Pub Date : 2025-12-19DOI: 10.1016/j.automatica.2025.112794
Ming Li , Zhaojian Wang , Mengshuo Jia , Feng Liu , Bo Yang , Xinping Guan
In this paper, we propose a distributed feedback controller to steer a multi-agent dynamical system to the generalized Nash equilibrium (GNE) for an aggregative game with only a monotone pseudo-gradient. First, a monotone aggregative game among the agents is formulated, which considers operational constraints in both the transient process and the steady state. Then, a distributed controller based on primal–dual gradient dynamics is designed for each agent to steer the system to track the GNE of the predefined game autonomously. The controller relaxes the requirement of the strictly/strongly monotone pseudo-gradient. At the same time, the hard limits of the control input are satisfied both at equilibrium and during the transient process. We further prove that the closed-loop system is asymptotically stable and its equilibrium attains the GNE. Finally, a practical example of frequency control on IEEE 68-bus system verifies the effectiveness of the proposed method.
{"title":"Distributed control to steer dynamical systems to the generalized Nash equilibria for monotone aggregative games with operational constraints","authors":"Ming Li , Zhaojian Wang , Mengshuo Jia , Feng Liu , Bo Yang , Xinping Guan","doi":"10.1016/j.automatica.2025.112794","DOIUrl":"10.1016/j.automatica.2025.112794","url":null,"abstract":"<div><div>In this paper, we propose a distributed feedback controller to steer a multi-agent dynamical system to the generalized Nash equilibrium (GNE) for an aggregative game with only a monotone pseudo-gradient. First, a monotone aggregative game among the agents is formulated, which considers operational constraints in both the transient process and the steady state. Then, a distributed controller based on primal–dual gradient dynamics is designed for each agent to steer the system to track the GNE of the predefined game autonomously. The controller relaxes the requirement of the strictly/strongly monotone pseudo-gradient. At the same time, the hard limits of the control input are satisfied both at equilibrium and during the transient process. We further prove that the closed-loop system is asymptotically stable and its equilibrium attains the GNE. Finally, a practical example of frequency control on IEEE 68-bus system verifies the effectiveness of the proposed method.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112794"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799647","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.2025.112756
Ali Wadi, Kyriakos G. Vamvoudakis
In this paper, we introduce a trajectory-informed machine learning framework to address the infinite-horizon optimal control problem for uncertain quantum-mechanical systems. Our approach is inspired by physics-informed neural networks (PINNs), but is based on trajectories rather than points. Moreover, our methodology does not require knowledge of the drift term of the system’s dynamics. It also uses a finite-horizon formulation for the quantum optimal control (QOC) problem that can guarantee a unique solution to the derived Hamilton–Jacobi–Bellman (HJB) equation, which is currently not achievable with traditional PINNs. We finally provide a rigorous mathematical justification, demonstrating that the solution uniformly converges and approximates the infinite-horizon HJB for a sufficiently large finite horizon. We have validated our proposed QOC approach through simulations on the spin- particle quantum system.
{"title":"Trajectory-informed machine learning for quantum optimal control of uncertain systems","authors":"Ali Wadi, Kyriakos G. Vamvoudakis","doi":"10.1016/j.automatica.2025.112756","DOIUrl":"10.1016/j.automatica.2025.112756","url":null,"abstract":"<div><div>In this paper, we introduce a trajectory-informed machine learning framework to address the infinite-horizon optimal control problem for uncertain quantum-mechanical systems. Our approach is inspired by physics-informed neural networks (PINNs), but is based on trajectories rather than points. Moreover, our methodology does not require knowledge of the drift term of the system’s dynamics. It also uses a finite-horizon formulation for the quantum optimal control (QOC) problem that can guarantee a unique solution to the derived Hamilton–Jacobi–Bellman (HJB) equation, which is currently not achievable with traditional PINNs. We finally provide a rigorous mathematical justification, demonstrating that the solution uniformly converges and approximates the infinite-horizon HJB for a sufficiently large finite horizon. We have validated our proposed QOC approach through simulations on the spin-<span><math><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></math></span> particle quantum system.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112756"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784444","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.2025.112778
David Angeli , Davide Martini , Giacomo Innocenti , Alberto Tesi
In this paper a non-invasive approach to chaos control based on -Contraction Theory is developed. Specifically, some recent results on 2-contractive nonlinear systems are suitably extended to design a feedback controller capable to remove attractors with positive Lyapunov exponents of the open loop system, without altering the equilibrium points. First, 2-contraction stabilizability of linear control systems is discussed, showing that it can be checked by solving some linear matrix inequalities. Then, a novel technique based on 2-contraction stabilizability is devised for computing the gain matrix of a derivative feedback controller ensuring that the controlled system has the same equilibrium points of the uncontrolled one but no longer displays attractors with positive Lyapunov exponents. Finally, the classical Lorenz system is employed to illustrate the features of the proposed technique.
