IFAC Journal of Systems and Control最新文献

英文中文

Periodic predictor of glucose dynamics in type 1 diabetes patients 1型糖尿病患者血糖动态的周期性预测因子

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-03-04 DOI: 10.1016/j.ifacsc.2026.100401

Matteo Ragni, Paolo Alberto Mongini, Lalo Magni, Chiara Toffanin

Glucose dynamics in type 1 diabetes are highly variable both across individuals and within the same individual throughout the day, due to factors such as meals, insulin sensitivity, and daily routines. This variability poses significant challenges for accurate prediction and control, limiting the effectiveness of single-model approaches. The aim of this work is to develop control-oriented models to provide accurate predictions of glucose dynamics, to be used within control strategies, such as model predictive control. The proposed approach adopts a periodic structure, based on multiple models, that accounts for both individual differences, in terms of metabolic response, and daily fluctuations in patient dynamics. Models are identified in different daily periods using an impulse response method applied to data generated by the UVA/Padova simulator. The day is divided into three time segments corresponding to breakfast, lunch, and dinner, with a separate model trained for each period. These models are integrated through a soft-switching mechanism. Two state estimation techniques, the Kalman filter and the moving horizon estimator, are compared to enable multi-step glucose prediction. Results indicate that the periodic predictor consistently outperforms the invariant predictor approaches across the entire virtual adult population. This modeling framework shows strong potential for integration into advanced insulin delivery systems based on predictive control.

由于饮食、胰岛素敏感性和日常生活等因素，1型糖尿病患者的葡萄糖动态在个体之间和同一个体内全天都是高度可变的。这种可变性对准确预测和控制提出了重大挑战，限制了单模型方法的有效性。这项工作的目的是开发面向控制的模型，以提供葡萄糖动力学的准确预测，用于控制策略，如模型预测控制。所提出的方法采用基于多个模型的周期性结构，既考虑了代谢反应方面的个体差异，也考虑了患者动力学的日常波动。使用脉冲响应方法对UVA/Padova模拟器生成的数据进行识别，以确定不同日周期的模型。一天分为三个时间段，分别是早餐、午餐和晚餐，每个时间段都有一个单独的模型。这些模型通过软开关机制集成。两种状态估计技术，卡尔曼滤波和移动视界估计，进行比较，使多步葡萄糖预测。结果表明，在整个虚拟成人群体中，周期性预测器始终优于不变预测器方法。该建模框架显示了整合到基于预测控制的先进胰岛素输送系统中的强大潜力。

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引用次数: 0

Sensitivity analysis of magnetic detection electrical impedance tomography: A numerical study 磁探测电阻抗层析成像的灵敏度分析：数值研究

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1016/j.ifacsc.2026.100391

Alberto Battistel , Ali Caglar Özen , Dominik von Elverfeldt , Knut Möller

Electrical Impedance Tomography (EIT) is safe, non-invasive, and cost-effective medical imaging technique that reconstructs the conductivity, or impedance, inside the body. It employs electrodes located on the exterior and sequentialy injects alternating currents between a pair of them and measures the resultant voltage on the remaining electrodes. To overcome the severely ill-posedness of EIT, that is the sensitivity of peripheral voltages to inner conductivities, magnetic readings can be added as in Magnetic Detection Electrical Impedance Tomography (MD-EIT). Here through numerical simulations we investigate the sensitivity and accuracy in the signal of the added magnetic readings and compare to the standard EIT. In particular a 3D cylindrical phantom with 16 electrodes was simulated where magnetic sensors were placed inbetween electrode pairs and that multiple sensor/target configurations were investigated. We identified numerical inaccuracy as a key factor influencing magnetic field calculations. In general,

ρ

the radial component of the magnetic field showed a signal sensitivity comparable to the one of standard EIT, while the

z

component was the largest in magnitude and the least affected by mesh-induced numerical artefacts.

