IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-25 DOI: 10.1016/j.automatica.2025.112493

Zeyu Kang , Qiang Shen , Shufan Wu , Christopher J. Damaren

引用次数: 0

Deep learning of a communication policy for an event-triggered observer for nonlinear systems 非线性系统事件触发观测器通信策略的深度学习

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-19 DOI: 10.1016/j.automatica.2025.112472

Mathieu Marchand , Vincent Andrieu , Sylvain Bertrand , Hélène Piet-Lahanier

{"title":"Deep learning of a communication policy for an event-triggered observer for nonlinear systems","authors":"Mathieu Marchand , Vincent Andrieu , Sylvain Bertrand , Hélène Piet-Lahanier","doi":"10.1016/j.automatica.2025.112472","DOIUrl":"10.1016/j.automatica.2025.112472","url":null,"abstract":"<div><div>This paper examines the problem of designing an event-triggered observer for discrete-time nonlinear systems by learning an optimal communication policy based on available input and output data. An emulation strategy is followed, wherein the observer is initially designed assuming full knowledge of the plant output at any instant. Then, communication constraints are taken into account and the objective is to construct an optimal transmission policy to minimize the amount of data exchanges between the sensors and the observer while preserving the convergence properties of the latter. First, our goal is to design the communication policy to minimize an infinite horizon discounted cost whose stage cost penalizes both the state estimation error and the number of events. The existence of such an optimal policy is guaranteed under mild conditions on the initially designed observer. Moreover, optimal policy are shown to preserve the desired convergence property of the observer. This optimization problem can be formulated as a mixed-integer nonlinear program, which is challenging to solve exactly. To address this, a deep learning algorithm is proposed to approximate the solution using recurrent neural networks. Finally, simulation examples demonstrate the effectiveness and robustness of the learned communication policies.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112472"},"PeriodicalIF":4.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comments on “A globally stable saturated desired compensation adaptive robust control for linear motor systems with comparative experiments” [Automatica 43 (2007) 1840–1848] “线性电机系统的全局稳定饱和期望补偿自适应鲁棒控制与比较实验”[Automatica 43 (2007) 1840-1848]

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-16 DOI: 10.1016/j.automatica.2025.112475

Yuxin Su

引用次数: 0

Analysis, state estimation, and control for the networked competitive multi-virus SIR model 网络化竞争多病毒SIR模型的分析、状态估计与控制

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-16 DOI: 10.1016/j.automatica.2025.112479

Ciyuan Zhang , Sebin Gracy , Tamer Başar , Philip E. Paré

{"title":"Analysis, state estimation, and control for the networked competitive multi-virus SIR model","authors":"Ciyuan Zhang , Sebin Gracy , Tamer Başar , Philip E. Paré","doi":"10.1016/j.automatica.2025.112479","DOIUrl":"10.1016/j.automatica.2025.112479","url":null,"abstract":"<div><div>This paper proposes a novel discrete-time multi-virus susceptible–infected–recovered (SIR) model that captures the spread of competing epidemics over a population network. First, we provide sufficient conditions for the infection levels of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model that captures the summation of all the viruses’ infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. Third, we present a sufficient, but not necessary, condition for the model to be strongly locally observable, and, assuming that the network has only infected or recovered individuals, a necessary and sufficient condition for the model to be strongly locally observable. We prove that the estimation error of our proposed estimator converges to zero asymptotically with the observer gain. Finally, we present a distributed feedback controller which guarantees that each virus dies out at an exponential rate. We then show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112479"},"PeriodicalIF":4.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic reduced-dimensional vector autoregressive modeling with oblique projections 斜投影概率降维矢量自回归建模

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-12 DOI: 10.1016/j.automatica.2025.112476

Yanfang Mo, S. Joe Qin

引用次数: 0

Solving a variety of linear time-varying systems of equations with prescribed performance 求解各种具有规定性能的线性时变方程组

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-12 DOI: 10.1016/j.automatica.2025.112473

Peng Yu, Ning Tan

引用次数: 0

Learning linearized models from nonlinear systems under initialization constraints with finite data 在有限数据的初始化约束下学习非线性系统的线性化模型

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-11 DOI: 10.1016/j.automatica.2025.112478

