IFAC Journal of Systems and Control最新文献

{"title":"Coevolution of actions and opinions in networks of coordinating and anti-coordinating agents","authors":"Hong Liang ,&nbsp;Mengbin Ye ,&nbsp;Lorenzo Zino ,&nbsp;Weiguo Xia","doi":"10.1016/j.ifacsc.2025.100325","DOIUrl":"10.1016/j.ifacsc.2025.100325","url":null,"abstract":"<div><div>In this paper, we investigate the dynamics of coordinating and anti-coordinating agents in a coevolutionary model for actions and opinions. In the model, individuals in a population interact on a two-layer network, sharing their opinions and observing others’ actions, while revising their own opinions and actions according to a game-theoretic mechanism grounded in social psychology literature. First, we consider the scenario of coordinating agents, where convergence to a Nash equilibrium (NE) is guaranteed. We identify conditions for reaching consensus configurations and establish regions of attraction for these equilibria. Second, we study networks of anti-coordinating agents. Here, we prove that all trajectories converge to an NE by leveraging potential game theory. Then, we establish analytical conditions on the network structure and model parameters to guarantee the existence of consensus and polarized equilibria, characterizing their regions of attraction.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100325"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654359","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}

{"title":"Slosh attenuation via Reference Governor-based adaptive control for space operations under actuators constraints","authors":"Guido Magnani ,&nbsp;Mario Cassaro ,&nbsp;Jean-Marc Biannic ,&nbsp;Hélène Evain ,&nbsp;Laurent Burlion","doi":"10.1016/j.ifacsc.2025.100321","DOIUrl":"10.1016/j.ifacsc.2025.100321","url":null,"abstract":"<div><div>This paper deals with satellite attitude control in the presence of actuator constraints and unmodeled disturbances. A model reference adaptive control law enhances the satellite control strategy to compensate for the perturbations and ensure precise user-defined closed-loop system performance. This result is consequently employed to build a scalar robust reference governor to enforce the system constraints with limited conservatism. The methodology is illustrated on a single-axis satellite in the presence of sloshing and flexible appendages disturbances, and equipped with reaction wheels subject to torque and angular momentum saturation.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100321"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321568","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}

{"title":"Impact of geometric irregularity and error on 3D surface reconstruction in Digital Imaging Elasto-Tomography System","authors":"Yuwei Sun,&nbsp;Samantha Couper,&nbsp;Cong Zhou,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2025.100332","DOIUrl":"10.1016/j.ifacsc.2025.100332","url":null,"abstract":"<div><div>High-accuracy three-dimensional (3-D) breast-surface reconstruction is pivotal for vibration-based diagnostic modalities, such as the Digital Imaging Elasto-Tomography (DIET) breast cancer screening technology. Yet the coupled influence of geometric complexity and measurement noise in structured-light systems has not been quantified rigorously in this type of application. To address this gap, we establish a dual-track evaluation framework combining ground-truth numerical simulations with laser-scanned silicone phantom data to assess reconstruction fidelity under clinically relevant conditions.</div><div>Analytically defined hemispherical point clouds, both regular and irregular, were synthesised and corrupted with random and periodic noise of varying amplitudes, creating a benchmark data set with explicit, known ground truth. In parallel, anatomically realistic silicone breast phantoms without internal, high stiffness tumour-mimicking inclusions were imaged using a 39-beam, 520 nm structured-light system. Approximately 70 calibration poses were acquired per laser line, yielding sub-millimetre accuracy. All data were processed within a spherical-polar parameterisation and optimised by a Levenberg–Marquardt scheme. Reconstruction error was quantified via root-mean-square error (RMSE), inter-quartile range (IQR), outlier counts, and multiple visualisations (histograms, spatial error maps, and boxplots).</div><div>Results reveal a clear performance hierarchy. Under noise-free conditions the simulated hemisphere shows the lowest RMSE, whereas sparse sampling in the phantoms causes local surface depressions. Systematic (periodic) noise dominates the error budget, increasing RMSE by an order of magnitude and producing persistent ring- or band-shaped artefacts across all reconstructions. Random noise mainly introduces local high-frequency roughness. Concave deformations generate a stable toroidal residual whose contribution to global RMSE remains below 10 % in the absence of noise. When geometric irregularities and noise coexist, the resulting errors greatly exceed those due to shape alone, underscoring noise suppression as the primary determinant of best attainable accuracy in this application.</div><div>The proposed simulation-and-phantom framework delivers the first comprehensive error map for structured-light breast reconstruction in a DIET system and indicates future algorithms must integrate curvature-adaptive modelling with explicit compensation for periodic perturbations to remain robust across the wide spectrum of breast sizes and shapes encountered in clinical practice.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100332"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907139","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}

