupling constraints. We establish the convergence rate of a payoff-based approach intended to learn a variational GNE (v-GNE) in such games. While convergent algorithms have recently been proposed in this setting given full or partial information of the gradients, rate of convergence in the payoff-based information setting has been an open problem. Leveraging properties of a game extended from the original one by a dual player, we establish a convergence rate of

O (\frac{1}{t^{4 / 7}})

to a v-GNE of the game.

研究了具有强单调伪梯度和联合线性耦合约束的对策中的广义纳什均衡问题。我们建立了一种基于收益的方法的收敛率，旨在学习这种游戏中的变分GNE （v-GNE）。虽然最近已经提出了在给定梯度的全部或部分信息的情况下的收敛算法，但基于收益的信息设置的收敛速度一直是一个开放的问题。利用双玩家游戏从原始游戏扩展而来的特性，我们建立了游戏的v-GNE收敛速率为0（1 / 4/7）。

引用次数: 0

Data-driven min–max MPC for LPV systems with unknown scheduling signal 具有未知调度信号的LPV系统的数据驱动最小-最大MPC

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ejcon.2025.101373

Yifan Xie, Julian Berberich, Felix Brändle, Frank Allgöwer

This paper presents a data-driven min–max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. The goal is to steer the system to the origin while ensuring that the closed-loop system satisfies input and state constraints. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min–max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation.

针对线性变参数系统，提出了一种数据驱动的最小-最大模型预测控制方案。目标是在保证闭环系统满足输入和状态约束的同时，将系统引导到原点。与现有的数据驱动LPV控制方法相反，我们假设在离线数据采集和在线系统运行期间调度信号是未知的。假设调度信号的二次矩阵不等式（QMI）描述，我们开发了一种新的数据驱动的一致性系统矩阵表征，仅使用输入状态数据。提出的数据驱动的最小最大MPC在一致的系统矩阵集和所有满足QMI的调度信号上最小化了最坏情况代价的可处理上界。该方法保证了递归可行性、闭环指数稳定性和初始可行时约束的满足性。通过仿真验证了该方法的有效性。

引用次数: 0

Fault-tolerant prescribed performance control for a class of uncertain nonlinear systems 一类不确定非线性系统的容错规定性能控制

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-09-02 DOI: 10.1016/j.ejcon.2025.101362

Lampros N. Bikas, Anastasia-Kyriaki G. Mavridou, George A. Rovithakis

This paper addresses the enhancement of prescribed performance control (PPC) robustness under inelastic actuator faults. PPC is a control strategy that enforces user-defined bounds on output tracking errors to ensure desired transient and steady-state performance, assuming ideal actuator functioning. However, actuator faults compromise this guarantee, risking violation of performance bounds and potential system instability. To overcome this limitation, a robust modification of the conventional PPC is proposed, capable of effectively managing errors during performance bound violations while maintaining closed-loop stability. The approach ensures finite-time error recovery to the prescribed bounds once faults are mitigated, without relying on fault detection mechanisms, thereby simplifying the control scheme. Theoretical analysis confirms the stability and robustness of the proposed controller, and simulation studies validate its effectiveness.

本文研究了在执行器非弹性故障情况下提高规定性能控制（PPC）鲁棒性的问题。PPC是一种控制策略，它在输出跟踪误差上强制执行用户定义的界限，以确保理想的瞬态和稳态性能，假设理想的执行器功能。然而，执行器故障破坏了这种保证，有可能违反性能界限和潜在的系统不稳定。为了克服这一限制，提出了对传统PPC的鲁棒修改，能够在保持闭环稳定性的同时有效地管理性能边界违反期间的错误。该方法在不依赖于故障检测机制的情况下，保证故障在有限时间内恢复到规定的范围内，从而简化了控制方案。理论分析证实了所提控制器的稳定性和鲁棒性，仿真研究验证了其有效性。

引用次数: 0

Robust tube-based reinforcement learning control for systems with parametric uncertainty 参数不确定系统的鲁棒管强化学习控制

IF 2.6 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

European Journal of Control

Pub Date : 2025-11-01 Epub Date: 2025-07-26 DOI: 10.1016/j.ejcon.2025.101326

Jiayue Wang , Hamid Reza Feyzmahdavian, Soroush Rastegarpour, Alf J. Isaksson

This paper presents a novel dual-agent reinforcement learning (Rl) framework designed to improve the robustness of Rl-based control in industrial processes. To address the challenges posed by modeling errors and uncertainties, the framework draws inspiration from the robust control principles of tube Mpc. It features two specialized Rl agents: a nominal agent trained on an idealized process model in a simulated environment and a corrective agent trained to compensate for the discrepancies between the simulated model and actual system dynamics. To ensure realistic error compensation without direct access to the physical process, the simulation is systematically perturbed during training using parametric variations and Gaussian disturbances. A comprehensive case study demonstrates the effectiveness of the proposed method, highlighting its ability to maintain stable, reliable, and adaptive control performance under uncertain operating conditions in industrial applications.

本文提出了一种新的双智能体强化学习（Rl）框架，旨在提高工业过程中基于Rl的控制的鲁棒性。为了解决建模误差和不确定性带来的挑战，该框架从管Mpc的鲁棒控制原理中汲取灵感。它具有两个专门的Rl代理：在模拟环境中根据理想化过程模型训练的名义代理和经过训练的纠正代理，以补偿模拟模型与实际系统动力学之间的差异。为了在不直接接触物理过程的情况下确保真实的误差补偿，在训练过程中使用参数变化和高斯扰动系统地干扰模拟。一个全面的案例研究证明了该方法的有效性，突出了其在工业应用中不确定操作条件下保持稳定、可靠和自适应控制性能的能力。

引用次数: 0

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