IFAC-PapersOnLine最新文献

{"title":"On Identifiability and Model Validation of a Population Balance Model for Liquid-Liquid-Mixing⁎","authors":"Terrance Wilms ,&nbsp;Daniel Topalovic ,&nbsp;Chrysoula Bliatsiou ,&nbsp;Jörn Villwock ,&nbsp;Matthias Kraume ,&nbsp;Andreas Bück ,&nbsp;Steffi Knorn","doi":"10.1016/j.ifacol.2025.03.033","DOIUrl":"10.1016/j.ifacol.2025.03.033","url":null,"abstract":"<div><div>In this paper, liquid-liquid-mixer systems modelled by population balance equations are considered. As the control input / manipulated variable, the stirrer speed (and hence the energy dissipation rate) is used to control the distribution of the particle size. Three different measurement signals are considered. We investigate the observability and identifiability of the system for each measurement option and show that only in one scenario the model is observable and structurally identifiable. Further, it is shown that in this scenario, practical identifiability is generally challenging and particular care must be taken to ensure the model with identified parameter values is able to describe the dynamic behaviour of the system.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 187-192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State-space modelling approach for control and observer design in property-controlled reverse flow forming","authors":"Lukas Kersting ,&nbsp;Bahman Arian ,&nbsp;Julian Rozo Vasquez ,&nbsp;Ansgar Trächtler ,&nbsp;Werner Homberg ,&nbsp;Frank Walther","doi":"10.1016/j.ifacol.2025.03.020","DOIUrl":"10.1016/j.ifacol.2025.03.020","url":null,"abstract":"<div><div>Flexible, metal forming processes such as flow forming are exposed to many disturbances that affect geometry and material properties during forming and reduce the reproducibility of those processes. In this context, closed-loop property control is a feasible solution for process improvement. To include advanced controllers and observers in closed-loop property control concepts, mathematical models in terms of state-space models are advantageous. However, especially in case of flow forming these models are not spread due to the complex process kinematics, nonlinearity of the process and spatial dimensions of the workpiece. This makes mathematic state-space modelling challenging. Thus, a model has to be newly developed. This paper therefore presents a novel discrete-time state-space model for flow forming. The model treats the workpiece geometry as discrete and represents the complex process kinematics in the form of a local sampling of the roller trajectory. It can be shown that the model is valid and that the resulting system dynamics especially results the from the displacement between roller and sensor which is considered in the model. Thus, the result of this paper is a valid state-space model that can be prospectively used in a model-based state observer for flow forming. This state observer could later extent a closed-loop property control to enable more complex property structures than before.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 109-114"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synchronization of Kuramoto oscillators via HEOL, and a discussion on AI","authors":"Emmanuel Delaleau ,&nbsp;Cédric Join ,&nbsp;Michel Fliess","doi":"10.1016/j.ifacol.2025.03.040","DOIUrl":"10.1016/j.ifacol.2025.03.040","url":null,"abstract":"<div><div>Artificial neural networks and their applications in deep learning have recently made an incursion into the field of control. Deep learning techniques in control are often related to optimal control, which relies on the Pontryagin maximum principle or the Hamilton-Jacobi-Bellman equation. They imply control schemes that are tedious to implement. We show here that the new HEOL setting, resulting from the fusion of the two established approaches, namely differential flatness and model-free control, provides a solution to control problems that is more sober in terms of computational resources. This communication is devoted to the synchronization of the popular Kuramoto’s coupled oscillators, which was already considered via artificial neural networks by L. Bttcher <em>et al.</em> (Nature Commun., 2022), where, contrarily to this communication, only the single control variable case is examined. One establishes the flatness of Kuramotos coupled oscillator model with multiplicative control and develops the resulting HEOL control. Unlike many examples, this system reveals singularities that are avoided by a clever generation of phase angle trajectories. The results obtained, verified in simulations, show that it is not only possible to synchronize these oscillators in finite time, and even to follow angular frequency profiles, but also to exhibit robustness concerning model mismatches. To the best of our knowledge that has never been done before. Concluding remarks advocate a viewpoint, which might be traced back to Wiener’s cybernetics: control theory belongs to AI.