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Taming non-linearity with local data-driven predictive control 用局部数据驱动的预测控制控制非线性
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI: 10.1016/j.ifacsc.2026.100396
Lorenzo Pasini, Mattia Bruschetta, Alessandro Chiuso
{"title":"Taming non-linearity with local data-driven predictive control","authors":"Lorenzo Pasini,&nbsp;Mattia Bruschetta,&nbsp;Alessandro Chiuso","doi":"10.1016/j.ifacsc.2026.100396","DOIUrl":"10.1016/j.ifacsc.2026.100396","url":null,"abstract":"<div><div>Model Predictive Control (MPC) relies on dynamical models to compute optimal control actions in a receding-horizon fashion. When accurate models are unavailable or expensive to obtain, data-driven predictive control (DDPC) offers a viable alternative for optimal control synthesis. While the linear case is now relatively well understood, both the theoretical foundations and practical implementations for nonlinear systems remain less developed. In this paper, we proposed an effective approach toward closing this gap by combining locally linear data-driven approximations that, when iteratively refined, yield a sequential data-driven quadratic programming algorithm for nonlinear DDPC. The proposed DDPC framework is validated through extensive numerical simulations on an inverted pendulum benchmark. In particular, we show that (i) as the amount of data increases, the closed-loop performance approaches that of a model-based MPC, (ii) our method compares favorably with a recently proposed nonlinear variant of Data Enabled Predictive Control (DeePC), and (iii) the algorithm can be effectively deployed online, achieving pendulum swing-up within a handful of trials, starting from small and easily collectable dataset.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100396"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384923","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
On continuous-time sparse identification of nonlinear polynomial systems 非线性多项式系统的连续时间稀疏辨识
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ifacsc.2026.100365
Mazen Alamir
{"title":"On continuous-time sparse identification of nonlinear polynomial systems","authors":"Mazen Alamir","doi":"10.1016/j.ifacsc.2026.100365","DOIUrl":"10.1016/j.ifacsc.2026.100365","url":null,"abstract":"<div><div>This paper leverages recent advances in high derivatives reconstruction from noisy-time series and sparse multivariate polynomial identification in order to improve the process of parsimoniously identifying, from a small amount of data, unknown Single-Input/Single-Output nonlinear dynamics of relative degree up to 4. The methodology is illustrated on the Electronic Throttle Controlled automotive system.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100365"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977346","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
Recursive identification for FIR systems with interval binary observations 区间二值观测FIR系统的递归辨识
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ifacsc.2026.100383
Xingrui Liu , Xin Li , Mingjie Shao , Yanlong Zhao
{"title":"Recursive identification for FIR systems with interval binary observations","authors":"Xingrui Liu ,&nbsp;Xin Li ,&nbsp;Mingjie Shao ,&nbsp;Yanlong Zhao","doi":"10.1016/j.ifacsc.2026.100383","DOIUrl":"10.1016/j.ifacsc.2026.100383","url":null,"abstract":"<div><div>This paper investigates the identification of finite impulse response systems based on interval binary observations, where only the information on whether the outputs lie within a prefixed interval is available. Unlike existing works that typically consider single-threshold binary observations, the distribution function of interval binary observations no longer exhibits monotonicity. This makes it difficult for most existing identification algorithms to ensure a unique solution at each recursive step. To overcome this challenge, a novel three-step recursive identification algorithm named the partition–convergence–decision algorithm is developed. First, to ensure that the distribution function remains monotonic, the parameter space is partitioned into multiple partition regions. Secondly, a recursive projection identification algorithm is executed in parallel across all partition regions. Finally, a recursive decision criterion based on the prediction errors of the interval binary observations is constructed to determine the partition region containing the true system parameter. Both the almost sure and the mean square convergence of the proposed recursive projection identification algorithm within the correct region are proved, and the effectiveness of the recursive decision criterion in the probabilistic sense is established. A simulation example is presented to validate the theoretical results.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100383"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173140","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 recursive parameter identification algorithm for nonlinear errors-in-variables models 非线性变量误差模型的递归参数辨识算法
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.ifacsc.2026.100381
Hugo Koide , Jérémy Vayssettes , Guillaume Mercère
{"title":"A recursive parameter identification algorithm for nonlinear errors-in-variables models","authors":"Hugo Koide ,&nbsp;Jérémy Vayssettes ,&nbsp;Guillaume Mercère","doi":"10.1016/j.ifacsc.2026.100381","DOIUrl":"10.1016/j.ifacsc.2026.100381","url":null,"abstract":"<div><div>Recursive identification of model parameters from data is an essential tool in adaptive signal processing and adaptive control applications. For general nonlinear regression problems where errors are present in both dependent and independent variables, the errors-in-variables (EIV) model is frequently employed to avoid a biased estimation of the system parameters. This work presents a recursive identification algorithm for nonlinear EIV models which is derived from an offline sequential quadratic programming solution. A proof of convergence is provided and the performance of the proposed algorithm is illustrated on benchmark simulation examples. The results demonstrate the numerical efficiency of the recursive algorithm, as well as its ability to track time-varying system parameters.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100381"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173693","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
Direct data-driven model-reference control for constrained systems 约束系统的直接数据驱动模型参考控制
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.ifacsc.2025.100355
Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga
