首页|Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard Landing

Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard Landing

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
The shipboard landing problem for a quadrotor is addressed in this paper,where the ship trajectory tracking con-trol issue is transformed into a stabilization control issue by building a relative position model.To guarantee both transient performance and steady-state landing error,a prescribed perfor-mance evolution control(PPEC)method is developed for the rel-ative position control.In addition,a novel compensation system is proposed to expand the performance boundaries when the input saturation occurs and the error exceeds the predefined threshold.Considering the wind and wave on the relative position model,an adaptive sliding mode observer(ASMO)is designed for the dis-turbance with unknown upper bound.Based on the dynamic sur-face control framework,a shipboard landing controller integrat-ing PPEC and ASMO is established for the quadrotor,and the relative position control error is guaranteed to be uniformly ulti-mately bounded.Simulation results have verified the feasibility and effectiveness of the proposed shipboard landing control scheme.

Adaptive sliding mode observer(ASMO)dynamic surface controlprescribed performance evolution control(PPEC)quadrotorshipboard landing

Yang Yuan、Haibin Duan、Zhigang Zeng

展开 >

State Key Laboratory of Virtual Reality Technology and Systems,the School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,China

State Key Laboratory of Virtual Reality Technology and Systems,the School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083

Virtual Reality Fundamental Research Laboratory,Department of Mathematics and Theories,Peng Cheng Laboratory,Shenzhen 518000,China

School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China

展开 >

Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelligence"国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金Academic Excellence Foundation of BUAA for Ph.D.Students

2018AAA010080362350048T2121003U191360291948204U20B2071

2024

10.1109/JAS.2024.124254

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(5)
  • 38