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 229-234"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of High-Fat High-Protein Meals Using the UVA/Padova T1D Simulator","authors":"Edoardo Faggionato ,&nbsp;Michele Schiavon ,&nbsp;Chiara Dalla Man","doi":"10.1016/j.ifacol.2025.06.018","DOIUrl":"10.1016/j.ifacol.2025.06.018","url":null,"abstract":"<div><div>Meals with a high content of fat (F) and protein (P) still represent a significant challenge for optimal glycemic control in individuals with type 1 diabetes (T1D). Current strategies dealing with such meals require patients to undergo a series of trial-and-error visits until satisfactory glycemic control is achieved. In this study, we updated the UVA/Padova T1D simulator by integrating F and P effects on model parameters to allow a realistic simulation of meals with varying content. Glycemic profiles of one-hundred virtual subjects were simulated after a nominal meal (75 g of carbohydrates, 20 g of F, and 24 g of P), a high-F meal (HF, +20 g of F to the nominal meal), a high-P meal (HP, +20 g of P), and a HF/HP meal (+20 g of F and +20 g of P) using the standard (carbohydrate-only based) meal insulin bolus. Simulations were evaluated in terms of average blood glucose (BG), time in range (TIR), and time below range (TBR). The simulated glucose time courses were compared against continuous glucose monitor profiles collected on real individuals who ate similar meals, showing comparable median profiles and variability. Then, the simulator was employed to test the efficacy of a dual-wave bolus (+30% of the dose, given 50% immediately and 50% over 2 h) over the standard bolus on the HF/HP meal challenge. The dual-wave bolus produced a significantly lower BG (p&lt;0.0001) and higher TIR compared to the standard therapy (p&lt;0.01), with TBR similar to that of the nominal meal (p=0.29), demonstrating its ability to restore normal glucose levels, without excessively increasing the time in hypoglycemia. However, the updated simulator still lacks proper validation against data that are free from any confounding factor. Therefore, the next step will involve the collection of data in a trial specifically designed to detect the effect of different macronutrients in the meal. Once validated, the proposed platform will be suitable for testing therapies involving meals with different macronutrient content, reducing the burden for clinical trials. Future work will also focus on exploring other dietary factors and integrating time-varying effects of F and P to improve precision and applicability of the simulator.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 103-108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588468","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":"Hybrid Optimization Method for Safe Autonomous Navigation under Uncertainty","authors":"Emmanuel Alao ,&nbsp;Lounis Adouane ,&nbsp;Philippe Martinet","doi":"10.1016/j.ifacol.2025.07.021","DOIUrl":"10.1016/j.ifacol.2025.07.021","url":null,"abstract":"<div><div>A fundamental difficulty that autonomous driving systems face in dynamic environments is uncertainty in the motion of the other traffic participants. Although predictive control algorithms have proven to be able to handle such scenarios, the multimodal motion of agents often increases the risk in traffic environments, leading to unsmooth motion and sometimes the failure of such risk assessment and management algorithms. This paper proposes to tackle such problems using a hybrid optimization method that finds a global safe trajectory with respect to the surrounding agents using a Fusion of stochastic Predictive Inter-Distance Profile (F-sPIDP) before following the safe path using a combination of stochastic reference points and an uncertainty-aware local trajectory optimizer. The results show that the proposed method guarantees safety even with varying levels of uncertainties.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 3","pages":"Pages 121-126"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662555","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":"RL-MPC Framework for Data-driven Energy Management in Mild Hybrid Electric Vehicles","authors":"Suyong Park ,&nbsp;Solyeon Kwon ,&nbsp;Kyoungseok Han","doi":"10.1016/j.ifacol.2025.07.015","DOIUrl":"10.1016/j.ifacol.2025.07.015","url":null,"abstract":"<div><div>This work highlights the potential of RL-MPC to achieve energy-efficient performance in real-world driving scenarios without the need for full travel information. Although dynamic programming (DP) can achieve a globally optimal solution, it requires full travel information, such as the whole velocity profile. To address this limitation, we decompose the optimal control problem into stage and terminal costs, allowing optimization without full travel information. Model predictive control (MPC) is used to solve stage cost when short-term predicted information is available. In addition, terminal costs are incorporated to penalize the state using a value function. Using similar driving patterns of the repeated routes, we approximate the value function that exhibits near optimality through reinforcement learning (RL). Compared to conventional MPC, which is based solely on short-term information, RL-MPC reduces fuel consumption by 5.53% while meeting the final state of charge (SOC) condition.