Pub Date : 2025-12-11DOI: 10.1016/j.ifacsc.2025.100352
Chris Verhoek , Ivan Markovsky , Roland Tóth
We consider the problem of estimating missing values in trajectories of linear parameter-varying (LPV) systems. We solve this interpolation problem for the class of shifted-affine LPV systems. Conditions for the existence and uniqueness of solutions are given and a direct data-driven algorithm for its computation is presented, i.e., the data-generating system is not given by a parametric model but is implicitly specified by data. We illustrate the applicability of the proposed solution on illustrative examples of a mass–spring-damper system with exogenous and endogenous parameter variation.
{"title":"Direct data-driven interpolation and approximation of linear parameter-varying system trajectories","authors":"Chris Verhoek , Ivan Markovsky , Roland Tóth","doi":"10.1016/j.ifacsc.2025.100352","DOIUrl":"10.1016/j.ifacsc.2025.100352","url":null,"abstract":"<div><div>We consider the problem of estimating missing values in trajectories of <em>linear parameter-varying</em> (LPV) systems. We solve this <em>interpolation</em> problem for the class of shifted-affine LPV systems. Conditions for the existence and uniqueness of solutions are given and a direct data-driven algorithm for its computation is presented, i.e., the data-generating system is not given by a parametric model but is implicitly specified by data. We illustrate the applicability of the proposed solution on illustrative examples of a mass–spring-damper system with exogenous and endogenous parameter variation.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100352"},"PeriodicalIF":1.8,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791729","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}
Pub Date : 2025-12-03DOI: 10.1016/j.ifacsc.2025.100351
Lui Holder-Pearson , J. Geoffrey Chase , Yeong Shiong Chiew , Geoffrey Shaw , Bernard Lambermont , Thomas Desaive
Acute respiratory distress and respiratory disease often require patients be treated with mechanical ventilation (MV) and thus place extreme demand on intensive care units (ICUs). This burden can be unsustainably high in some periods, and particularly during pandemics, such as Covid-19. In low resource regions and countries, the result can be inequity, a problem addressable via simple technological innovation. Ventilator sharing over two or more patients has been proposed but strongly discouraged because it could not treat different patient needs and hindered individual patient monitoring. However, all these approaches ventilated patients in-parallel, each breathing at the same time.
A simple switching valve enables series breathing, one patient after the other. External, low-cost, and reusable sensor arrays enable individual monitoring, while low-cost adjustable pressure reducing valves allow pressure to be fully customised across two patients. This study uses an experimental test lung to experimentally demonstrate and validate the ability of such a system to balance ventilation across 2 simulated patients with very different lung compliances.
A method is presented to achieve equal tidal volumes in two lungs with differing compliances of 0.10 L cmH 2O−1 and 0.05 L cmH 2O−1. This goal requires driving and end-expiratory pressures of at least 20 cmH 2O, which are clinically relatively high. The approach prioritises safety, ensuring more compliant lung is not over-ventilated during the process, reducing the risk of ventilator-induced lung injury (VILI). The system is compatible with different ventilators, and cost-effectively fabricated in low-resource settings. Strategies addressing key safety concerns, such as cross-contamination, sterilisation, and ventilator configuration, are also presented.
