Mingkuan Wang , Genbao Zhao , Dongsheng Yang , Juan Zhang
{"title":"Leader–follower consensus of nonlinear multi-agent systems under DoS attack based on edge-event triggered mechanisms","authors":"Mingkuan Wang , Genbao Zhao , Dongsheng Yang , Juan Zhang","doi":"10.1016/j.jai.2025.10.003","DOIUrl":"10.1016/j.jai.2025.10.003","url":null,"abstract":"<div><div>Consider that nonlinear terms for multi-agent systems (MASs) exist and information transmission among agents is interrupted by denial-of-service (DoS) attacks, the paper focuses on the leader–follower consensus of nonlinear MASs under DoS attacks based on edge-event triggered mechanisms (EETMs). Firstly, in order to minimize the transmission of unnecessary information between any two neighboring agents in MASs, new EETMs are designed for all communication edges. Secondly, a fully distributed consensus protocol under the triggered mechanisms is designed to avoid any global information. At the meantime, due to DoS attacks, the communication network is unreliable. New EETMs consensus protocol can overcome the influence of DoS attacks. Additionally, the new strategy is exclusion of the Zeno behavior. Ultimately, simulation example will demonstrate the theoretical results.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 139-146"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rongrong Wang , Jianhua Chen , Suhuan Li , Yong Zhang , Yafei Wei , Pu Xie , Hongtian Chen
{"title":"Dynamic carbon emission flow tracking with a new optimal storage schedule for power grids","authors":"Rongrong Wang , Jianhua Chen , Suhuan Li , Yong Zhang , Yafei Wei , Pu Xie , Hongtian Chen","doi":"10.1016/j.jai.2025.11.001","DOIUrl":"10.1016/j.jai.2025.11.001","url":null,"abstract":"<div><div>With the global drive toward carbon neutrality, the deep integration of variable renewable energy sources (VRES) and energy storage systems (ESS) has rendered traditional static carbon accounting methods insufficient to capture the spatiotemporal dynamics of carbon flows in power grids, highlighting the critical need for accurate tracking and equitable allocation of carbon responsibility. This paper proposes a dynamic carbon emission flow tracking framework tailored to the Jibei power grid in China, integrating a dynamic generator carbon intensity model and power transfer distribution factor (PTDF) enhanced network tracking. The framework also includes an optimal carbon allocation matrix and a predictive ESS scheduling model that links the carbon intensity during charging periods to emissions during discharging. Validated using a modified IEEE 30-bus system representing five cities in the Jibei region, results show that the dynamic model achieves a 15.2% higher accuracy than static methods, optimal ESS scheduling reduces system-wide emissions by 8.7%, and the framework maintains over 93% tracking accuracy under extreme uncertainties. Moreover, the framework quantifies inter-city carbon transfers and allocates responsibilities among grid participants, thus enabling real-time monitoring. It provides a robust foundation for carbon-aware dispatch and nodal carbon pricing, supporting the transition toward carbon-neutral power systems.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 172-181"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guanzheng Wang , Xiangke Wang , Xiaoxin Li , Zhihong Liu , Zhiqiang Miao
{"title":"Learning minimum-time flight control for fixed-wing UAVs in three-dimensional space with high-fidelity 6-DOF dynamics","authors":"Guanzheng Wang , Xiangke Wang , Xiaoxin Li , Zhihong Liu , Zhiqiang Miao","doi":"10.1016/j.jai.2025.11.007","DOIUrl":"10.1016/j.jai.2025.11.007","url":null,"abstract":"<div><div>Minimizing flight time while ensuring stability and efficiency presents a significant challenge in UAV control. This paper introduces velocity-level and actuator-level control approaches for addressing the minimum-time flight problem of fixed-wing UAVs, utilizing a six degrees of freedom (6-DOF) high-fidelity dynamic model and reinforcement learning. We evaluate four state-of-the-art reinforcement learning algorithms through extensive simulations and compare their performance against traditional methods. The results demonstrate that the proposed velocity-level and actuator-level control methods, based on Proximal Policy Optimization (PPO), achieve a substantial improvement in flight efficiency while maintaining strong generalization and stability. Specifically, the PPO-based actuator-level controller fully leverages the UAV’s maneuverability, resulting in a 27.6% reduction in average flight time compared to traditional controllers. The code is available as open source at <span><span>https://github.com/running-mars/OpenFlight</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 126-138"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa
{"title":"Learning symbolic models of dynamical systems through Kolmogorov–Arnold Networks (KANs) in centralized and distributed settings","authors":"Alessandro Giuseppi, Danilo Menegatti, Antonio Pietrabissa","doi":"10.1016/j.jai.2025.11.005","DOIUrl":"10.1016/j.jai.2025.11.005","url":null,"abstract":"<div><div>Identifying an interpretable and tractable model is a crucial step for the analysis and control of dynamical systems. In this work, we employ the recently introduced Kolmogorov–Arnold Networks (KANs), a novel neural network architecture tailored for symbolic regression and interpretability, to learn symbolic models directly from data without any a priori knowledge of the observed dynamics. We then extend our result to the distributed case through federated learning introducing the FedKANs algorithm. FedKANs allows agents observing similar, but non-identical, systems to cooperate to learn more efficiently a symbolic model without the need to exchange any process data. To our knowledge, this represents the first distributed deep learning framework for symbolic regression in dynamical systems. Numerical simulations validate the proposed solutions in various settings, involving linear, nonlinear, discrete-time and continuous-time dynamics.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 155-165"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adaptive identification of distributed parameters in nonlinear PDE setting: Sine-Gordon case study","authors":"Yury Orlov , Boris Andrievsky","doi":"10.1016/j.jai.2025.10.007","DOIUrl":"10.1016/j.jai.2025.10.007","url":null,"abstract":"<div><div>The primary concern of the work is to develop adaptive identification approach to uncertain dynamic systems governed by nonlinear partial differential equations. The well-known nonlinear sine-Gordon PDE model, which describes distributed wave dynamics (e.g., of continuum of interacting oscillators), serves as a testbed. It is shown that the sine-Gordon model with uncertain external force and viscous friction, which are distributed in space, is identifiable over the entire state measurement provided that it is excited by a specific nonzero input. Numerical simulations support the theory in the case where unknown plant parameters are spatially invariant.