文献类型：期刊文献

英文题名：Predefined-time self-structuring neural network H _∞ cooperative control for multirobot systems with prescribed performance and input quantization

作者：Liu, Haitao[1,2,3];Li, Weichen[1];Tian, Xuehong[1,2];Du, Liang[4]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Shenzhen Inst, Shenzhen 518120, Peoples R China;[3]Guangdong Engn Technol Res Ctr Ocean Equipment & M, Zhanjiang 524088, Peoples R China;[4]Guangdong Ind Polytech, Sch Mech & Elect Technol, Guangzhou 510300, Peoples R China

年份：2024

卷号：361

期号：17

外文期刊名：JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

收录：SCI-EXPANDED(收录号：WOS:001334090600001)、、EI(收录号：20243717018672)、Scopus(收录号：2-s2.0-85203283327)、WOS

基金：This work was supported by the Guangdong Basic and Applied Basic Research Foundation [grant number 2024A1515011345] , the Shenzhen Science and Technology Program [grant number JCYJ20220530162014033] , the Key Project of the Department of Edu-cation of Guangdong Province [grant number 2023ZDZX1005] , the National Natural Science Foundation of China [grant number 62171143] , the Science and Technology Program of Guangzhou [grant number 202002030243] , and the Science and Technology Planning Project of Zhanjiang City [grant numbers 2021A05023 and 2021E05012] .

语种：英文

外文关键词：Self-structuring neural network; Self-adjusting prescribed performance; Predefined-time adaptive command filter; Multirobot systems; Predefined-time H- infinity control

外文摘要：In this paper, a predefined-time adaptive command filter H- infinity controller with a self-adjusting performance function is proposed for multirobot systems. It guarantees that the tracking error meets the desired performance requirements and solves the vulnerability problem that arises in traditional prescribed performance. First, an asymmetric tan-type barrier Lyapunov function is introduced to establish asymmetric barrier constraints under input saturation and input quantization. Second, a prescribed performance with self-adjustment is introduced in the asymmetric tan-type barrier Lyapunov function, which limits the position error and changes the performance envelope based on its state. Third, a predefined-time adaptive command filter is introduced to address the "complexity explosion" issue and improve the convergence speed. Fourth, a predefined-time self-structuring neural network is introduced to fit the model uncertainty and time-varying disturbances, and a predefined-time H- infinity control strategy is designed to address the strong sudden disturbances. Finally, some simulation examples are provided to test the validity of the above algorithms.