文献类型：期刊文献

英文题名：Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation

作者：Hu, Yunsong[1];Yan, Huaicheng[1,2];Zhang, Hao[3];Wang, Meng[1];Zeng, Lu[4]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China;[3]Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China;[4]Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China

年份：2023

卷号：53

期号：4

起止页码：2636

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20221712038625);WOS:【SCI-EXPANDED(收录号:WOS:000785796100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62173146, Grant 62073143, Grant 61922063, and Grant 62003139; in part by the Shanghai Natural Science Foundation under Grant 22ZR1416200 and Grant 20ZR1415200; in part by the Program of Shanghai Academic Research Leader under Grant 19XD1421000; in part by the Shanghai and HongKong-Macao-Taiwan Science and Technology Cooperation Project under Grant 19510760200; in part by the Shanghai Shuguang Project under Grant 18SG18; and in part by the Innovation Program of Shanghai Municipal Education Commission under Grant 2021-01-07-00-02-E00107. This article was recommended by Associate Editor H. Zhang.

语种：英文

外文关键词：Robots; Convergence; Uncertainty; Sliding mode control; Manipulator dynamics; Dynamical systems; Artificial neural networks; Fixed-time control; input saturation; neural networks (NNs); nonsingular fast terminal sliding mode (NFTSM)

摘要：In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.