急性呼吸窘迫和呼吸系统疾病通常需要患者进行机械通气（MV）治疗，因此对重症监护病房（icu）提出了极高的要求。在某些时期，特别是在Covid-19等大流行期间，这种负担可能高得不可持续。在资源匮乏的地区和国家，结果可能是不平等，这个问题可以通过简单的技术创新来解决。两名或两名以上患者共用呼吸机已被提议，但强烈反对，因为它不能满足不同患者的需求，并阻碍了患者的个体监测。然而，所有这些方法都是平行的，每次呼吸都是同时进行的。一个简单的开关阀可以实现病人一个接一个的连续呼吸。外部、低成本和可重复使用的传感器阵列可以实现个人监测，而低成本的可调减压阀可以完全定制两个患者的压力。本研究通过实验测试肺，实验证明并验证了该系统在两个肺顺应性差异很大的模拟患者中平衡通气的能力。提出了一种方法，以实现相等的潮汐体积在两个肺不同的顺应性0.10 L cmh2o−1和0.05 L cmh2o−1。这一目标要求驱动和呼气末压力至少为20 cmh2o，这在临床上是相对较高的。该方法优先考虑安全性，确保更适应的肺在通气过程中不会过度通气，降低呼吸机诱导肺损伤（VILI）的风险。该系统与不同的呼吸机兼容，并且在低资源环境下具有成本效益。还提出了解决关键安全问题的策略，例如交叉污染，灭菌和呼吸机配置。
{"title":"Experimental Validation of the ACTIV Multi-Patient Mechanical Ventilation System","authors":"Lui Holder-Pearson , J. Geoffrey Chase , Yeong Shiong Chiew , Geoffrey Shaw , Bernard Lambermont , Thomas Desaive","doi":"10.1016/j.ifacsc.2025.100351","DOIUrl":"10.1016/j.ifacsc.2025.100351","url":null,"abstract":"<div><div>Acute respiratory distress and respiratory disease often require patients be treated with mechanical ventilation (MV) and thus place extreme demand on intensive care units (ICUs). This burden can be unsustainably high in some periods, and particularly during pandemics, such as Covid-19. In low resource regions and countries, the result can be inequity, a problem addressable via simple technological innovation. Ventilator sharing over two or more patients has been proposed but strongly discouraged because it could not treat different patient needs and hindered individual patient monitoring. However, all these approaches ventilated patients in-parallel, each breathing at the same time.</div><div>A simple switching valve enables series breathing, one patient after the other. External, low-cost, and reusable sensor arrays enable individual monitoring, while low-cost adjustable pressure reducing valves allow pressure to be fully customised across two patients. This study uses an experimental test lung to experimentally demonstrate and validate the ability of such a system to balance ventilation across 2 simulated patients with very different lung compliances.</div><div>A method is presented to achieve equal tidal volumes in two lungs with differing compliances of 0.10 L cmH <sub>2</sub>O<sup>−1</sup> and 0.05 L cmH <sub>2</sub>O<sup>−1</sup>. This goal requires driving and end-expiratory pressures of at least 20 cmH <sub>2</sub>O, which are clinically relatively high. The approach prioritises safety, ensuring more compliant lung is not over-ventilated during the process, reducing the risk of ventilator-induced lung injury (VILI). The system is compatible with different ventilators, and cost-effectively fabricated in low-resource settings. Strategies addressing key safety concerns, such as cross-contamination, sterilisation, and ventilator configuration, are also presented.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100351"},"PeriodicalIF":1.8,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697832","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}
Pub Date : 2025-12-03DOI: 10.1016/j.ifacsc.2025.100350
Rami Katz , Giulia Giordano , Dmitry Batenkov
Given a reaction–diffusion equation with unknown right-hand side, we consider the nonlinear inverse problem of estimating the associated leading eigenvalues and initial condition Fourier coefficients from a finite number of non-local noisy measurements. We define a reconstruction (i.e., estimation) criterion and, for small enough noise, we prove existence and uniqueness of the desired estimates. We derive closed-form expressions for the first-order condition numbers and bounds for their asymptotic behavior in a regime when the number of measured samples is fixed and the inter-sampling interval length is arbitrarily large. When computing the sought estimates numerically, our simulations show that the exponential fitting algorithm ESPRIT is first-order optimal, since its first-order condition numbers have the same asymptotic behavior as the analytic condition numbers in the considered regime.
{"title":"Identification of reaction–diffusion systems from finitely many non-local noisy measurements via exponential fitting","authors":"Rami Katz , Giulia Giordano , Dmitry Batenkov","doi":"10.1016/j.ifacsc.2025.100350","DOIUrl":"10.1016/j.ifacsc.2025.100350","url":null,"abstract":"<div><div>Given a reaction–diffusion equation with unknown right-hand side, we consider the nonlinear inverse problem of estimating the associated leading eigenvalues and initial condition Fourier coefficients from a finite number of non-local noisy measurements. We define a reconstruction (i.e., estimation) criterion and, for small enough noise, we prove existence and uniqueness of the desired estimates. We derive closed-form expressions for the first-order condition numbers and bounds for their asymptotic behavior in a regime when the number of measured samples is fixed and the inter-sampling interval length is arbitrarily large. When computing the sought estimates numerically, our simulations show that the exponential fitting algorithm ESPRIT is first-order optimal, since its first-order condition numbers have the same asymptotic behavior as the analytic condition numbers in the considered regime.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100350"},"PeriodicalIF":1.8,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145697833","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}
This study proposes a dual-rate, data-driven system for automated ergometer load adjustment using Heart Rate (HR) and Heart Rate Variability (HRV). The system continuously collects HR and HRV data during exercise to estimate the user’s real-time physiological state and dynamically adjust resistance, maintaining exercise intensity tailored to individual responses. Validation with human participants demonstrated improved HRV without compromising HR tracking performance, highlighting the potential of this approach for personalized training in clinical rehabilitation, athlete conditioning, and general fitness.