Lei Xin , Baike She , Qi Dou , George T.-C. Chiu , Shreyas Sundaram

{"title":"Learning linearized models from nonlinear systems under initialization constraints with finite data","authors":"Lei Xin , Baike She , Qi Dou , George T.-C. Chiu , Shreyas Sundaram","doi":"10.1016/j.automatica.2025.112478","DOIUrl":"10.1016/j.automatica.2025.112478","url":null,"abstract":"<div><div>The identification of a linear system model from data has wide applications in control theory. The existing work that provides finite sample guarantees for linear system identification typically uses data from a single long system trajectory under i.i.d. random inputs, and assumes that the underlying dynamics is truly linear. In contrast, we consider the problem of identifying a linearized model when the true underlying dynamics is nonlinear, given that there is a certain constraint on the region where one can initialize the experiments. We provide a multiple trajectories-based deterministic data acquisition algorithm followed by a regularized least squares algorithm, and provide a finite sample error bound on the learned linearized dynamics. Our error bound shows that one can consistently learn the linearized dynamics, and demonstrates a trade-off between the error due to nonlinearity and the error due to noise. We validate our results through numerical experiments, where we also show the potential insufficiency of linear system identification using a single trajectory with i.i.d. random inputs, when nonlinearity does exist.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112478"},"PeriodicalIF":4.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic interval observer design for linear systems 线性系统的系统区间观测器设计

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-11 DOI: 10.1016/j.automatica.2025.112477

Thach Ngoc Dinh , Gia Quoc Bao Tran

引用次数: 0

Decentralized prescribed-time input-to-state stabilization for interconnected normal form nonlinear systems 互连范式非线性系统的分散规定时间输入-状态镇定

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-11 DOI: 10.1016/j.automatica.2025.112480

Hefu Ye , Changyun Wen , James Lam , Petros A. Ioannou

{"title":"Decentralized prescribed-time input-to-state stabilization for interconnected normal form nonlinear systems","authors":"Hefu Ye , Changyun Wen , James Lam , Petros A. Ioannou","doi":"10.1016/j.automatica.2025.112480","DOIUrl":"10.1016/j.automatica.2025.112480","url":null,"abstract":"<div><div>Thus far, decentralized prescribed-time regulation of interconnected nonlinear systems with unknown control coefficients and persistent disturbances has remained unresolved, despite the prevalence of such scenarios in practical applications. This paper provides a systematic solution by integrating state-scaling-based time-varying feedback with a low-conservatism design framework. The stability analysis includes a comprehensive examination of the interconnection dynamics across subsystems, followed by a small-gain analysis for input-to-state stable (ISS) cascade systems. We rigorously prove that the proposed decentralized controllers can achieve global stabilization of the interconnected system within a user-defined time, without requiring prior knowledge of interconnected strengths. A key technical lemma is established, enabling the demonstration that the designed control strategy is robust against persistent disturbances and interconnected uncertainties while also facilitating the derivation of a relaxed condition for the small-gain theorem. The effectiveness of the proposed framework is validated using numerical simulations and a practical case study on room temperature regulation, with results confirming both theoretical guarantees and practical applicability.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112480"},"PeriodicalIF":4.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust data-driven Kalman filtering for unknown linear systems using maximum likelihood optimization 基于最大似然优化的未知线性系统鲁棒数据驱动卡尔曼滤波

IF 4.8 2区计算机科学

Automatica Pub Date : 2025-07-10 DOI: 10.1016/j.automatica.2025.112474

Peihu Duan , Tao Liu , Yu Xing , Karl Henrik Johansson

{"title":"Robust data-driven Kalman filtering for unknown linear systems using maximum likelihood optimization","authors":"Peihu Duan , Tao Liu , Yu Xing , Karl Henrik Johansson","doi":"10.1016/j.automatica.2025.112474","DOIUrl":"10.1016/j.automatica.2025.112474","url":null,"abstract":"<div><div>This paper investigates the state estimation problem for unknown linear systems subject to both process and measurement noise. Based on a prior input–output trajectory sampled at a higher frequency and a prior state trajectory sampled at a lower frequency, we propose a novel robust data-driven Kalman filter (RDKF) that integrates model identification with state estimation for the unknown system. Specifically, the state estimation problem is formulated as a non-convex maximum likelihood optimization problem. Then, we slightly modify the optimization problem to get a problem solvable with a recursive algorithm. Based on the optimal solution to this new problem, the RDKF is designed, which can estimate the state of a given but unknown state-space model. The performance gap between the RDKF and the optimal Kalman filter based on known system matrices is quantified through a sample complexity bound. In particular, when the number of the pre-collected states tends to infinity, this gap converges to zero. Finally, the effectiveness of the theoretical results is illustrated by numerical simulations.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112474"},"PeriodicalIF":4.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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