Pub Date : 2025-12-20DOI: 10.1016/j.automatica.2025.112792
Sibren Lagauw, Lukas Vanpoucke, Bart De Moor
We consider the least squares (LS) realization of an autonomous single-output linear time-invariant dynamical model for a given sequence of output data. As opposed to standard system identification practices, which often rely on (heuristic) iterative optimization techniques, we propose an exact solution to this non-convex optimization problem: the globally optimal solution(s) are identified by means of a deterministic eigenvalue procedure. In particular, we illustrate that for all (local) minimizers, the corresponding misfit can be characterized as the result of filtering an unknown signal twice through a finite-impulse response filter. Exploiting this insight, we propose a novel (rectangular) multiparameter eigenvalue problem (MEP), the eigentuples of which allow to retrieve all local and global minimizers of the identification problem. The proposed MEP is of great theoretical interest and offers new insights into the structure of the LS realization problem, which we explore in detail. We provide numerical examples to illustrate our findings.
{"title":"Exact solution to the least squares realization problem as a multiparameter eigenvalue problem","authors":"Sibren Lagauw, Lukas Vanpoucke, Bart De Moor","doi":"10.1016/j.automatica.2025.112792","DOIUrl":"10.1016/j.automatica.2025.112792","url":null,"abstract":"<div><div>We consider the least squares (LS) realization of an autonomous single-output linear time-invariant dynamical model for a given sequence of output data. As opposed to standard system identification practices, which often rely on (heuristic) iterative optimization techniques, we propose an exact solution to this non-convex optimization problem: the <em>globally optimal</em> solution(s) are identified by means of a <em>deterministic</em> eigenvalue procedure. In particular, we illustrate that for all (local) minimizers, the corresponding misfit can be characterized as the result of filtering an unknown signal twice through a finite-impulse response filter. Exploiting this insight, we propose a novel (rectangular) multiparameter eigenvalue problem (MEP), the eigentuples of which allow to retrieve all local and global minimizers of the identification problem. The proposed MEP is of great theoretical interest and offers new insights into the structure of the LS realization problem, which we explore in detail. We provide numerical examples to illustrate our findings.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112792"},"PeriodicalIF":5.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784437","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}
Pub Date : 2025-12-20DOI: 10.1016/j.automatica.2025.112781
Zhihe Zhuang , Rodrigo A. González , Hongfeng Tao , Wojciech Paszke , Tom Oomen
Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances. This paper develops a novel direct data-based ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level. Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems’ Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.
{"title":"Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks","authors":"Zhihe Zhuang , Rodrigo A. González , Hongfeng Tao , Wojciech Paszke , Tom Oomen","doi":"10.1016/j.automatica.2025.112781","DOIUrl":"10.1016/j.automatica.2025.112781","url":null,"abstract":"<div><div>Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances. This paper develops a novel direct data-based ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level. Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems’ Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112781"},"PeriodicalIF":5.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784438","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}
Pub Date : 2025-12-20DOI: 10.1016/j.automatica.2025.112773
Giordano Pola, Elena De Santis, Maria Domenica Di Benedetto
In this paper we consider a pair of interconnected, nondeterministic and metric finite state systems and address a control problem where controllers are designed for enforcing local specifications expressed in terms of regular languages, up to a desired accuracy. The control architecture considered is decentralized, that is each controller can only communicate with the corresponding plant. Since plant systems are interconnected, the part of the specification that can be enforced on one system depends on the part that can be applied on the other one. We show how this dependency can be formalized in terms of equilibria, by extending game theory to the present framework. We introduce notions of equilibria, Nash equilibria and dominant equilibria. When controlled plants are at an equilibrium, they satisfy a part of their specification; when they are at a Nash equilibrium, deviation of each plant from its control strategy may correspond to a loss in terms of the part of specification enforced; when they are at a dominant equilibrium, there is no other equilibrium where plants can achieve larger parts of the corresponding specifications. A characterization of these notions is derived and checkable conditions are discussed. An example in the context of multi-agent systems with shared resources is also included.
