弹道
控制理论(社会学)
趋同(经济学)
跟踪(教育)
控制器(灌溉)
计算机科学
观察员(物理)
水下
工程类
地质学
人工智能
控制(管理)
物理
海洋学
经济
天文
生物
量子力学
经济增长
教育学
心理学
农学
作者
Guofang Chen,Mingwei Sheng,Lei Wan,Yihui Liu,Ziyang Zhang,Yufei Xu
标识
DOI:10.1016/j.apor.2022.103281
摘要
• A multi-parameter high-order extended state observer is designed to estimate lumped uncertainties. • A fixed-time fault-tolerant adaptive trajectory tracking controller is presented. • The desired trajectory is constructed by interpolating the path. • The simulation topography is built according to the TAG active mound. This paper investigates the fixed-time trajectory tracking problem of small autonomous underwater vehicles (S-AUVs) in the Trans-Atlantic Geotraverse (TAG) active mound with ocean current, unknown disturbances, model uncertainties, actuator faults, and input saturations. First, the effect of the near-bottom environment of the mound on S-AUVs is analyzed in detail from three issues. Without the upper bound and gradient of lumped uncertainties, a high-order adaptive extended state observer (ESO) is designed. Then, a continuous fixed-time sliding mode manifold is applied to obtain fast convergence performance. In order to realize the fixed-time convergence of tracking errors, an adaptive fault-tolerant trajectory tracking control law with an auxiliary dynamic system (ADS) is proposed. In addition, the simulated topography of the TAG mound is constructed and the trajectory based on path points is modeled by cubic spline interpolation. The effectiveness and superiority of the proposed algorithm are validated by comprehensive simulations.
科研通智能强力驱动
Strongly Powered by AbleSci AI