{"title":"Motion data-driven exercise design for the simultaneous enhancement of physical capability and psychological state","authors":"Takao Sato, Yoshiharu Kawahara, Natsuki Kawaguchi, Yusuke Tsunoda","doi":"10.1016/j.ifacsc.2025.100349","DOIUrl":"10.1016/j.ifacsc.2025.100349","url":null,"abstract":"<div><div>This study proposes a dual-rate, data-driven system for automated ergometer load adjustment using Heart Rate (HR) and Heart Rate Variability (HRV). The system continuously collects HR and HRV data during exercise to estimate the user’s real-time physiological state and dynamically adjust resistance, maintaining exercise intensity tailored to individual responses. Validation with human participants demonstrated improved HRV without compromising HR tracking performance, highlighting the potential of this approach for personalized training in clinical rehabilitation, athlete conditioning, and general fitness.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"35 ","pages":"Article 100349"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738701","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}
Pub Date : 2025-11-10DOI: 10.1016/j.ifacsc.2025.100348
Xinwei Sun , Lei Zhang
Investigating vibration signals in complex electromechanical systems is essential for improving system stability and control performance. This study proposes a data–physics dual-driven framework to model the dynamic coupling between suspension current and levitation gap in maglev systems. A joint time–frequency analysis is first conducted using Fourier transform, ripple coefficient evaluation, and hysteresis correlation to quantify nonlinear coupling strength and identify a positively lagged relationship between current and gap. To capture this effect, we develop a physics-informed neural network (PINN) that integrates a lag compensation module, embeds electromagnetic equations as physical constraints, and employs an LSTM architecture for end-to-end vibration signal prediction. Unlike conventional approaches that design neural controllers from a control perspective, our method focuses on learning intrinsic coupling patterns directly from real-world operational data. This data-informed modeling approach, enhanced with time-delay compensation and physical consistency, enables accurate prediction of dynamic responses under realistic disturbances. Experiments on data from the Changsha medium-low-speed maglev train show that our model achieves the lowest MAE and RMSE compared to standard PINNs and purely data-driven baselines. It also responds rapidly to gap changes, with a response time of 0.167 ms, making it suitable for real-time maglev control applications. The implementation code is available at: https://github.com/sunning2024/RPinn.
{"title":"A physics-informed LSTM framework with lag compensation for coupled vibration signal modeling","authors":"Xinwei Sun , Lei Zhang","doi":"10.1016/j.ifacsc.2025.100348","DOIUrl":"10.1016/j.ifacsc.2025.100348","url":null,"abstract":"<div><div>Investigating vibration signals in complex electromechanical systems is essential for improving system stability and control performance. This study proposes a data–physics dual-driven framework to model the dynamic coupling between suspension current and levitation gap in maglev systems. A joint time–frequency analysis is first conducted using Fourier transform, ripple coefficient evaluation, and hysteresis correlation to quantify nonlinear coupling strength and identify a positively lagged relationship between current and gap. To capture this effect, we develop a physics-informed neural network (PINN) that integrates a lag compensation module, embeds electromagnetic equations as physical constraints, and employs an LSTM architecture for end-to-end vibration signal prediction. Unlike conventional approaches that design neural controllers from a control perspective, our method focuses on learning intrinsic coupling patterns directly from real-world operational data. This data-informed modeling approach, enhanced with time-delay compensation and physical consistency, enables accurate prediction of dynamic responses under realistic disturbances. Experiments on data from the Changsha medium-low-speed maglev train show that our model achieves the lowest MAE and RMSE compared to standard PINNs and purely data-driven baselines. It also responds rapidly to gap changes, with a response time of 0.167 ms, making it suitable for real-time maglev control applications. The implementation code is available at: <span><span>https://github.com/sunning2024/RPinn</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100348"},"PeriodicalIF":1.8,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568440","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}
Pub Date : 2025-10-20DOI: 10.1016/j.ifacsc.2025.100341
Javier Urquizo, Patricia Pasmay, Luis Muñoz, Luis Galarza
Permanent magnet synchronous motors play a critical role in modern applications, particularly in the electrification of transportation. Their high energy efficiency and ability to maintain constant power over a wide speed range make them ideal for high-speed trains and electric vehicles. This research explores advanced control strategies, including Field oriented control (FOC), voltage droop control (Vdroop), and dispatchable virtual oscillator control (dVOC), implemented using the Texas Instruments microcontroller development kit, the Boost inverter, and the conventional platform. Furthermore, supervised machine learning algorithms such as support vector machine and reinforcement learning to learn the optimal action-selection policy for an agent interacting with an environment, such as Q-Learning. Large Language Model Meta Artificial Intelligence instruct (LLAMA3) is employed to dynamically optimize control strategies. Laboratory tests validate the implementation, focusing on system efficiency, adaptability, and stability under varying operating conditions. Our findings highlight the potential of artificial intelligence (AI) selected control methods over traditional strategies to deliver optimal performance for modern Permanent magnet synchronous motor.
