控制理论(社会学)
初始化
稳健性(进化)
滑模控制
计算机科学
变结构控制
自适应控制
控制器(灌溉)
李雅普诺夫函数
趋同(经济学)
控制工程
控制(管理)
工程类
非线性系统
人工智能
物理
经济增长
农学
经济
基因
化学
程序设计语言
生物
量子力学
生物化学
作者
Huifang Kong,Tiankuo Liu,Yao Fang,Jiapeng Yan
标识
DOI:10.1177/01423312231156241
摘要
In this paper, a fixed-time adaptive recursive sliding mode (FTARSM) control scheme is addressed for a steer-by-wire (SbW) automated guided vehicle against model uncertainties and disturbances. First, based on a newly constructed faster fixed-time stable system, a fixed-time recursive sliding structure is developed to guarantee the SbW system fixed-time convergence, where the setting time is independent of initial conditions. By making appropriate initialization settings for the recursive structure, the sliding reaching phase is removed and the control robustness is improved. Then, the extreme learning machine (ELM) is incorporated into the FTARSM controller to estimate the lumped uncertainties upper bound, thus not only the requirement for prior bounds information in controller design is eliminated but also the control chattering is suppressed effectively. Rigorous Lyapunov analyses are further employed to ensure fixed-time closed-loop stability. Finally, the superior performance of the derived control law is verified by experimental results.
科研通智能强力驱动
Strongly Powered by AbleSci AI