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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
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
{"title":"Reconstruction of Pressure Support Ventilation Signals: A Virtual Patient Set","authors":"K. Lindup ,&nbsp;M. Bertoni ,&nbsp;F. Padula ,&nbsp;A. Visioli","doi":"10.1016/j.ifacsc.2026.100379","DOIUrl":"10.1016/j.ifacsc.2026.100379","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100379"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"Trajectory-level self-supervision for simulation driven estimators","authors":"Braghadeesh Lakshminarayanan,&nbsp;Cristian R. Rojas","doi":"10.1016/j.ifacsc.2026.100374","DOIUrl":"10.1016/j.ifacsc.2026.100374","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100374"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"Observability analysis and state estimation of wind turbine power systems: A novel sensitivity-based approach","authors":"Hesham Abdelfattah ,&nbsp;Sameh A. Eisa ,&nbsp;Peter Stechlinski","doi":"10.1016/j.ifacsc.2026.100370","DOIUrl":"10.1016/j.ifacsc.2026.100370","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100370"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"Optimal targeted marketing strategy in multiple market systems based on social network","authors":"Mingda Yue ,&nbsp;Jingyu Zhang ,&nbsp;Yuhu Wu ,&nbsp;Xun Shen","doi":"10.1016/j.ifacsc.2025.100356","DOIUrl":"10.1016/j.ifacsc.2025.100356","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100356"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"Efficient Reinforcement Learning from Human Feedback via Bayesian preference inference","authors":"Matteo Cercola,&nbsp;Valeria Capretti,&nbsp;Simone Formentin","doi":"10.1016/j.ifacsc.2026.100398","DOIUrl":"10.1016/j.ifacsc.2026.100398","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100398"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"A model of stem cell dynamics with carrying capacity: The role of feedback on proliferation rate","authors":"Alessandro Borri ,&nbsp;Pasquale Palumbo ,&nbsp;Abhyudai Singh","doi":"10.1016/j.ifacsc.2025.100358","DOIUrl":"10.1016/j.ifacsc.2025.100358","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100358"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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
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
{"title":"Convex computation of regions of attraction from data using sums-of-squares programming","authors":"Oumayma Khattabi ,&nbsp;Matteo Tacchi-Bénard ,&nbsp;Sorin Olaru","doi":"10.1016/j.ifacsc.2026.100361","DOIUrl":"10.1016/j.ifacsc.2026.100361","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100361"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Model-free optimization and control of rigid body dynamics: An extremum seeking for vibrational stabilization approach 无模型优化与刚体动力学控制：一种求极值的振动稳定方法
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-27 DOI: 10.1016/j.ifacsc.2026.100394
Rohan Palanikumar, Ahmed A. Elgohary, Simone Martini, Sameh A. Eisa
{"title":"Model-free optimization and control of rigid body dynamics: An extremum seeking for vibrational stabilization approach","authors":"Rohan Palanikumar,&nbsp;Ahmed A. Elgohary,&nbsp;Simone Martini,&nbsp;Sameh A. Eisa","doi":"10.1016/j.ifacsc.2026.100394","DOIUrl":"10.1016/j.ifacsc.2026.100394","url":null,"abstract":"<div><div>In this paper, we introduce a model-free, real-time, dynamic optimization and control method for a class of rigid body dynamics. Our method is based on a recent extremum seeking control for vibrational stabilization (ESC-VS) approach that is applicable to a class of second-order mechanical systems. The new ESC-VS method is able to stabilize a rigid body dynamic system about the optimal state of an objective function that can be unknown expression-wise, but assessable through measurements; the ESC-VS is operable by using <em>only</em> one perturbation/vibrational signal. We demonstrate the effectiveness and the applicability of our ESC-VS approach via three rigid-body systems: (1) satellite attitude dynamics, (2) quadcopter attitude dynamics, and (3) acceleration-controlled unicycle dynamics. The results, including simulations with and without measurement delays/noise, illustrate the ability of our ESC-VS to operate successfully as a new methodology of optimization and control for rigid body dynamics.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100394"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Stochastic data-driven predictive control of linear systems with sub-Gaussian disturbances using causal predictors 基于因果预测的亚高斯扰动线性系统的随机数据驱动预测控制
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-28 DOI: 10.1016/j.ifacsc.2026.100399
Johannes Teutsch, Marion Leibold
{"title":"Stochastic data-driven predictive control of linear systems with sub-Gaussian disturbances using causal predictors","authors":"Johannes Teutsch,&nbsp;Marion Leibold","doi":"10.1016/j.ifacsc.2026.100399","DOIUrl":"10.1016/j.ifacsc.2026.100399","url":null,"abstract":"<div><div>We present a stochastic data-driven predictive control (DPC) framework for discrete-time linear time-invariant systems subject to sub-Gaussian additive disturbances based solely on input–output data. In contrast to related methods that rely on exact disturbance data or at least sample generation for closed-loop guarantees, the proposed approach leverages a disturbance data estimate. By enforcing consistency of the disturbance data estimate with the available input–output data and system class, we first identify data-driven and provably causal subspace predictors for use in DPC. Then, we analyze statistical properties of the corresponding prediction error, yielding tightened constraints for the nominal predictions that guarantee satisfaction of chance constraints. The proposed DPC scheme comes with guarantees on recursive feasibility and conditional chance constraint satisfaction in closed-loop under standard assumptions. A numerical evaluation study demonstrates the performance of the proposed controller.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100399"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reducing exercise-related hypoglycemia in automated insulin delivery with reinforcement learning 通过强化学习减少自动胰岛素输送中运动相关的低血糖
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-04 DOI: 10.1016/j.ifacsc.2025.100337
Dana Zimmermann, Hans-Michael Kaltenbach
{"title":"Reducing exercise-related hypoglycemia in automated insulin delivery with reinforcement learning","authors":"Dana Zimmermann,&nbsp;Hans-Michael Kaltenbach","doi":"10.1016/j.ifacsc.2025.100337","DOIUrl":"10.1016/j.ifacsc.2025.100337","url":null,"abstract":"<div><div>Exercise is an important component for glucose management in type 1 diabetes, but remains challenging for automated insulin delivery systems as altered glucose dynamics are difficult to model explicitly. Glucose monitoring data might enable data-driven approaches for learning these dynamics implicitly. We propose combining model predictive control with a reinforcement learning component to adjust basal insulin infusion rates for exercise. We train our model on a variety of exercise scenarios and demonstrate improved glucose control using two different frameworks. We evaluate how generalizable both frameworks are by personalizing a trained model with a small number of additional individual-specific training episodes.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100337"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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