{"title":"Model-based and Large Language Model Meta Artificial Intelligence techniques for intelligent permanent magnet synchronous motor drive control","authors":"Javier Urquizo, Patricia Pasmay, Luis Muñoz, Luis Galarza","doi":"10.1016/j.ifacsc.2025.100341","DOIUrl":"10.1016/j.ifacsc.2025.100341","url":null,"abstract":"<div><div>Permanent magnet synchronous motors play a critical role in modern applications, particularly in the electrification of transportation. Their high energy efficiency and ability to maintain constant power over a wide speed range make them ideal for high-speed trains and electric vehicles. This research explores advanced control strategies, including Field oriented control (FOC), voltage droop control (Vdroop), and dispatchable virtual oscillator control (dVOC), implemented using the Texas Instruments microcontroller development kit, the Boost inverter, and the conventional platform. Furthermore, supervised machine learning algorithms such as support vector machine and reinforcement learning to learn the optimal action-selection policy for an agent interacting with an environment, such as Q-Learning. Large Language Model Meta Artificial Intelligence instruct (LLAMA3) is employed to dynamically optimize control strategies. Laboratory tests validate the implementation, focusing on system efficiency, adaptability, and stability under varying operating conditions. Our findings highlight the potential of artificial intelligence (AI) selected control methods over traditional strategies to deliver optimal performance for modern Permanent magnet synchronous motor.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100341"},"PeriodicalIF":1.8,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363461","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}
Pub Date : 2025-10-06DOI: 10.1016/j.ifacsc.2025.100345
Ella F.S. Guy , Jennifer L. Knopp , Lui R. Holder-Pearson , J. Geoffrey Chase
Background and Objective:
Feasible methods to assess respiratory compliance and airway resistance without requiring clinical effort or interrupting normal breathing patterns would decrease the high burden of respiratory testing on healthcare systems. This study aims to provide proof of concept of a novel rapid expiratory occlusion (REO) test in a healthy adult population.
Methods:
REO test data was collected for unassisted spontaneous breaths and a PEEP challenge in N=80 healthy adults. Model-identified compliance and resistance values are compared to physiological expectations and literature.
Results:
Median [min, max] compliance was 0.506 [0.199, 1.562] cmH 2O−1L, and resistance was 1.777 [0.811 2.478] cmH 2OL−1s in initial spontaneous breathing, matching expectations. When PEEP was applied compliance decreased (independent of PEEP level) and resistance increased (proportional to set PEEP).
Conclusions:
This study established proof-of-concept efficacy for a model-based REO method identifying compliance and resistance, and informs device development and testing for clinical populations.
