Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard LandingOACSTPCDEI
Prescribed Performance Evolution Control for Quadrotor Autonomous Shipboard Landing
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.
Yang Yuan;Haibin Duan;Zhigang Zeng
State Key Laboratory of Virtual Reality Technology and Systems,the School of Automation Science and Electrical Engineering,Beihang University,Beijing 100083,ChinaState 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,ChinaSchool of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
Adaptive sliding mode observer(ASMO)dynamic surface controlprescribed performance evolution control(PPEC)quadrotorshipboard landing
《自动化学报（英文版）》 2024 (005)
1151-1162 / 12
This work was partially supported by Science and Technology Innovation 2030-Key Project of"New Generation Artificial Intelligence"(2018AAA0100803),the National Natural Science Foundation of China(62350048,T2121003,U1913602,91948204,U20B2071),and the Academic Excellence Foundation of BUAA for Ph.D.Students.
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