{"title":"Non-invasive chaos control based on 2-contraction stabilizability","authors":"David Angeli , Davide Martini , Giacomo Innocenti , Alberto Tesi","doi":"10.1016/j.automatica.2025.112778","DOIUrl":"10.1016/j.automatica.2025.112778","url":null,"abstract":"<div><div>In this paper a non-invasive approach to chaos control based on <span><math><mi>k</mi></math></span>-Contraction Theory is developed. Specifically, some recent results on 2-contractive nonlinear systems are suitably extended to design a feedback controller capable to remove attractors with positive Lyapunov exponents of the open loop system, without altering the equilibrium points. First, 2-contraction stabilizability of linear control systems is discussed, showing that it can be checked by solving some linear matrix inequalities. Then, a novel technique based on 2-contraction stabilizability is devised for computing the gain matrix of a derivative feedback controller ensuring that the controlled system has the same equilibrium points of the uncontrolled one but no longer displays attractors with positive Lyapunov exponents. Finally, the classical Lorenz system is employed to illustrate the features of the proposed technique.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112778"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799189","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.2025.112752
Chiara Romano , Alessandro Borri , Mario Di Ferdinando , Maria Domenica Di Benedetto , Pierdomenico Pepe
Ensuring stability in control systems with incomplete state information poses a significant challenge. This paper addresses this issue for nonlinear systems by leveraging the power of state observers and fast sampling. We demonstrate the following result: if the linearized system exhibits the standard structural properties of stabilizability and detectability, then the sampled-data Euler emulation of a continuous-time (Luenberger) observer-based stabilizer, designed for the linear continuous-time system, also guarantees local exponential convergence to the origin of the nonlinear system state, provided that sampling is sufficiently fast. While the result may seem expected, our work moves beyond conjecture by providing a rigorous proof that establishes this convergence under minimal assumptions. Unlike many approaches that require numerous strong assumptions for global guarantees, we forgo these requirements and accept a local result, which is still valuable in applications where understanding the system behavior around specific points is crucial. As an illustrative example, we show an application of the method to the control of the Hahnfeldt’s model of colon tumor angiogenesis, showing the potential and effectiveness of the proposed approach.
{"title":"Sampled-data observer-based exponential stabilization of nonlinear systems with an application to tumor control","authors":"Chiara Romano , Alessandro Borri , Mario Di Ferdinando , Maria Domenica Di Benedetto , Pierdomenico Pepe","doi":"10.1016/j.automatica.2025.112752","DOIUrl":"10.1016/j.automatica.2025.112752","url":null,"abstract":"<div><div>Ensuring stability in control systems with incomplete state information poses a significant challenge. This paper addresses this issue for nonlinear systems by leveraging the power of state observers and fast sampling. We demonstrate the following result: if the linearized system exhibits the standard structural properties of stabilizability and detectability, then the sampled-data Euler emulation of a continuous-time (Luenberger) observer-based stabilizer, designed for the linear continuous-time system, also guarantees local exponential convergence to the origin of the nonlinear system state, provided that sampling is sufficiently fast. While the result may seem expected, our work moves beyond conjecture by providing a rigorous proof that establishes this convergence under minimal assumptions. Unlike many approaches that require numerous strong assumptions for global guarantees, we forgo these requirements and accept a local result, which is still valuable in applications where understanding the system behavior around specific points is crucial. As an illustrative example, we show an application of the method to the control of the Hahnfeldt’s model of colon tumor angiogenesis, showing the potential and effectiveness of the proposed approach.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112752"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799652","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.2025.112745
Qingsong Liu , Aming Li , Li Chai
Understanding opinion propagation in complex networked systems has always been an important research topic in modern social sciences. It is well-known that individual’s memory and the authenticity of the information significantly influence opinion propagation in complex networks. However, how memory quantitatively affects opinion propagation is unclear, especially in the presence of fake news, and how to theoretically analyze the quantitative impact of fake information on opinion propagation with memory is still a challenge. In this paper, we propose a theoretical framework to systematically study the quantitative impacts of memory and fake news on opinion propagation. Considering the diversity of individual interactions and the varying lengths of memory in real society, we studied three key scenarios: (i) social networks with only friendly interactions, (ii) social networks with both friendly and hostile interactions, and (iii) individuals with long-term and short-term memory. Using algebraic Riccati equation and Lyapunov functional approach, we establish criteria for determining the polarization and consensus of opinions. Furthermore, necessary and sufficient conditions are provided to determine when opinions remain neutral in complex networks. Finally, numerical analyses demonstrate that the speed of the formation of the ultimate opinion is inversely proportional to the length of individual memory, suggesting that the stronger the intensity of the influence of fake news, the greater the degree of opinion polarization and the greater the amplitude of consensus opinion.