{"title":"Identification of passive respiratory mechanics using Rapid Expiratory Occlusions (REOs)","authors":"Ella F.S. Guy , Jennifer L. Knopp , Lui R. Holder-Pearson , J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2025.100345","DOIUrl":"10.1016/j.ifacsc.2025.100345","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Feasible methods to assess respiratory compliance and airway resistance without requiring clinical effort or interrupting normal breathing patterns would decrease the high burden of respiratory testing on healthcare systems. This study aims to provide proof of concept of a novel rapid expiratory occlusion (REO) test in a healthy adult population.</div></div><div><h3>Methods:</h3><div>REO test data was collected for unassisted spontaneous breaths and a PEEP challenge in N=80 healthy adults. Model-identified compliance and resistance values are compared to physiological expectations and literature.</div></div><div><h3>Results:</h3><div>Median [min, max] compliance was 0.506 [0.199, 1.562] cmH <sub>2</sub>O<sup>−1</sup>L, and resistance was 1.777 [0.811 2.478] cmH <sub>2</sub>OL<sup>−1</sup>s in initial spontaneous breathing, matching expectations. When PEEP was applied compliance decreased (independent of PEEP level) and resistance increased (proportional to set PEEP).</div></div><div><h3>Conclusions:</h3><div>This study established proof-of-concept efficacy for a model-based REO method identifying compliance and resistance, and informs device development and testing for clinical populations.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100345"},"PeriodicalIF":1.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268212","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}
Pub Date : 2025-10-06DOI: 10.1016/j.ifacsc.2025.100346
Hassan Ouabi, Rachid Lajouad, Mohammed Kissaoui, Abdelmounime El Magri
This study proposes an advanced multi-mode control strategy for a stand-alone photovoltaic (PV) system equipped with a Li-ion battery. The system is designed to cope with weather fluctuations and varying load demands, which can affect battery lifespan and charging efficiency. The proposed multimode control strategy dynamically switches between three modes: Maximum Power Point Tracking (MPPT) maximizes energy extraction under low PV generation, Constant Current (CC) ensuring fast battery charging, and Constant Voltage (CV) to preserve battery health during saturation. An Artificial Neural Network (ANN) is implemented to adaptively generate the PV reference voltage, enhancing system responsiveness to environmental changes. Furthermore, a state observer is designed to deliver accurate values of all battery states like battery’s state of charge (SoC), ensuring optimized performance, longevity, and safety. The effectiveness of the proposed control strategy and observer is validated through MATLAB/Simulink simulations. Finally, a semi-experimental study based on Processor-in-the-Loop (PIL) testing with the eZdsp TMS320F28335 board confirms the robustness and reliability of the system under real operating conditions.
{"title":"Robust control and state of charge estimation for off-grid solar power systems using ANN-based reference voltage generation","authors":"Hassan Ouabi, Rachid Lajouad, Mohammed Kissaoui, Abdelmounime El Magri","doi":"10.1016/j.ifacsc.2025.100346","DOIUrl":"10.1016/j.ifacsc.2025.100346","url":null,"abstract":"<div><div>This study proposes an advanced multi-mode control strategy for a stand-alone photovoltaic (PV) system equipped with a Li-ion battery. The system is designed to cope with weather fluctuations and varying load demands, which can affect battery lifespan and charging efficiency. The proposed multimode control strategy dynamically switches between three modes: Maximum Power Point Tracking (MPPT) maximizes energy extraction under low PV generation, Constant Current (CC) ensuring fast battery charging, and Constant Voltage (CV) to preserve battery health during saturation. An Artificial Neural Network (ANN) is implemented to adaptively generate the PV reference voltage, enhancing system responsiveness to environmental changes. Furthermore, a state observer is designed to deliver accurate values of all battery states like battery’s state of charge (SoC), ensuring optimized performance, longevity, and safety. The effectiveness of the proposed control strategy and observer is validated through MATLAB/Simulink simulations. Finally, a semi-experimental study based on Processor-in-the-Loop (PIL) testing with the eZdsp TMS320F28335 board confirms the robustness and reliability of the system under real operating conditions.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100346"},"PeriodicalIF":1.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268210","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}
This study examines a scenario in which individuals, each belonging to a specific type of group (e.g., organizations), are faced with a two-alternative decision-making task. This decision problem is modeled using a novel mixed logit dynamics incorporating conformity biases and committed minority. The model defines two types of conformity biases: internal bias, referred to as inertia, and external bias, referred to as social coordination. Inertia leads group members to adhere to their own status quo, while social coordination drives individuals toward the social majority. We analyze the social model from a control theoretical perspective, proving that social quasi-consensus is stimulated by committed minorities under a bounded rationality condition. In addition to the theoretical results, hypotheses based on the results are validated through numerical experiments.