{"title":"Decentralized control of finite state systems: A game theoretic approach","authors":"Giordano Pola, Elena De Santis, Maria Domenica Di Benedetto","doi":"10.1016/j.automatica.2025.112773","DOIUrl":"10.1016/j.automatica.2025.112773","url":null,"abstract":"<div><div>In this paper we consider a pair of interconnected, nondeterministic and metric finite state systems and address a control problem where controllers are designed for enforcing local specifications expressed in terms of regular languages, up to a desired accuracy. The control architecture considered is decentralized, that is each controller can only communicate with the corresponding plant. Since plant systems are interconnected, the part of the specification that can be enforced on one system depends on the part that can be applied on the other one. We show how this dependency can be formalized in terms of equilibria, by extending game theory to the present framework. We introduce notions of equilibria, Nash equilibria and dominant equilibria. When controlled plants are at an equilibrium, they satisfy a part of their specification; when they are at a Nash equilibrium, deviation of each plant from its control strategy may correspond to a loss in terms of the part of specification enforced; when they are at a dominant equilibrium, there is no other equilibrium where plants can achieve larger parts of the corresponding specifications. A characterization of these notions is derived and checkable conditions are discussed. An example in the context of multi-agent systems with shared resources is also included.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112773"},"PeriodicalIF":5.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784432","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112787
Lapo Frascati , Alberto Bemporad
This paper proposes a novel combination of extended Kalman filtering (EKF) with the alternating direction method of multipliers (ADMM) for learning parametric nonlinear models online under non-smooth regularization terms, including and penalties and bound constraints on model parameters. For the case of linear time-varying models and non-smooth convex regularization terms, we provide a sublinear regret bound that ensures the proper behavior of the online learning strategy. The approach is computationally efficient for a wide range of regularization terms, which makes it appealing for its use in embedded control applications for online model adaptation. We show the performance of the proposed method in three simulation examples, highlighting its effectiveness compared to other batch and online algorithms.
{"title":"Online learning of nonlinear parametric models under non-smooth regularization using EKF and ADMM","authors":"Lapo Frascati , Alberto Bemporad","doi":"10.1016/j.automatica.2025.112787","DOIUrl":"10.1016/j.automatica.2025.112787","url":null,"abstract":"<div><div>This paper proposes a novel combination of extended Kalman filtering (EKF) with the alternating direction method of multipliers (ADMM) for learning parametric nonlinear models online under non-smooth regularization terms, including <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> penalties and bound constraints on model parameters. For the case of linear time-varying models and non-smooth convex regularization terms, we provide a sublinear regret bound that ensures the proper behavior of the online learning strategy. The approach is computationally efficient for a wide range of regularization terms, which makes it appealing for its use in embedded control applications for online model adaptation. We show the performance of the proposed method in three simulation examples, highlighting its effectiveness compared to other batch and online algorithms.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112787"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784442","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112785
Hao Yu , Zhe Guan , Toru Yamamoto , Junzheng Wang
This paper studies sampled-data tracking control problems for first-order nonlinear time-invariant plants. A sampled-data adaptive PI controller is developed from exact discretization and full form dynamic linearization (FFDL) methods. To ensure the uniform boundedness of adaptive PI parameters with respect to sufficiently small sampling periods, novel lifted FFDL models and cost functions are introduced for designing controllers and adaptive rules. After establishing nonlinear closed-loop dynamics, new overall Lyapunov functions containing logarithmic operation are constructed for proving global stability and convergence. An extension to locally Lipschitz dynamics is given. Finally, two numerical examples and a practical application in longitudinal speed tracking for electrical cars are simulated to illustrate the efficiency and feasibility of the proposed results.
{"title":"FFDL-based sampled-data adaptive PI control with uniformly bounded parameters","authors":"Hao Yu , Zhe Guan , Toru Yamamoto , Junzheng Wang","doi":"10.1016/j.automatica.2025.112785","DOIUrl":"10.1016/j.automatica.2025.112785","url":null,"abstract":"<div><div>This paper studies sampled-data tracking control problems for first-order nonlinear time-invariant plants. A sampled-data adaptive PI controller is developed from exact discretization and full form dynamic linearization (FFDL) methods. To ensure the uniform boundedness of adaptive PI parameters with respect to sufficiently small sampling periods, novel lifted FFDL models and cost functions are introduced for designing controllers and adaptive rules. After establishing nonlinear closed-loop dynamics, new overall Lyapunov functions containing logarithmic operation are constructed for proving global stability and convergence. An extension to locally Lipschitz dynamics is given. Finally, two numerical examples and a practical application in longitudinal speed tracking for electrical cars are simulated to illustrate the efficiency and feasibility of the proposed results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112785"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799605","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112766
Yan-Jun Liu, Wenguang Zan, Li Tang
This study explores the event-triggered based output feedback consensus issue for multi-agent systems (MASs) under denial-of-service (DoS) attacks. All agents in the considered systems are governed by parabolic partial differential equations (PDEs). Faced with unpredictable system states and malicious attackers launching non-periodic DoS attacks, an event-triggered security control agreement for MASs is introduced and achieve consensus on any given undirected communication graph. The tolerable frequency and duration of DoS attacks are outlined in relation to the observer-based security consensus problem. By employing Lyapunov technique and mathematical inequalities, the sufficient conditions to ensure the global asymptotic stability of MASs under DoS attacks are given. Furthermore, a rigorous demonstration of the minimum dwell time between successive triggered events is furnished. Finally, simulation examples effectively validate the theoretical findings.