电阻抗断层扫描（EIT）是一种安全、无创、经济高效的医学成像技术，可重建人体内部的电导率或阻抗。它采用位于外部的电极，依次在一对电极之间注入交流电，并测量其余电极上的合成电压。为了克服EIT的严重缺陷，即外围电压对内部电导率的敏感性，可以像磁检测电阻抗断层扫描（MD-EIT）一样增加磁读数。本文通过数值模拟研究了附加磁读数信号的灵敏度和精度，并与标准EIT进行了比较。特别地，模拟了具有16个电极的三维圆柱形体，其中磁传感器放置在电极对之间，并研究了多个传感器/目标配置。我们认为数值误差是影响磁场计算的关键因素。总的来说，磁场的径向分量ρ显示出与标准EIT相当的信号灵敏度，而z分量的幅度最大，受网格诱发的数值伪影的影响最小。

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引用次数: 0

A model of stem cell dynamics with carrying capacity: The role of feedback on proliferation rate 具有承载能力的干细胞动力学模型：反馈对增殖率的作用

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2025-12-31 DOI: 10.1016/j.ifacsc.2025.100358

Alessandro Borri , Pasquale Palumbo , Abhyudai Singh

Stem cells play a crucial role in biomedical research, offering remarkable potential for regenerative medicine, disease modeling, and drug discovery. Their ability to self-renew and differentiate into specialized cell types makes them essential for tissue repair and regeneration. This note explores a basic model of the differentiation/proliferation mechanisms while accounting for the maximum population size the environment can sustainably support due to limiting resources — i.e., the carrying capacity. Regulatory mechanisms affecting the proliferation rate are investigated using both deterministic and stochastic approaches. The deterministic analysis identifies regions of the parameter space that ensure a stable balance between stem and differentiated cells, while the stochastic approach provides valuable insights suggesting that a positive feedback on the proliferation rate leads to lower fluctuations in the accumulation of differentiated cells.

干细胞在生物医学研究中发挥着至关重要的作用，为再生医学、疾病建模和药物发现提供了巨大的潜力。它们自我更新和分化为特化细胞类型的能力使它们对组织修复和再生至关重要。本文探讨了分化/增殖机制的基本模型，同时考虑了由于资源有限，环境可以持续支持的最大种群规模-即承载能力。利用确定性和随机方法研究了影响增殖速率的调节机制。确定性分析确定了确保干细胞和分化细胞之间稳定平衡的参数空间区域，而随机方法提供了有价值的见解，表明对增殖率的正反馈导致分化细胞积累的波动较小。

引用次数: 0

Convex computation of regions of attraction from data using sums-of-squares programming 利用平方和规划从数据中凸计算吸引区域

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1016/j.ifacsc.2026.100361

Oumayma Khattabi , Matteo Tacchi-Bénard , Sorin Olaru

This paper focuses on the analysis of the Region of Attraction (RoA) for unknown autonomous dynamical systems. A data-driven approach based on the moment-Sum of Squares (SoS) hierarchy is proposed, enabling novel RoA outer approximations despite the reduced information on the dynamics. The main contribution consists of bypassing the system model and, hence, the recurring constraint on its polynomial structure. Numerical experiments showcase the influence of data on learned approximating sets, highlighting the potential of this method.

本文主要研究未知自主动力系统的吸引区（RoA）问题。提出了一种基于矩平方和（so）层次结构的数据驱动方法，尽管减少了动力学信息，但仍能实现新的RoA外部逼近。其主要贡献在于绕过了系统模型，从而避免了对其多项式结构的反复约束。数值实验显示了数据对学习近似集的影响，突出了该方法的潜力。

引用次数: 0

Regularized GLISp for sensor-guided human-in-the-loop optimization 用于传感器引导的人在环优化的正则化GLISp

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ifacsc.2026.100368

Matteo Cercola , Michele Lomuscio , Dario Piga , Simone Formentin

Human-in-the-loop calibration is often addressed via preference-based optimization, where algorithms learn from pairwise comparisons rather than explicit cost evaluations. While effective, methods such as Preferential Bayesian Optimization or Global optimization based on active preference learning with radial basis functions (GLISp) treat the system as a black box and ignore informative sensor measurements. In this work, we introduce a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop through a physics-informed hypothesis function and a least-squares regularization term. This injects grey-box structure, combining subjective feedback with quantitative sensor information while preserving the flexibility of preference-based search. Numerical evaluations on an analytical benchmark and on a human-in-the-loop vehicle suspension tuning task show faster convergence and superior final solutions compared to baseline GLISp.