{"title":"Stability analysis of mixed logit dynamics with internal/external conformity biases and committed minority","authors":"Tatsuya Miyano , Yuji Ito , Daisuke Inoue , Takeshi Hatanaka","doi":"10.1016/j.ifacsc.2025.100343","DOIUrl":"10.1016/j.ifacsc.2025.100343","url":null,"abstract":"<div><div>This study examines a scenario in which individuals, each belonging to a specific type of group (e.g., organizations), are faced with a two-alternative decision-making task. This decision problem is modeled using a novel mixed logit dynamics incorporating conformity biases and committed minority. The model defines two types of conformity biases: internal bias, referred to as inertia, and external bias, referred to as social coordination. Inertia leads group members to adhere to their own status quo, while social coordination drives individuals toward the social majority. We analyze the social model from a control theoretical perspective, proving that social quasi-consensus is stimulated by committed minorities under a bounded rationality condition. In addition to the theoretical results, hypotheses based on the results are validated through numerical experiments.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100343"},"PeriodicalIF":1.8,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221230","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}
Pub Date : 2025-10-03DOI: 10.1016/j.ifacsc.2025.100344
Enes Bajrami, Andrea Kulakov, Eftim Zdravevski, Petre Lameski
Background:
This study addresses national-scale energy optimization using deep reinforcement learning. Unlike prior works that rely on simulated environments or synthetic datasets, this research integrates real-world energy indicators, including electricity generation, greenhouse gas emissions, renewable energy share, fossil fuel dependency, and oil consumption. These indicators, sourced from the World Energy Consumption dataset, capture both developed and developing energy systems, enabling the evaluation of intelligent control policies across diverse contexts.
Methodology:
Two advanced algorithms, Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), were implemented and trained using PyTorch across multi-phase evaluation runs (300–3000 episodes). Comparative performance analysis was conducted on key metrics: execution speed, action consistency, and reward optimization. A secondary regional analysis focused on contrasting the Balkan and Nordic countries to evaluate algorithm adaptability between highly developed and developing energy infrastructures.
Significant findings:
SAC demonstrated superior computational throughput and policy stability, making it suitable for real-time and resource-constrained environments. PPO exhibited stronger action magnitudes, enabling more assertive control signals for high-impact interventions. Both agents significantly outperformed a rule-based baseline in responsiveness and adaptability. The proposed framework represents a novel contribution by combining deep reinforcement learning with interpretable, country-level energy indicators. Future work will extend the evaluation to additional continents, including Asia, Africa, and South America, to assess global scalability and applicability.
{"title":"A comparative analysis of PPO and SAC algorithms for energy optimization with country-level energy consumption insights","authors":"Enes Bajrami, Andrea Kulakov, Eftim Zdravevski, Petre Lameski","doi":"10.1016/j.ifacsc.2025.100344","DOIUrl":"10.1016/j.ifacsc.2025.100344","url":null,"abstract":"<div><h3>Background:</h3><div>This study addresses national-scale energy optimization using deep reinforcement learning. Unlike prior works that rely on simulated environments or synthetic datasets, this research integrates real-world energy indicators, including electricity generation, greenhouse gas emissions, renewable energy share, fossil fuel dependency, and oil consumption. These indicators, sourced from the World Energy Consumption dataset, capture both developed and developing energy systems, enabling the evaluation of intelligent control policies across diverse contexts.</div></div><div><h3>Methodology:</h3><div>Two advanced algorithms, Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), were implemented and trained using PyTorch across multi-phase evaluation runs (300–3000 episodes). Comparative performance analysis was conducted on key metrics: execution speed, action consistency, and reward optimization. A secondary regional analysis focused on contrasting the Balkan and Nordic countries to evaluate algorithm adaptability between highly developed and developing energy infrastructures.</div></div><div><h3>Significant findings:</h3><div>SAC demonstrated superior computational throughput and policy stability, making it suitable for real-time and resource-constrained environments. PPO exhibited stronger action magnitudes, enabling more assertive control signals for high-impact interventions. Both agents significantly outperformed a rule-based baseline in responsiveness and adaptability. The proposed framework represents a novel contribution by combining deep reinforcement learning with interpretable, country-level energy indicators. Future work will extend the evaluation to additional continents, including Asia, Africa, and South America, to assess global scalability and applicability.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100344"},"PeriodicalIF":1.8,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221234","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}
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