{"title":"DoS attacks in parabolic multi-agent systems: An output feedback based event-triggered control approach","authors":"Yan-Jun Liu, Wenguang Zan, Li Tang","doi":"10.1016/j.automatica.2025.112766","DOIUrl":"10.1016/j.automatica.2025.112766","url":null,"abstract":"<div><div>This study explores the event-triggered based output feedback consensus issue for multi-agent systems (MASs) under denial-of-service (DoS) attacks. All agents in the considered systems are governed by parabolic partial differential equations (PDEs). Faced with unpredictable system states and malicious attackers launching non-periodic DoS attacks, an event-triggered security control agreement for MASs is introduced and achieve consensus on any given undirected communication graph. The tolerable frequency and duration of DoS attacks are outlined in relation to the observer-based security consensus problem. By employing Lyapunov technique and mathematical inequalities, the sufficient conditions to ensure the global asymptotic stability of MASs under DoS attacks are given. Furthermore, a rigorous demonstration of the minimum dwell time between successive triggered events is furnished. Finally, simulation examples effectively validate the theoretical findings.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112766"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799650","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112746
Kaikai Zheng , Dawei Shi , Sandra Hirche , Yang Shi
Learning-based control has attracted significant attention in recent years, especially for plants that are difficult to model based on first-principles. A key issue in learning-based control is how to make efficient use of data as the abundance of data becomes overwhelming. To address this issue, this work proposes an information-triggered learning framework and a corresponding learning-based controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics. A set-membership approach is introduced to learn a parametric uncertainty set for the unknown dynamics. Then, a data selection mechanism is proposed by evaluating the incremental information in a data sample, where the incremental information is quantified by its effects on shrinking the parametric uncertainty set. Next, after introducing a stability criterion using the set-membership estimate of the system dynamics, a robust learning-based predictive controller (LPC) is designed by minimizing a worst-case cost function. The closed-loop stability of the LPC equipped with the information-triggered learning protocol is discussed within a high-probability framework. Finally, comparative numerical experiments are performed to verify the validity of the proposed approach.
{"title":"Information-triggered learning with application to learning-based predictive control","authors":"Kaikai Zheng , Dawei Shi , Sandra Hirche , Yang Shi","doi":"10.1016/j.automatica.2025.112746","DOIUrl":"10.1016/j.automatica.2025.112746","url":null,"abstract":"<div><div>Learning-based control has attracted significant attention in recent years, especially for plants that are difficult to model based on first-principles. A key issue in learning-based control is how to make efficient use of data as the abundance of data becomes overwhelming. To address this issue, this work proposes an information-triggered learning framework and a corresponding learning-based controller design approach with guaranteed stability. Specifically, we consider a linear time-invariant system with unknown dynamics. A set-membership approach is introduced to learn a parametric uncertainty set for the unknown dynamics. Then, a data selection mechanism is proposed by evaluating the incremental information in a data sample, where the incremental information is quantified by its effects on shrinking the parametric uncertainty set. Next, after introducing a stability criterion using the set-membership estimate of the system dynamics, a robust learning-based predictive controller (LPC) is designed by minimizing a worst-case cost function. The closed-loop stability of the LPC equipped with the information-triggered learning protocol is discussed within a high-probability framework. Finally, comparative numerical experiments are performed to verify the validity of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112746"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799193","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112795
Alexander Yu. Pogromsky , Alexey S. Matveev
This paper addresses the problem of robust remote state estimation for uncertain nonlinear discrete-time systems when sensor data are transmitted through a digital communication channel of finite bit-rate capacity. The goal is to determine the minimal channel rate required to guarantee a prescribed estimation accuracy in the presence of bounded model uncertainty. We derive an explicit, tractable lower bound on the channel bit rate that ensures this accuracy for any admissible uncertainty level. The bound highlights the fundamental role of the accuracy-to-uncertainty ratio in remote estimation. The analysis relies on a quadratic dissipation inequality describing system uncertainty within the framework of incremental input-to-state stability, leading to a constructive Lyapunov-based characterization. The proposed conditions admit a closed-form analytical expression for a class of systems, including the uncertain Lozi map, which serves as an illustrative example.
