IFAC-PapersOnLine最新文献

{"title":"Trade-off in Quantization Between Data-driven Design and Control Inputs⁎","authors":"Iori Takaki ,&nbsp;Ahmet Cetinkaya ,&nbsp;Hideaki Ishii","doi":"10.1016/j.ifacol.2025.07.052","DOIUrl":"10.1016/j.ifacol.2025.07.052","url":null,"abstract":"<div><div>In this paper, we consider a remote control problem based on data-driven control with an emphasis on communication constraints. Specifically, we propose a direct data-driven stabilization method with quantization in input and state data for unknown discrete-time linear systems. Moreover, the controller is designed taking account of the effects of quantization in the feedback data. Logarithmic type quantization is employed, and we show the inherent trade-off in the quantization coarseness for data-driven design and feedback control. We illustrate the effectiveness of the method through numerical simulations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","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":"144724123","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":"From Dissensus to Consensus: Bias-Controlled Transition in Nonlinear Opinion Dynamics⁎","authors":"Rajul Kumar,&nbsp;Ningshi Yao","doi":"10.1016/j.ifacol.2025.07.059","DOIUrl":"10.1016/j.ifacol.2025.07.059","url":null,"abstract":"<div><div>We propose a novel bias-based consensus framework for nonlinear opinion dynamics. Due to the observable and malleable nature of bias in human-robot interactions, we utilize it as a control parameter to achieve consensus. First, we analyze the Lyapunov–Schmidt reduced system near equilibrium under small bias assumptions. Through constrained cusp bifurcation, we show that increasing individual biases beyond identified thresholds—and relative biases beyond saddle-node limit points ensures consensus with a unique stable equilibrium. For large biases, we conduct a global phase-plane analysis. By establishing strong monotonicity and applying the Poincaré–Bendixson theorem, we eliminate the possibility of limit cycles and guarantee consensus with a unique stable attractor as equilibrium. Finally, along with numerical simulations for the two-agent, two-option case, we show that the proposed bias control approach extends seamlessly to decentralized multi-agent opinion consensus.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 145-150"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724125","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":"Neural Network-based Stability Guarantee for Dissensus Opinion Behaviors on the Sphere⁎","authors":"Junkai Wang ,&nbsp;Ziqiao Zhang ,&nbsp;Fumin Zhang","doi":"10.1016/j.ifacol.2025.07.044","DOIUrl":"10.1016/j.ifacol.2025.07.044","url":null,"abstract":"<div><div>In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 4","pages":"Pages 55-60"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724568","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":"Human Impact of Visual Detail in Vehicle Lane-Keeping System Communication","authors":"Yu Chul Lee ,&nbsp;Junmin Wang","doi":"10.1016/j.ifacol.2025.07.004","DOIUrl":"10.1016/j.ifacol.2025.07.004","url":null,"abstract":"<div><div>Visual displays are used in vehicle automation systems to communicate the vehicle’s perception of the surrounding environment to the driver and passengers. This visual communication may impact how humans interact with the vehicle during driving. Thus, careful design of vehicle automation system visual communication is important for vehicle-driver collaboration. However, there is a lack of systematic study on how the level of detail in vehicle automation visual communication affects human driver’s workload, engagement, and acceptance. This paper presents a pilot research aiming to assess the impact of visual communication level of detail in vehicle automation systems. Both objective evaluation and subjective evaluation are conducted with human driving data collected on a driving simulator. Experimental results show a good agreement between the proposed objective assessment and subjective assessment.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 3","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662544","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":"Mitigation Strategy for Navigation Errors in Strict Route Plans","authors":"Mohammad Almudhaf ,&nbsp;Ümit Özgüner","doi":"10.1016/j.ifacol.2025.07.023","DOIUrl":"10.1016/j.ifacol.2025.07.023","url":null,"abstract":"<div><div>Localization errors can be caused by unintentional malfunctions or intentional attacks on localization processes and sensory devices. These errors can occur at critical driving situations and at high speeds, resulting in false navigation for Autonomous Vehicles (AV). Those situations can be at a road fork that is branching to an adjacent parallel road or to a different level of a multi-level road. The AV can merge to the wrong road and continue following the original route plan until the correct current location is realized. This can lead to hazardous driving situations or deviations from a strict plan. This work considers special cases where the AV must adhere to a strictly specified route, i.e. large vehicles that must be driven on highways. An algorithm that we call Road-Class Aware Rerouting (RCAR) is developed to identify the point of deviation within a directed graph representing a road map and find the optimal way to return to the original route or reach the destination while maintaining road network constraints. Simulated examples are included to illustrate the proposed algorithm.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 3","pages":"Pages 133-138"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662557","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":"Real-time predictive modeling of flight delays using distributed systems and machine learning","authors":"Z. Kowalczuk ,&nbsp;J. Wszołek ,&nbsp;J. Okuniewska","doi":"10.1016/j.ifacol.2025.07.034","DOIUrl":"10.1016/j.ifacol.2025.07.034","url":null,"abstract":"<div><div>This study presents a real-time system for flight delay prediction using distributed systems and machine learning. By integrating flight data from ADS-B signals, METARs, and TAF weather reports, the system processes the data streams via a reactive architecture. The applied predictive models, including random forests and linear regression, were validated and evaluated for their accuracy and scalability. The developed system offers a practical solution for improving decision-making in air traffic management.