人在环校准通常通过基于偏好的优化来解决，算法从两两比较中学习，而不是明确的成本评估。虽然有效，但诸如优先贝叶斯优化或基于径向基函数主动偏好学习（GLISp）的全局优化等方法将系统视为黑箱，忽略了信息传感器测量。在这项工作中，我们引入了GLISp的传感器引导正则化扩展，该扩展通过物理信息假设函数和最小二乘正则化项将可测量描述符集成到偏好学习循环中。这注入了灰盒结构，将主观反馈与定量传感器信息相结合，同时保持了基于偏好的搜索的灵活性。对分析基准和人在环车辆悬架调整任务的数值评估表明，与基线GLISp相比，该方法收敛速度更快，最终解决方案更优。

引用次数: 0

Reconstruction of Pressure Support Ventilation Signals: A Virtual Patient Set 压力支持通气信号重建：一组虚拟患者

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ifacsc.2026.100379

K. Lindup , M. Bertoni , F. Padula , A. Visioli

Protection of the lungs and diaphragm is imperative to the safety of a patient receiving pressure support ventilation in the intensive care unit. To this end, accurate modeling of patient–ventilator interactions that occur within a breath is a vital step. Modeling of these interactions may be useful to better understand interactions that compromise patient safety, test in-silico new techniques to estimate physiological signals, and ultimately deliver safe ventilation. However, undisclosed and highly nonlinear internal ventilator dynamics hinder the derivation of such a model. Instead, in this paper, interactions were derived from clinical data, considering 400 breaths per patient to construct a set of 10 virtual patients. A simple first-order system was utilized to describe the interactions, with appropriate correlations between the system’s gain and time constant, and the magnitude of the patient’s effort to generalize the model. In parallel, generalized patient respiratory effort profiles were derived by analyzing similarities in measured efforts. Reconstruction of ventilator waveforms, utilizing the virtual patients, was achieved with a median accuracy greater than 85% in the worst case. A potential use case is also presented, further demonstrating the value of the presented virtual patients for in-silico development and validation of novel techniques. The derived virtual patients are shared via an online repository, and sufficient information is provided for readers to derive additional virtual patients.

保护肺和膈肌对重症监护病房接受压力支持通气的患者的安全至关重要。为此，在一次呼吸中发生的患者与呼吸机相互作用的准确建模是至关重要的一步。这些相互作用的建模可能有助于更好地理解危及患者安全的相互作用，测试计算机新技术来估计生理信号，并最终提供安全的通气。然而，未公开的和高度非线性的内部通风机动力学阻碍了这种模型的推导。相反，在本文中，交互作用来源于临床数据，考虑每个患者400次呼吸来构建一组10个虚拟患者。使用一个简单的一阶系统来描述相互作用，在系统增益和时间常数以及患者推广模型的努力程度之间具有适当的相关性。同时，通过分析测量努力的相似性，得出了广义患者呼吸努力概况。在最坏的情况下，利用虚拟患者重建呼吸机波形的中位数准确率大于85%。还提出了一个潜在的用例，进一步展示了所提出的虚拟患者在计算机开发和新技术验证方面的价值。衍生的虚拟患者通过在线存储库共享，并为读者提供足够的信息以派生额外的虚拟患者。

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引用次数: 0

Trajectory-level self-supervision for simulation driven estimators 仿真驱动估计器的轨迹级自监督

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ifacsc.2026.100374

Braghadeesh Lakshminarayanan, Cristian R. Rojas

Recent advancements in modeling have led to the construction of high-fidelity simulators (digital twins) to represent physical systems. However, the parameters of these high fidelity-simulators must be calibrated to match a given physical system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the parameters of the physical system lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach based on trajectory-level self-supervision for the Two-Stage approach, a simulation-driven estimator, that mitigates OOD effects and improves its accuracy. The effectiveness of the proposed method is verified through numerical simulations.

最近在建模方面的进步导致了高保真模拟器（数字双胞胎）的构建，以表示物理系统。然而，这些高保真模拟器的参数必须校准以匹配给定的物理系统。这激发了仿真驱动参数估计器的构建，通过生成采样参数值的综合观测值并学习从观测值到参数的监督映射来构建。然而，当物理系统的参数位于采样范围之外时，预测会出现分布外（OOD）误差。本文介绍了一种基于轨迹级自我监督的两阶段方法的微调方法，这是一种模拟驱动的估计器，可以减轻OOD影响并提高其准确性。通过数值仿真验证了该方法的有效性。

引用次数: 0

Observability analysis and state estimation of wind turbine power systems: A novel sensitivity-based approach 风力发电系统的可观测性分析与状态估计：一种新的基于灵敏度的方法

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ifacsc.2026.100370

Hesham Abdelfattah , Sameh A. Eisa , Peter Stechlinski

In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state-of-the-art WTPS models that are non-reduced, highly nonlinear differential–algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on different wind speed profiles, including real-world wind data. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails.

