AutomaticaPub Date : 2025-07-25DOI: 10.1016/j.automatica.2025.112493
Zeyu Kang , Qiang Shen , Shufan Wu , Christopher J. Damaren
{"title":"Authors’ reply to ‘Comment on “Saturated adaptive pose tracking control of spacecraft on SE(3) under attitude constraints and obstacle-avoidance constraints” [Automatica 159 (2024) 111367]’","authors":"Zeyu Kang , Qiang Shen , Shufan Wu , Christopher J. Damaren","doi":"10.1016/j.automatica.2025.112493","DOIUrl":"10.1016/j.automatica.2025.112493","url":null,"abstract":"","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112493"},"PeriodicalIF":4.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702274","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}
{"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}
AutomaticaPub Date : 2025-07-16DOI: 10.1016/j.automatica.2025.112475
Yuxin Su
{"title":"Comments on “A globally stable saturated desired compensation adaptive robust control for linear motor systems with comparative experiments” [Automatica 43 (2007) 1840–1848]","authors":"Yuxin Su","doi":"10.1016/j.automatica.2025.112475","DOIUrl":"10.1016/j.automatica.2025.112475","url":null,"abstract":"<div><div>This correspondence points out two flaws in Hong and Yao (2007), shedding doubt on the validity of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112475"},"PeriodicalIF":4.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634435","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}
AutomaticaPub Date : 2025-07-16DOI: 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}
AutomaticaPub Date : 2025-07-12DOI: 10.1016/j.automatica.2025.112476
Yanfang Mo, S. Joe Qin
{"title":"Probabilistic reduced-dimensional vector autoregressive modeling with oblique projections","authors":"Yanfang Mo, S. Joe Qin","doi":"10.1016/j.automatica.2025.112476","DOIUrl":"10.1016/j.automatica.2025.112476","url":null,"abstract":"<div><div>In this paper, we propose a probabilistic reduced-dimensional vector autoregressive model to extract low-dimensional dynamics from large dimensional noisy data. The model partitions the measurement space into a subspace of reduced-dimensional dynamics and a complementary noise subspace, where the dynamic and static noise sources can be correlated contemporaneously. An oblique projection is required to achieve a partition for the best predictability. A maximum likelihood framework is developed with instrumental variables interpretation and refinement to achieve minimum covariance of the latent prediction errors, yielding dynamic latent variables with a non-increasing order of predictability and an explicit latent dynamic model. The superior performance and efficiency of the proposed approach are demonstrated using datasets from a simulated system and an industrial process.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112476"},"PeriodicalIF":4.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604354","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}
AutomaticaPub Date : 2025-07-12DOI: 10.1016/j.automatica.2025.112473
Peng Yu, Ning Tan
{"title":"Solving a variety of linear time-varying systems of equations with prescribed performance","authors":"Peng Yu, Ning Tan","doi":"10.1016/j.automatica.2025.112473","DOIUrl":"10.1016/j.automatica.2025.112473","url":null,"abstract":"<div><div>Solving a linear time-varying system of equations (LTVSE) is commonly encountered in control theory, whereas previous methods for solving LTVSE suffer from inconvenient parameter fine-tuning, insufficient solution accuracy, and high computational overhead. This paper proposes a generic framework for solving a variety of LTVSE. By reformulating different systems, a generic error function is defined. Then, a prescribed-performance solver is proposed by exploiting prescribed performance theory and zeroing dynamics, and the convergence and robustness of the proposed solver are theoretically analyzed. Numerical studies demonstrate that the proposed method is at least one order of magnitude more accurate or efficient than the previous methods for solving LTVSE. More importantly, one can explicitly prescribe the performance of the solver based on available computing resource or the requirements on accuracy and convergence time. Finally, the proposed method is applied to robot control, observer construction and time-varying parameter identification, which reveals its practical value.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112473"},"PeriodicalIF":4.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604353","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}
AutomaticaPub Date : 2025-07-11DOI: 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}
AutomaticaPub Date : 2025-07-11DOI: 10.1016/j.automatica.2025.112477
Thach Ngoc Dinh , Gia Quoc Bao Tran
{"title":"Systematic interval observer design for linear systems","authors":"Thach Ngoc Dinh , Gia Quoc Bao Tran","doi":"10.1016/j.automatica.2025.112477","DOIUrl":"10.1016/j.automatica.2025.112477","url":null,"abstract":"<div><div>We first develop systematic and comprehensive interval observer designs for linear time-invariant (LTI) systems, under standard assumptions of observability and interval bounds on the initial condition and uncertainties. Traditionally, such designs rely on specific transformations into Metzler (in continuous time) or non-negative (in discrete time) forms, which may impose limitations. We demonstrate that these can be effectively replaced by an LTI transformation that is straightforward to compute offline. Subsequently, we extend the framework to time-varying systems, overcoming the limitations of conventional approaches that offer no guarantees. Our method utilizes dynamic transformations into higher-dimensional target systems, for which interval observers can always be constructed. These transformations become left-invertible after a finite time, provided the system is observable and the target dynamics are sufficiently high-dimensional and fast, thereby enabling the finite-time recovery of interval bounds in the original coordinates. Academic examples are provided to illustrate the proposed methodology.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"180 ","pages":"Article 112477"},"PeriodicalIF":4.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596599","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}
AutomaticaPub Date : 2025-07-11DOI: 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}
AutomaticaPub Date : 2025-07-10DOI: 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}