{"title":"Direct data-driven model-reference control for constrained systems","authors":"Manas Mejari,&nbsp;Milad Banitalebi Dehkordi,&nbsp;Dario Piga","doi":"10.1016/j.ifacsc.2025.100355","DOIUrl":"10.1016/j.ifacsc.2025.100355","url":null,"abstract":"<div><div>A central challenge in direct data-driven control design is to ensure constraint satisfaction and safe operation of the closed-loop system while maintaining certain performance. To address this, we propose a hierarchical data-driven control architecture for constrained linear time-invariant systems to track a given setpoint reference. The inner-loop consists of a model reference controller (MRC) synthesized directly from the noisy data, which ensures performance by attempting to match a user-specified reference model. The outer-loop is a robust model predictive control (RMPC), acting as a safety pre-filter which optimally modifies the reference signal given to the inner loop MRC, ensuring constraint satisfaction and improving overall tracking performance. Additionally, the RMPC scheme accounts for a potential mismatch between the achieved closed-loop and the desired reference model, in the case of imperfect matching by the inner loop controller. The effectiveness of the method is demonstrated via a numerical example.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100355"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885139","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
The waterbed effect on quasiperiodic disturbance observer: Avoidance of sensitivity tradeoff with time delays 准周期扰动观测器的水床效应：避免时延下的灵敏度权衡
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1016/j.ifacsc.2026.100392
Hisayoshi Muramatsu
{"title":"The waterbed effect on quasiperiodic disturbance observer: Avoidance of sensitivity tradeoff with time delays","authors":"Hisayoshi Muramatsu","doi":"10.1016/j.ifacsc.2026.100392","DOIUrl":"10.1016/j.ifacsc.2026.100392","url":null,"abstract":"<div><div>In linear time-invariant systems, the sensitivity function to disturbances is designed under a sensitivity tradeoff known as the waterbed effect. To compensate for a quasiperiodic disturbance, a quasiperiodic disturbance observer using time delays was proposed. Its sensitivity function avoids the sensitivity tradeoff, achieving wideband harmonic suppression without amplifying aperiodic disturbances or shifting harmonic suppression frequencies. However, its open-loop transfer function is not rational and does not satisfy the assumptions of existing Bode sensitivity integrals due to its time delays. This paper provides Bode-like sensitivity integrals for the quasiperiodic disturbance observer in both continuous-time and discrete-time representations and clarifies the avoided sensitivity tradeoff with time delays.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100392"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384919","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
Pulmonary air distribution during abdominal and chest breathing 腹部和胸部呼吸时肺部空气分布
IF 1.8
IFAC Journal of Systems and Control Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ifacsc.2026.100364
B. Laufer , S. Krueger-Ziolek , K. Moeller
{"title":"Pulmonary air distribution during abdominal and chest breathing","authors":"B. Laufer ,&nbsp;S. Krueger-Ziolek ,&nbsp;K. Moeller","doi":"10.1016/j.ifacsc.2026.100364","DOIUrl":"10.1016/j.ifacsc.2026.100364","url":null,"abstract":"<div><div>Wider knowledge of air distribution within the lungs during breathing would lead to a better understanding of respiratory mechanics and could both; enhance the therapy of patients with lung diseases and increase oxygenation. Electrical impedance tomography (EIT), a motion capture system and a spirometer were used to investigate differences in pulmonary air distribution during abdominal and chest breathing in 11 sitting subjects. The subjects inhaled comparable tidal volumes while EIT was used to analyse air distribution in two layers on the upper body during abdominal and chest breathing.</div><div>The lower part of the lung was stronger ventilated during abdominal breathing, while the entire lung was ventilated more homogeneously during chest breathing. It is possible to influence the distribution of air within the lungs, and thus controlled ventilation of the lungs could improve the oxygen supply of patients and might increase the success of lung therapies through the targeted delivery of medication and controlled ventilation of damaged lung areas.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100364"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385003","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
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
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
{"title":"Periodic predictor of glucose dynamics in type 1 diabetes patients","authors":"Matteo Ragni,&nbsp;Paolo Alberto Mongini,&nbsp;Lalo Magni,&nbsp;Chiara Toffanin","doi":"10.1016/j.ifacsc.2026.100401","DOIUrl":"10.1016/j.ifacsc.2026.100401","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100401"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385007","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
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
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
{"title":"Sensitivity analysis of magnetic detection electrical impedance tomography: A numerical study","authors":"Alberto Battistel ,&nbsp;Ali Caglar Özen ,&nbsp;Dominik von Elverfeldt ,&nbsp;Knut Möller","doi":"10.1016/j.ifacsc.2026.100391","DOIUrl":"10.1016/j.ifacsc.2026.100391","url":null,"abstract":"<div><div>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, <span><math><mi>ρ</mi></math></span> the radial component of the magnetic field showed a signal sensitivity comparable to the one of standard EIT, while the <span><math><mi>z</mi></math></span> component was the largest in magnitude and the least affected by mesh-induced numerical artefacts.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100391"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385008","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
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
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
{"title":"Regularized GLISp for sensor-guided human-in-the-loop optimization","authors":"Matteo Cercola ,&nbsp;Michele Lomuscio ,&nbsp;Dario Piga ,&nbsp;Simone Formentin","doi":"10.1016/j.ifacsc.2026.100368","DOIUrl":"10.1016/j.ifacsc.2026.100368","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100368"},"PeriodicalIF":1.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022733","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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