文献类型：期刊文献

英文题名：Event-Triggered Prescribed-Time Resilient Control of Euler-Lagrange Systems With Deception Attacks and Deferred Constraints

作者：Hu, Yunsong[1,2];Yan, Huaicheng[1];Wang, Yuan[1];You, Zheng[3];Zhao, Yu[1];Lv, Yunkai[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Smart Mfg Energy Chem Proc, Shanghai, Peoples R China;[2]Changzhou Univ, Sch Microelect & Control Engn, Changzhou, Peoples R China;[3]Jimei Univ, Dept Naval Architecture & Ocean Engn, Xiamen, Peoples R China

年份：2026

外文期刊名：INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL

收录：;EI(收录号：20262821059633);Scopus(收录号：2-s2.0-105043909268);WOS:【SCI-EXPANDED(收录号:WOS:001812384100001)】；

基金：This work is supported by the National Natural Science Foundation of China (Grant Nos. 62333005, 62503065, 62303194, 62503349, 62403200, 62533002); in part by the Natural Science Foundation of Colleges and Universities of Jiangsu Province (Grant Nos. 25KJB120001, 25KJB120008); in part by the Basic Research Program of the Changzhou Science and Technology Bureau (Grant No. CJ20250035); in part by the Natural Science Foundation of Fujian Province (Grant Nos. 2026J009059, 2024J01711); in part by the State Key Laboratory of Autonomous Intelligent Unmanned Systems (Grant No. ZZKF2025-1-29); in part by the Basic Research Program of Jiangsu (Grant No. BK20250987); in part by the Shanghai Natural Science Foundation (Grant No. 24ZR1416200); in part by the Shanghai Aerospace Science and Technology Innovation Fund (Grant No. SAST2024-066).

语种：英文

外文关键词：barrier Lyapunov function; deception attacks; deferred constraints; Euler-Lagrange systems; event-triggered control; resilient control

摘要：In this paper, an event-triggered prescribed-time resilient control scheme for uncertain Euler-Lagrange (EL) systems with deception attacks and deferred constraints is presented. First, to mitigate the effects of false data injection (FDI) attacks in the sensor channel, a novel coordinate transformation and the Nussbaum gain technique are applied under the framework of the backstepping method. The unknown actuator attacks are compensated by the application of a radial basis function neural network (RBFNN). Then, distinguishing from most existing resilient control algorithms that do not consider the transient characteristics of the output signal, the prescribed-time performance functions (PTPFs) and a barrier Lyapunov function (BLF) are merged in this paper, so that the output signal can converge to an adjustable set within a predefined time. To guarantee that the system satisfies the deferred constraint, a transformation function is introduced. What's more, we construct an improved event-triggered mechanism (ETM), which can utilize communication resources more efficiently. Finally, simulation results based on a two-link manipulator model are depicted to showcase the effectiveness of the proposed method.