{"title":"Dynamics of opinion propagation with memory and fake news","authors":"Qingsong Liu , Aming Li , Li Chai","doi":"10.1016/j.automatica.2025.112745","DOIUrl":"10.1016/j.automatica.2025.112745","url":null,"abstract":"<div><div>Understanding opinion propagation in complex networked systems has always been an important research topic in modern social sciences. It is well-known that individual’s memory and the authenticity of the information significantly influence opinion propagation in complex networks. However, how memory quantitatively affects opinion propagation is unclear, especially in the presence of fake news, and how to theoretically analyze the quantitative impact of fake information on opinion propagation with memory is still a challenge. In this paper, we propose a theoretical framework to systematically study the quantitative impacts of memory and fake news on opinion propagation. Considering the diversity of individual interactions and the varying lengths of memory in real society, we studied three key scenarios: (i) social networks with only friendly interactions, (ii) social networks with both friendly and hostile interactions, and (iii) individuals with long-term and short-term memory. Using algebraic Riccati equation and Lyapunov functional approach, we establish criteria for determining the polarization and consensus of opinions. Furthermore, necessary and sufficient conditions are provided to determine when opinions remain neutral in complex networks. Finally, numerical analyses demonstrate that the speed of the formation of the ultimate opinion is inversely proportional to the length of individual memory, suggesting that the stronger the intensity of the influence of fake news, the greater the degree of opinion polarization and the greater the amplitude of consensus opinion.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112745"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799185","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.2025.112797
Luis Ovalle , Andres Gonzalez , Leonid Fridman , Hernan Haimovich
This paper exposes a fundamental limitation in the discrete implementation of barrier-function-based adaptive sliding-mode controllers (BFASMCs). Under sampling, the predefined performance problem originally motivating these controllers becomes theoretically unsolvable, partly because of its infinite control authority. This contradicts the empirical success of BFASMCs in digital control, revealing a gap between theory and practice. We resolve this paradox by proposing a revised control framework that incorporates actuator saturation and sampled-data dynamics. Within this framework, we derive an explicit relation between actuator capacity, sampling rate, and barrier width, offering a principled tuning strategy. Finally, to validate the theoretical findings and the discussions given in the simulation section, an experimental study is performed on a ball-and-plate system.
{"title":"Discrete implementations of sliding-mode controllers with barrier-function adaptations require a revised framework","authors":"Luis Ovalle , Andres Gonzalez , Leonid Fridman , Hernan Haimovich","doi":"10.1016/j.automatica.2025.112797","DOIUrl":"10.1016/j.automatica.2025.112797","url":null,"abstract":"<div><div>This paper exposes a fundamental limitation in the discrete implementation of barrier-function-based adaptive sliding-mode controllers (BFASMCs). Under sampling, the predefined performance problem originally motivating these controllers becomes theoretically unsolvable, partly because of its infinite control authority. This contradicts the empirical success of BFASMCs in digital control, revealing a gap between theory and practice. We resolve this paradox by proposing a revised control framework that incorporates actuator saturation and sampled-data dynamics. Within this framework, we derive an explicit relation between actuator capacity, sampling rate, and barrier width, offering a principled tuning strategy. Finally, to validate the theoretical findings and the discussions given in the simulation section, an experimental study is performed on a ball-and-plate system.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112797"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799188","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.2025.112788
Gökhan Göksu
In this study, we propose novel methods to ensure the exponential stability of switched nonlinear time-delay systems based on Lyapunov–Krasovskii, Lyapunov–Halanay and Lyapunov–Razumikhin approaches. We derive new average dwell-time bounds to establish global exponential stability by integrating Lyapunov–Krasovskii functionals and Lyapunov–Halanay functions under specified compatibility conditions. These conditions require an interchange between Lyapunov–Krasovskii functionals and Lyapunov–Halanay functions, with bounded integral operators. Additionally, we extend Halanay’s inequality to accommodate functions with growth, enabling its applicability to scenarios where growth is observed. Using the method in Loko etal (2024), we construct Lyapunov–Krasovskii functionals from Lyapunov–Razumikhin functions, to establish exponential stability within a constrained switching framework. Illustrative examples demonstrate the effectiveness of our methodologies in various scenarios involving discrete, distributed, and historywise delays, highlighting the applicability of the proposed techniques.