在本文中，我们提供了一个新的框架，使基于灵敏度的风力发电系统（wtps）的可观察性测试和状态估计算法。所提供的框架是此类文献中的第一个，因为它能够处理最先进的非约化、高度非线性微分代数方程系统的WTPS模型。此外，该框架在动力学和输出函数中都考虑了非平滑性，以统一不同风速区域的运行条件。我们证明了所提出的框架（得益于广义导数理论的基础工具）在不同风速剖面上的有效性，包括真实世界的风数据。我们还说明了所提出的框架如何利用非光滑过渡期间的鲁棒可观察性分析，在传统扩展卡尔曼滤波方法失败的情况下实现准确的状态估计。

引用次数: 0

Efficient Reinforcement Learning from Human Feedback via Bayesian preference inference 基于贝叶斯偏好推理的有效强化学习

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2026-03-02 DOI: 10.1016/j.ifacsc.2026.100398

Matteo Cercola, Valeria Capretti, Simone Formentin

Learning from human preferences is essential for aligning machine learning models with subjective judgments, but collecting preference data is costly. We study a hybrid framework that combines the scalability of Reinforcement Learning from Human Feedback (RLHF), which trains neural reward models from pairwise comparisons, with the sample efficiency of Preferential Bayesian Optimization. The method integrates Laplace-based Bayesian uncertainty estimation to guide informative preference queries. On high-dimensional Rosenbrock optimization, the approach successfully converges in problems with up to 50 dimensions. In large language model (LLM) fine-tuning, it improves reward-model accuracy by a value within 6–14% under limited annotation budgets.

从人类偏好中学习对于使机器学习模型与主观判断保持一致至关重要，但收集偏好数据的成本很高。我们研究了一个混合框架，该框架结合了基于人类反馈的强化学习（RLHF）的可扩展性（通过两两比较训练神经奖励模型）和优先贝叶斯优化的样本效率。该方法结合基于拉普拉斯的贝叶斯不确定性估计来指导信息偏好查询。在高维Rosenbrock优化上，该方法成功地收敛了50维的问题。在大型语言模型（LLM）微调中，在有限的注释预算下，它将奖励模型的准确性提高了6-14%。

引用次数: 0

Optimal targeted marketing strategy in multiple market systems based on social network 基于社会网络的多市场系统中最优目标营销策略

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.ifacsc.2025.100356

Mingda Yue , Jingyu Zhang , Yuhu Wu , Xun Shen

This paper investigates a duopoly marketing competition over multiple interconnected market systems (MSs). In each MS, consumers are divided into three groups: loyalists of Firm 1, loyalists of Firm 2, and undecided switchers. Firms employ targeted marketing strategies to influence consumer loyalty, leading to instantaneous shifts in the MS composition. Additionally, consumers across different MSs interact through a fixed social network, modeled as a directed graph, which drives continuous consensus-based opinion dynamics. We first establish that the consumer loyalty dynamics are well-posed over time. Then, we prove that the competition between the two firms always admits a unique Nash equilibrium. Furthermore, we analytically characterize the firms’ optimal advertising strategies at equilibrium by explicitly deriving the closed-form structure of their best responses. The results offer practical insights for managers on how to leverage social network interactions across MSs to optimize marketing resource allocation and competitive positioning.

本文研究了在多个相互关联的市场系统（MSs）上的双寡头市场竞争。在每个MS中，消费者被分为三组：公司1的忠诚者，公司2的忠诚者和未决定转换者。公司采用有针对性的营销策略来影响消费者的忠诚度，从而导致MS组成的瞬时变化。此外，跨不同MSs的消费者通过固定的社交网络进行交互，该网络被建模为有向图，从而驱动持续的基于共识的意见动态。我们首先确定消费者忠诚度动态是随时间变化的。然后，我们证明了两家企业之间的竞争总是存在唯一的纳什均衡。此外，我们通过明确地推导出企业最佳对策的封闭形式结构，分析了企业在均衡状态下的最优广告策略。研究结果为管理者如何利用社交网络互动来优化营销资源配置和竞争定位提供了实用的见解。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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