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 3","pages":"Pages 85-90"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663144","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":"Control-Inspired Federated Learning: A Projection-Based Approach","authors":"Ayush Rai ,&nbsp;Xudong Chen ,&nbsp;Shaoshuai Mou","doi":"10.1016/j.ifacol.2025.07.053","DOIUrl":"10.1016/j.ifacol.2025.07.053","url":null,"abstract":"<div><div>In this work, we study federated learning, a distributed learning framework where data remains decentralized across multiple clients, and a shared model is trained collaboratively via a central server. We take a control-theoretic approach, focusing on a specific class of residual neural networks by modeling them as dynamical systems. Building on this perspective, we propose FedProject, an algorithm designed to mitigate statistical heterogeneity caused by non-identically distributed data. Our method introduces a projection operator on the proximal term of FedProx and employs a two-step update to balance local learning and client drift from the global model. We evaluate FedProject against FedProx and FedAvg on both IID and heterogeneous datasets, demonstrating improved convergence and robustness to hyperparameter selection for this class of neural networks.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 109-114"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724122","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":"Distributed Model Predictive Control of Epidemics via Transportation Flow Regulations⁎","authors":"Yuhong Chen ,&nbsp;Xiaobing Dai ,&nbsp;Sihua Zhang ,&nbsp;Martin Buss ,&nbsp;Sandra Hirche ,&nbsp;Fangzhou Liu","doi":"10.1016/j.ifacol.2025.07.062","DOIUrl":"10.1016/j.ifacol.2025.07.062","url":null,"abstract":"<div><div>The COVID-19 pandemic highlights the necessity of epidemic control while minimizing societal and economic disruption. This paper formulates an optimal transportation flow restriction problem that offers a more direct and realistic representation of real-world policy design. A sequential distributed Lyapunov-based MPC (SDLMPC) approach is introduced, enabling asynchronous decision-making for subpopulations. The feasibility of applying SDLMPC is proven, and its effectiveness is validated through simulations using real COVID-19 data from Germany. The results demonstrate that SDLMPC effectively balances epidemic control with reduced negative impacts, offering a practical and implementable decision-making framework.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 163-168"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724128","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":"Obstacle avoidance adaptive coverage control using collective initial excitation","authors":"S. Surendhar ,&nbsp;Sayan Basu Roy ,&nbsp;Shubhendu Bhasin","doi":"10.1016/j.ifacol.2025.07.045","DOIUrl":"10.1016/j.ifacol.2025.07.045","url":null,"abstract":"<div><div>This paper introduces the obstacle avoidance adaptive coverage control for the mobile sensor network (MSN). In addition to obstacle avoidance, parameter convergence of the unknown spatial sensory function is guaranteed under the mild excitation condition termed collective initial excitation (C-IE). The proportional-integral (PI) type adaptation enables the multi-agents to achieve convergence and consensus under a less stringent excitation condition than collective persistence of excitation (C-PE). The proposed coverage algorithm is evaluated through simulation.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 61-66"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724569","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":"A Mathematical Model for the Intracellular Accumulation of two Energy Reserve Carbohydrates in S. cerevisiae Cultures","authors":"Antoine Huet ,&nbsp;Mihaela Sbarciog ,&nbsp;Philippe Bogaerts","doi":"10.1016/j.ifacol.2025.03.070","DOIUrl":"10.1016/j.ifacol.2025.03.070","url":null,"abstract":"<div><div>This paper presents the development of a novel macroscopic model that predicts the dynamics of trehalose and glycogen in yeast <em>Saccharomyces cerevisiae</em> fed-batch cultures. The model includes storage and mobilization reactions for each reserve carbohydrate and conversion of glycogen to trehalose. The proposed model is a purely macroscopic model, which relates the two carbohydrates only to the variation of the extracellular components without including complex intracellular phenomena. Its predictions are in agreement with the experimental data. The model can be used to optimize the accumulation of the two reserve carbohydrates, to obtain yeast cells that can withstand stressful conditions.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 409-414"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}