{"title":"Stochastic virtual patient-guided mechanical ventilation treatment: A virtual patient study with mechanical power consideration","authors":"Christopher Yew Shuen Ang ,&nbsp;Yeong Shiong Chiew ,&nbsp;Xin Wang ,&nbsp;Ean Hin Ooi ,&nbsp;Mohd Basri Mat Nor ,&nbsp;Matthew E. Cove ,&nbsp;Cong Zhou ,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2025.100334","DOIUrl":"10.1016/j.ifacsc.2025.100334","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: Computerised decision support systems (CDSS) in mechanical ventilation (MV) provide individualised, closed-loop treatment but often require extensive input parameters, which are challenging to obtain continuously in clinical settings. Many also fail to incorporate mechanical power (<em>MP</em>) and <em>MP ratio</em> — recently identified as significant predictors of patient outcomes. This study introduces the Stochastic Virtual Patient Ventilation Protocol (SVP VENT), a model-based CDSS addressing these limitations.</div></div><div><h3>Methods</h3><div>: The SVP VENT Protocol integrates a stochastic virtual patient model to predict temporal lung elastance, <span><math><msub><mrow><mi>E</mi></mrow><mrow><mi>r</mi><mi>s</mi></mrow></msub></math></span>, trends and deliver closed-loop, lung protective ventilation minimising <em>MP ratio</em> and driving pressure. The protocol was validated against the VENT and SiVENT protocols using an established virtual patient platform comprising over 1229 h of both volume control (VC) and pressure control (PC) retrospective MV data. Patient responses were monitored to ensure adherence to accepted clinical safety guidelines.</div></div><div><h3>Results</h3><div>: The SVP VENT protocol consistently outperformed retrospective clinical data, VENT and SiVENT protocols in ensuring adherence to clinical safety metrics, achieving an all-adherence rate of <span><math><mo>∼</mo></math></span>57% and <span><math><mo>∼</mo></math></span>67% for the VC and PC cohorts, respectively. Across cohorts, the protocol maintained <em>MP</em> and <em>MP ratio</em> levels below safety thresholds (12 J/min and 4.5, respectively), and extended intervention intervals up to 3 h, potentially reducing clinical workload.</div></div><div><h3>Conclusion</h3><div>: Overall, the virtual trial demonstrates the SVP VENT protocol’s potential to enhance MV management by extending intervention intervals, while maintaining patient safety. These findings support initial clinical trials to evaluate the protocol’s impact on clinical workload and patient safety over prolonged monitoring periods, facilitating its integration into standard clinical practices.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100334"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917878","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}

{"title":"An analysis of an LQR design for a hybrid power plant’s load-sharing problem","authors":"Joni Vasara,&nbsp;István Selek","doi":"10.1016/j.ifacsc.2025.100327","DOIUrl":"10.1016/j.ifacsc.2025.100327","url":null,"abstract":"<div><div>This paper analyzes the design of a Linear Quadratic Regulator (LQR) for a hybrid power plant providing Frequency Containment Reserve (FCR). The analysis focuses on the selection of the LQR’s weighting coefficients aiming to achieve the highest possible turbine stress reduction by distributing the hybrid plant’s gross power between a turbine and energy storage. In this study, the LQR was formulated to approximate the constrained optimization problem related to load sharing. To find the weighting coefficients of the LQR, an approach is presented that utilizes the optimal solution of the load-sharing problem. The evaluation revealed that the weight selection principle can be presented in an analytic form containing the important parameters related to the hybrid plant operation. The analytic solution was validated with numerical methods. The simulations verified that the best attainable turbine stress reduction can be obtained with the proposed design.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100327"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685771","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}

{"title":"Reinforcement Learning based temperature regulation for a Material Extrusion Additive Manufacturing system","authors":"Eleni Zavrakli ,&nbsp;Andrew Parnell ,&nbsp;Subhrakanti Dey","doi":"10.1016/j.ifacsc.2025.100330","DOIUrl":"10.1016/j.ifacsc.2025.100330","url":null,"abstract":"<div><div>With the rapid development of Additive Manufacturing (AM) comes an urgent need for advanced monitoring and control of the process. Many aspects of the AM process play a significant role in the efficiency, accuracy, and repeatability of the process, with temperature regulation being one of the most important. In this work, we solve the problem of optimal tracking control for a state space temperature model of a Big Area Additive Manufacturing (BAAM) system. In particular, we address the problem of designing a Linear Quadratic Tracking (LQT) controller when access to the exact system state is not possible, except in the form of measurements. We initially solve the problem with a model-based approach based on reinforcement learning concepts with state estimation through an observer. We then design a model-free reinforcement-learning based controller with an internal state estimation step and demonstrate its performance through a simulator of the systems’ behaviour. In addition, we explore the use of Bayesian Optimisation as a means to optimise the design parameters of the LQT problem. Our results showcase the possibility of achieving optimal behaviour while learning directly from process data, independently of a model of the process. This is an encouraging outcome towards the realisation of intelligent manufacturing.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100330"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810510","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}