{"title":"Remote robust state estimation for nonlinear systems","authors":"Alexander Yu. Pogromsky , Alexey S. Matveev","doi":"10.1016/j.automatica.2025.112795","DOIUrl":"10.1016/j.automatica.2025.112795","url":null,"abstract":"<div><div>This paper addresses the problem of robust remote state estimation for uncertain nonlinear discrete-time systems when sensor data are transmitted through a digital communication channel of finite bit-rate capacity. The goal is to determine the minimal channel rate required to guarantee a prescribed estimation accuracy in the presence of bounded model uncertainty. We derive an explicit, tractable lower bound on the channel bit rate that ensures this accuracy for any admissible uncertainty level. The bound highlights the fundamental role of the accuracy-to-uncertainty ratio in remote estimation. The analysis relies on a quadratic dissipation inequality describing system uncertainty within the framework of incremental input-to-state stability, leading to a constructive Lyapunov-based characterization. The proposed conditions admit a closed-form analytical expression for a class of systems, including the uncertain Lozi map, which serves as an illustrative example.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112795"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784445","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}
On-orbit useful lifetime is a critical metric for spacecraft missions, for which we propose a maximum-system-reliability control allocation scheme for spacecraft with redundant actuators. The dynamic reliability model for the redundant actuator system is formulated, incorporating an online parameter estimation method initialized with offline estimates. On this basis, we define a control allocation optimization problem that maximizes the one-step system reliability prediction. To enable efficient online computation, a recursive optimization algorithm is introduced. Theoretical analysis proves that the proposed approach can extend the useful lifetime of the redundant actuator system with any failure mode. A comparative example of spacecraft attitude stabilization validates the feasibility, generality, and superiority of the proposed method.
{"title":"Maximum-system-reliability control allocation for spacecraft with redundant actuators","authors":"Jianchun Zhang , Xiang Yu , Jianzhong Qiao , Lei Guo","doi":"10.1016/j.automatica.2025.112779","DOIUrl":"10.1016/j.automatica.2025.112779","url":null,"abstract":"<div><div>On-orbit useful lifetime is a critical metric for spacecraft missions, for which we propose a maximum-system-reliability control allocation scheme for spacecraft with redundant actuators. The dynamic reliability model for the redundant actuator system is formulated, incorporating an online parameter estimation method initialized with offline estimates. On this basis, we define a control allocation optimization problem that maximizes the one-step system reliability prediction. To enable efficient online computation, a recursive optimization algorithm is introduced. Theoretical analysis proves that the proposed approach can extend the useful lifetime of the redundant actuator system with any failure mode. A comparative example of spacecraft attitude stabilization validates the feasibility, generality, and superiority of the proposed method.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112779"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799110","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}
Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112772
Juntao Li , Cong Liang , Deyuan Meng
Designing superior distributed algorithms for solving linear algebraic equations (LAEs) plays a crucial role in engineering and computer science fields. This paper proposes two discrete distributed algorithms for solving LAEs from the perspective of optimal control. By benefiting from the devised error system and constructed performance index, the presented algorithms can converge R-linearly to a solution of LAEs without solving algebraic Riccati equations. In particular, the full-row rank requirements on sub-matrices are eliminated in row partitioning framework. Moreover, the need for communication exchange among all agents within the same cluster is alleviated, and only one state variable is updated in the row-wise arbitrary column partitioning framework. Simulation results demonstrate that the proposed distributed algorithms outperform non-optimal control design algorithms in terms of convergence performance.
{"title":"Distributed algorithms for solving linear algebraic equations: An optimal control perspective","authors":"Juntao Li , Cong Liang , Deyuan Meng","doi":"10.1016/j.automatica.2025.112772","DOIUrl":"10.1016/j.automatica.2025.112772","url":null,"abstract":"<div><div>Designing superior distributed algorithms for solving linear algebraic equations (LAEs) plays a crucial role in engineering and computer science fields. This paper proposes two discrete distributed algorithms for solving LAEs from the perspective of optimal control. By benefiting from the devised error system and constructed performance index, the presented algorithms can converge R-linearly to a solution of LAEs without solving algebraic Riccati equations. In particular, the full-row rank requirements on sub-matrices are eliminated in row partitioning framework. Moreover, the need for communication exchange among all agents within the same cluster is alleviated, and only one state variable is updated in the row-wise arbitrary column partitioning framework. Simulation results demonstrate that the proposed distributed algorithms outperform non-optimal control design algorithms in terms of convergence performance.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112772"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799603","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}
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