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 3","pages":"Pages 198-203"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662568","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":"Towards time adaptive observations for model order reduction in data assimilation","authors":"C. Gräßle ,&nbsp;J. Marquardt","doi":"10.1016/j.ifacol.2025.03.059","DOIUrl":"10.1016/j.ifacol.2025.03.059","url":null,"abstract":"<div><div>In this work, we focus on two aspects of 4D-var data assimilation (DA) governed by parabolic partial differential equations (PDEs). First, we are interested on how to set up adaptive time grids for DA problems and in what extend DA benefits from it. Second, we study the application of model order reduction (MOR) for DA problems. Since solving DA problems requires to solve the involved PDEs repeatedly, the use of MOR techniques is an obvious approach. We apply the methods Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) and investigate whether the previously introduced adaptive time grid facilitates the MOR with respect to accuracy and efficiency.</div><div>In order to construct an adaptive time grid, we interpret the DA problem in the context of optimal control and use a reformulation of the optimality conditions. Following Gong et al. (2012), we transferred their idea of deriving a-posteriori error estimates to the 4D-var problem in Graßle and Marquardt (2024). In this work, we extend our previous results where we derived an error estimate for the adjoint state by additionally considering an estimate for the state. The resulting time grid is used for MOR, which has already been done for distributed control problems in order to identify suitable snapshot locations, see Alla et al. (2016, 2018). We conclude with a numerical example.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 343-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704266","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":"Using the Envelope of the Electroencephalogram as a Model for Gaussianity during Sleep and Anesthesia","authors":"Julian Ostertag ,&nbsp;Tobias Kraft-Blank ,&nbsp;Gerhard Schneider ,&nbsp;Matthias Kreuzer ,&nbsp;Juliana Zimmermann","doi":"10.1016/j.ifacol.2025.03.051","DOIUrl":"10.1016/j.ifacol.2025.03.051","url":null,"abstract":"<div><div>Despite the significant differences between sleeping and being under anesthesia, i.e., a physiological process vs. a pharmacologically induced state, they share notable similarities. This is particularly evident when examining the electroencephalogram (EEG), where the spectral content of both states reveals marked increased power within delta (1 - 4 Hz) and alpha (8 - 13 Hz) frequency ranges. To further explore this, a novel analytical framework called the coefficient of variation of the envelope (CVE) was utilized to assess the alpha and delta EEG envelopes during sleep and general anesthesia. This measure is sensitive to different underlying neural dynamics by linking signal morphology and signal energy, specifically through examining deviations from Gaussianity as a marker of synchronicity. Stable episodes were extracted from patients under general anesthesia and controls in non-REM sleep stage 2 and 3. After filtering the EEGs to isolate the delta and alpha bands, the EEG data was segmented into 24-second intervals with a 50% overlap. In addition to the envelope’s energy, CVEs were calculated using the Hilbert transformation. Cutoff values for Gaussianity were derived from simulated EEG signals. CVE values outside the 99% confidence intervals (CI) of the simulated data are considered to indicate either rhythmic (CV E &lt; lowerCI) or pulsatile (CV E &gt; upperCI) activity. The findings revealed differences in CVEs across both delta and alpha-band filtered EEG. Specifically, during sleep, CVEs derived from the delta band were more frequently classified as pulsatile and fell less often within the gaussian range, compared to those observed during general anesthesia. Similar distinctions were observed for alpha-band oscillations. Although the spectral content related to delta and alpha power may appear similar, the morphology of the underlying neural oscillations differs. These differences are critical points that differentiate anesthesia from sleep.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 295-300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704258","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":"Impact of topography and combustion functions on fire front propagation in an advection-diffusion-reaction model for wildfires","authors":"Luca Nieding ,&nbsp;Cordula Reisch ,&nbsp;Dirk Langemann ,&nbsp;Adrián Navas-Montilla","doi":"10.1016/j.ifacol.2025.03.019","DOIUrl":"10.1016/j.ifacol.2025.03.019","url":null,"abstract":"<div><div>Given the recent increase in wildfires, developing a better understanding of their dynamics is crucial. For this purpose, the advection-diffusion-reaction model has been widely used to study wildfire dynamics. In this study, we introduce the previously unconsidered influence of topography through an additional advective term. Furthermore, we propose a linear term for the combustion function, comparing it with the commonly used Arrhenius law to offer a simpler model for further analysis. Our findings on the model’s dynamics are supported by numerical simulations showing the differences of model extensions and approximations.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","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":"143704308","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":"Control-Oriented Gray-Box Modeling for Thermoset Injection Molding⁎","authors":"Jens Ahlers ,&nbsp;Christopher Schulte ,&nbsp;Moritz Mascher ,&nbsp;Christoph Zimmermann ,&nbsp;Heike Vallery ,&nbsp;Christian Hopmann ,&nbsp;Sebastian Stemmler","doi":"10.1016/j.ifacol.2025.03.004","DOIUrl":"10.1016/j.ifacol.2025.03.004","url":null,"abstract":"<div><div>Cavity pressure control can enhance the repeatability of injection molding processes. While extensive research has focused on thermoplastic cavity pressure control, there is a notable gap in models and control strategies for thermoset injection molding. This study aims to develop a model structure for thermoset injection molding suitable for integration into a model-based control scheme. The modeling approach is intended to be as generalizable as possible and sufficiently flexible to adapt to various process conditions. At the same time, it should be easy to parameterize or to train. To address this challenge, we first derive a first-principles process model. In the second step, we integrate a feed-forward artificial neural network into this model, which learns parameters and source terms from past injection molding cycles, resulting in a gray-box model. The neural network outputs replace the initial model parameters with functions of system inputs, states, and time. We validate both models against experimental data from a thermoset injection molding machine using a fat-plate mold geometry and a phenolic resin compound. We identify limitations of the proposed approach and suggest potential solutions.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 1","pages":"Pages 13-18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704317","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}