{"title":"Exponential stability of switched nonlinear time-delay systems combining Lyapunov–Krasovskii, Halanay and Razumikhin approaches","authors":"Gökhan Göksu","doi":"10.1016/j.automatica.2025.112788","DOIUrl":"10.1016/j.automatica.2025.112788","url":null,"abstract":"<div><div>In this study, we propose novel methods to ensure the exponential stability of switched nonlinear time-delay systems based on Lyapunov–Krasovskii, Lyapunov–Halanay and Lyapunov–Razumikhin approaches. We derive new average dwell-time bounds to establish global exponential stability by integrating Lyapunov–Krasovskii functionals and Lyapunov–Halanay functions under specified compatibility conditions. These conditions require an interchange between Lyapunov–Krasovskii functionals and Lyapunov–Halanay functions, with bounded integral operators. Additionally, we extend Halanay’s inequality to accommodate functions with growth, enabling its applicability to scenarios where growth is observed. Using the method in Loko etal (2024), we construct Lyapunov–Krasovskii functionals from Lyapunov–Razumikhin functions, to establish exponential stability within a constrained switching framework. Illustrative examples demonstrate the effectiveness of our methodologies in various scenarios involving discrete, distributed, and historywise delays, highlighting the applicability of the proposed techniques.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112788"},"PeriodicalIF":5.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799653","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 : 2025-12-18DOI: 10.1016/j.automatica.2025.112774
Changxin Liu , Xiao Tan , Xuyang Wu , Dimos V. Dimarogonas , Karl H. Johansson
Constraint satisfaction is a critical component in a wide range of engineering applications, including safe multi-agent control and economic dispatch in power systems. This study explores violation-free distributed optimization techniques for problems characterized by separable objective functions and coupling constraints. First, we incorporate auxiliary decision variables together with a network-dependent linear mapping to each coupling constraint. For the reformulated problem, we show that the projection of its feasible set onto the space of primal variables is identical to that of the original problem, which is the key to achieving all-time constraint satisfaction. Upon treating the reformulated problem as a min-min optimization problem with respect to auxiliary and primal variables, we demonstrate that the gradients in the outer minimization problem have a locally computable closed-form. Then, two violation-free distributed optimization algorithms are developed and their convergence under reasonable assumptions is analyzed. Finally, the proposed algorithm is applied to implement a control barrier function based controller in a distributed manner, and the results verify its effectiveness.
{"title":"Achieving violation-free distributed optimization under coupling constraints","authors":"Changxin Liu , Xiao Tan , Xuyang Wu , Dimos V. Dimarogonas , Karl H. Johansson","doi":"10.1016/j.automatica.2025.112774","DOIUrl":"10.1016/j.automatica.2025.112774","url":null,"abstract":"<div><div>Constraint satisfaction is a critical component in a wide range of engineering applications, including safe multi-agent control and economic dispatch in power systems. This study explores violation-free distributed optimization techniques for problems characterized by separable objective functions and coupling constraints. First, we incorporate auxiliary decision variables together with a network-dependent linear mapping to each coupling constraint. For the reformulated problem, we show that the projection of its feasible set onto the space of primal variables is identical to that of the original problem, which is the key to achieving all-time constraint satisfaction. Upon treating the reformulated problem as a min-min optimization problem with respect to auxiliary and primal variables, we demonstrate that the gradients in the outer minimization problem have a locally computable closed-form. Then, two violation-free distributed optimization algorithms are developed and their convergence under reasonable assumptions is analyzed. Finally, the proposed algorithm is applied to implement a control barrier function based controller in a distributed manner, and the results verify its effectiveness.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112774"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799191","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 : 2025-12-18DOI: 10.1016/j.automatica.2025.112770
Kaixin Lu , Ziliang Lyu , Haoyong Yu
This work studies the inverse optimality of input-to-state stabilizing controllers with input–output stability guarantees for nonlinear homogeneous systems. We formulate a new inverse optimal control problem, where the cost functional incorporates penalties on the output, in addition to the state, control and disturbance as in current related works. One benefit of penalizing the output is that the resulting inverse optimal controllers can ensure both input-to-state stability and input–output stability. We propose a technique for constructing the corresponding meaningful cost functional by using homogeneity properties, and provide sufficient conditions on solving the inverse optimal gain assignment problem. We show that homogeneous stabilizability of homogeneous systems in the case without disturbance is sufficient for the solvability of inverse optimal gain assignment problem for homogeneous systems.