{"title":"Adaptive command-filtered control for nonlinear systems with actuator faults and unknown control directions","authors":"Chenxuan Sheng ,&nbsp;Guobao Liu ,&nbsp;Huai Liu ,&nbsp;Siyu Xia","doi":"10.1016/j.ifacsc.2025.100318","DOIUrl":"10.1016/j.ifacsc.2025.100318","url":null,"abstract":"<div><div>This paper focuses on the issue of adaptive fuzzy control for nonlinear systems with actuator faults and unknown control directions. Command filtering techniques are integrated with the Nussbaum function to address the issue of complexity explosion and to compensate for the effects of unknown control directions. This approach effectively compensates for filtering errors. In the presence of actuator faults and disturbances, an actuator fault compensation auxiliary system is designed to resolve the signal mismatch between the controller and the actuator. By utilizing Lyapunov stability theory and backstepping control methods, it is proven that the tracking error can ultimately converge to a small neighborhood near the origin. Furthermore, in accordance with the control design, all signals within the system remain bounded. Finally, the effectiveness of this control strategy can be validated through simulations of a circuit containing a nonlinear controlled source and nonlinear pendulum model.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100318"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313353","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}

{"title":"Predictive energy scheduling of smart parking infrastructure with solar-powered electric vehicles","authors":"Saba Askari Noghani,&nbsp;Paolo Scarabaggio,&nbsp;Raffaele Carli,&nbsp;Mariagrazia Dotoli","doi":"10.1016/j.ifacsc.2025.100322","DOIUrl":"10.1016/j.ifacsc.2025.100322","url":null,"abstract":"<div><div>This paper presents a novel model predictive control framework for managing energy flow in smart parking infrastructures with renewable energy facilities, electric vehicles, and solar-powered electric vehicles. The proposed control framework minimizes the energy costs for the parking lot operators, ensuring the user-defined charge levels for vehicles at departure, and protecting the charging infrastructure during operation. Field validation on Lonsdale Street, Melbourne (Australia)—using real data on vehicle behavior, solar irradiance, and energy prices—shows significant grid load reduction even with partial solar production. Compared to a rule-based strategy, the MPC approach reduces operational costs by 15.32% and energy demand by 6.12%. Lastly, we show that the proposed framework is robust under forecast uncertainty, supporting its practical deployment in dynamic real-world environments.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"33 ","pages":"Article 100322"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724955","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}

{"title":"Prescribed-time fault-tolerant consensus for uncertain nonlinear multi-agent systems","authors":"Vijay Kumar Singh,&nbsp;Jagannathan Sarangapani","doi":"10.1016/j.ifacsc.2025.100313","DOIUrl":"10.1016/j.ifacsc.2025.100313","url":null,"abstract":"<div><div>Achieving consensus within a user-defined time frame for uncertain nonlinear systems is both crucial and challenging. To tackle this issue, we propose an adaptive consensus protocol that utilizes a radial basis function neural network to handle unknown nonlinearities and actuator faults. Unlike traditional finite-time or fixed-time consensus methods, our approach employs continuous, time-varying feedback to guarantee convergence within the desired time. The proposed strategy ensures that all closed-loop signals of the system remain bounded, achieving consensus within the prescribed time. The effectiveness of the proposed control strategy is demonstrated through a simulation example of phase synchronization in a power system.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100313"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943562","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}

{"title":"Robust adaptive maximum-entropy linear quadratic regulator","authors":"Ahmed Kamel,&nbsp;Ramin Esmzad,&nbsp;Nariman Niknejad,&nbsp;Hamidreza Modares","doi":"10.1016/j.ifacsc.2025.100305","DOIUrl":"10.1016/j.ifacsc.2025.100305","url":null,"abstract":"<div><div>Balancing the trade-off between venturing into unknowns (exploration for learning) and optimizing outcomes within familiar grounds (exploitation for performance delivery) is a longstanding challenge in learning-enabled control systems. This is specifically challenging when the learning process starts with no data and rich data must be collected from the closed-loop system. This is in sharp contrast to the standard practice in data-driven control that assumes the availability of a priori rich collected open-loop data. To ensure that the closed-loop system delivers acceptable performance despite exploration for rich data collection in the context of linear quadratic regulator (LQR), we first formalize a linear matrix inequality (LMI) solution for an LQR problem that is regularized by the control entropy. Given available side information (e.g., a set that system parameters belong to), a conservative solution to the LQR can be found. To reduce the conservatism over time while ensuring an acceptable performance during learning, we present a set membership closed-loop system identification and integrate it with side information in solving the entropy-regularized LQR through Schur complement, along with the lossy S-procedure. We show that the presented set membership approach progressively improves the entropy-regularized LQR cost by shrinking the size of the set of system parameters. We also show that this is achieved while guaranteeing acceptable performance. An iterative algorithm is presented using the closed-loop set membership learning to progressively learn a new improved controller after every online data sample is collected by applying the current learned control policy. Simulation examples are provided to verify the effectiveness of the presented results.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"32 ","pages":"Article 100305"},"PeriodicalIF":1.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739293","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}