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 147-154"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Security stabilization for switched systems via dynamic sampling strategies","authors":"Yuanshan Liu , Jinjiang Li , Yude Xia","doi":"10.1016/j.jai.2025.10.005","DOIUrl":"10.1016/j.jai.2025.10.005","url":null,"abstract":"<div><div>This paper investigates the problem of state feedback stabilization for a class of planar switched nonlinear systems (PSNSs) subject to Denial-of-Service (DoS) attacks. By incorporating a technique that adding a power integrator and developing switched dynamic event-triggered mechanisms (SDE-TMs), a continuous state feedback stabilizer is constructed. The SDE-TMs are intelligently designed by selecting appropriate parameters. The novelty of the proposed scheme lies in its unified design, which enables steady state feedback stabilization for PSNSs under DoS conditions, without necessitating any modifications to the controller gain parameters. Finally, one simulation is expressed to verify the effectiveness of the proposed algorithm.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 166-171"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A survey of safety control for service robots","authors":"Lin Zhang , Peng Shi , Shuoyu Wang , Xiaojie Su","doi":"10.1016/j.jai.2025.10.002","DOIUrl":"10.1016/j.jai.2025.10.002","url":null,"abstract":"<div><div>Safety constitutes the fundamental bottleneck in human–robot integration, particularly for service robots operating in unstructured environments. This survey systematically reviews the research progress in the field of robot safety control, focusing on injury prevention and resolution mechanisms in physical human–robot interaction scenarios. Firstly, by deconstructing the safety boundaries of robot behavior, the concept of robot safety domain was established, and safety control objectives for three distinct operational states are briefly summarized. For each hierarchical objective, the article deeply analyzes corresponding key control strategies and their efficacy. This survey not only summarizes the existing methods, but also highlights the challenges in the future, providing guidance for subsequent research in service robot safety and laying a foundation for advancing trustworthy human–robot coexistence amid escalating autonomy demands.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 2","pages":"Pages 112-125"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiyao Yu , Yifan Wang , Dongyu Li , Ruihang Ji , Shuzhi Sam Ge
{"title":"Adaptive event-triggered coding and decoding scheme based on fuzzy logic","authors":"Yiyao Yu , Yifan Wang , Dongyu Li , Ruihang Ji , Shuzhi Sam Ge","doi":"10.1016/j.jai.2025.08.001","DOIUrl":"10.1016/j.jai.2025.08.001","url":null,"abstract":"<div><div>In this paper, we propose a fuzzy logic-based coded event-triggered control with self-adjustable prescribed performance (FL-CEC-SPP) to address the trade-off between control performance and communication efficiency in resource-constrained networked control systems. The method integrates a fuzzy-coded event-triggered controller into a coded control framework to dynamically adjust the triggering threshold, thereby reducing unnecessary transmissions while maintaining system stability. A self-adjustable prescribed performance constraint is also incorporated to ensure that the tracking error remains within predefined bounds under arbitrary initial conditions. Theoretical analyses and simulation comparisons show that the method proposed in this paper maintains good tracking performance and stability while reducing the communication burden, and has wide applications in resource-constrained network control systems.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 2-12"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Output feedback prescribed performance state synchronization for leader-following high-order uncertain nonlinear multi-agent systems","authors":"Ilias Katsoukis, George A. Rovithakis","doi":"10.1016/j.jai.2025.09.001","DOIUrl":"10.1016/j.jai.2025.09.001","url":null,"abstract":"<div><div>This paper addresses the synchronization of follower agents’ state vectors with that of a leader in high-order nonlinear multi-agent systems. The proposed low-complexity control scheme employs high-gain observers to estimate higher-order synchronization errors, enabling the controller to rely solely on relative output measurements. This approach significantly reduces the dependence on full-state information, which is often infeasible or costly in practical engineering applications. An output feedback control strategy is developed to overcome these limitations while ensuring robust and effective synchronization. Simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the theoretical findings.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 35-45"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Feng , Damián Marelli , Chen Wang , Minyue Fu , Tianju Sui
{"title":"Coding-based distributed filtering for cyber–physical systems under denial of service attacks","authors":"Shuang Feng , Damián Marelli , Chen Wang , Minyue Fu , Tianju Sui","doi":"10.1016/j.jai.2025.06.002","DOIUrl":"10.1016/j.jai.2025.06.002","url":null,"abstract":"<div><div>This article investigates the distributed recursive filtering problem for discrete-time stochastic cyber–physical systems. A particular feature of our work is that we consider systems in which the state is constrained by saturation. Measurements are transmitted to nodes of a sensor network over unreliable wireless channels. We propose a linear coding mechanism, together with a distributed method for obtaining a state estimate at each node. These designs aim to minimize the state estimation error covariance. In addition, we derive a bound on this covariance, and accommodate the design parameters to minimize this bound. The resulting design depends on the packet loss probabilities of the wireless channels. This permits applying the proposed scheme to systems in which communications suffer from denial-of-service attacks, as such attacks typically affect those probabilities. Finally, we present a numerical example illustrating this application.</div></div>","PeriodicalId":100755,"journal":{"name":"Journal of Automation and Intelligence","volume":"5 1","pages":"Pages 13-23"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147556915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}