{"title":"Inverse optimal design of input-to-state stabilizing homogeneous controllers for nonlinear homogeneous systems","authors":"Kaixin Lu , Ziliang Lyu , Haoyong Yu","doi":"10.1016/j.automatica.2025.112770","DOIUrl":"10.1016/j.automatica.2025.112770","url":null,"abstract":"<div><div>This work studies the inverse optimality of input-to-state stabilizing controllers with input–output stability guarantees for nonlinear homogeneous systems. We formulate a new inverse optimal control problem, where the cost functional incorporates penalties on the output, in addition to the state, control and disturbance as in current related works. One benefit of penalizing the output is that the resulting inverse optimal controllers can ensure both input-to-state stability and input–output stability. We propose a technique for constructing the corresponding meaningful cost functional by using homogeneity properties, and provide sufficient conditions on solving the inverse optimal gain assignment problem. We show that homogeneous stabilizability of homogeneous systems in the case without disturbance is sufficient for the solvability of inverse optimal gain assignment problem for homogeneous systems.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112770"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799649","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 : 2025-12-18DOI: 10.1016/j.automatica.2025.112793
Rui Gao , Jiangshuai Huang , Changyun Wen
Achieving distributed task execution with privacy preservation in multi-agent dynamical systems under eavesdropping attacks remains a challenging problem. Most existing schemes focus on eavesdroppers between adjacent agents, while state information leakage from controller or parameter estimator channels via smart eavesdroppers is rarely considered. Moreover, uncertainty of nonlinear multi-agent systems cannot be addressed by approaches developed for linear systems. This paper proposes a control framework that combines a full output mask method with an event-triggered mechanism. Output mask functions are applied to both sensor and parameter estimator channels to resist eavesdroppers on inter-agent links as well as on agents own channels. To overcome the non-differentiability of virtual controllers caused by masked outputs with unknown forms and parameters, each recursive step employs unmasked raw information. Coupling among leader-following consensus, parameter estimation, and privacy protection in uncertain nonlinear multi-agent systems is further addressed through multi-channel triggering conditions and parameter design. Theoretical analysis proves that output consensus tracking errors converge to a small set while ensuring privacy preservation, which is validated by simulation results.
{"title":"Event-triggered privacy-preserving consensus of uncertain nonlinear systems: A full output mask approach","authors":"Rui Gao , Jiangshuai Huang , Changyun Wen","doi":"10.1016/j.automatica.2025.112793","DOIUrl":"10.1016/j.automatica.2025.112793","url":null,"abstract":"<div><div>Achieving distributed task execution with privacy preservation in multi-agent dynamical systems under eavesdropping attacks remains a challenging problem. Most existing schemes focus on eavesdroppers between adjacent agents, while state information leakage from controller or parameter estimator channels via smart eavesdroppers is rarely considered. Moreover, uncertainty of nonlinear multi-agent systems cannot be addressed by approaches developed for linear systems. This paper proposes a control framework that combines a full output mask method with an event-triggered mechanism. Output mask functions are applied to both sensor and parameter estimator channels to resist eavesdroppers on inter-agent links as well as on agents own channels. To overcome the non-differentiability of virtual controllers caused by masked outputs with unknown forms and parameters, each recursive step employs unmasked raw information. Coupling among leader-following consensus, parameter estimation, and privacy protection in uncertain nonlinear multi-agent systems is further addressed through multi-channel triggering conditions and parameter design. Theoretical analysis proves that output consensus tracking errors converge to a small set while ensuring privacy preservation, which is validated by simulation results.</div></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":"185 ","pages":"Article 112793"},"PeriodicalIF":5.9,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799651","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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