Neural-Network-Based Adaptive Finite-Time Control for a Two-Degree-of-Freedom Helicopter System With an Event-Triggering Mechanism  

作　　者：Zhijia Zhao Jian Zhang Shouyan Chen Wei He Keum-Shik Hong 

机构地区：[1]IEEE [2]the School of Mechanical and Electrical Engineering,Guangzhou University,Guangzhou 510006,China [3]the School of Intelligence Science and Technology [4]the Institute of Artificial Intelligence,University of Science and Technology Beijing,Beijing 100083,China [5]the School of Mechanical Engineering,Pusan National University,Busan 46241,South Korea [6]the Institute for Future,School of Automation,Qingdao University,Qingdao 266071,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第8期1754-1765,共12页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(62273112,62061160371,61933001,51905115);the Science and Technology Planning Project of Guangzhou City(202201010758);the Guangzhou University-Hong Kong University of Science and Technology Joint Research Collaboration Fund(YH202205);the Open Research Fund from the Guangdong Laboratory of Artificial Intelligence and Digital Economy(Shenzhen(SZ))(GML-KF-22-27);the Korea Institute of Energy Technology Evaluation and Planning Through the Auspices of the Ministry of Trade,Industry and Energy,Republic of Korea(20213030020160)。

摘　　要：Helicopter systems present numerous benefits over fixed-wing aircraft in several fields of application.Developing control schemes for improving the tracking accuracy of such systems is crucial.This paper proposes a neural-network(NN)-based adaptive finite-time control for a two-degree-of-freedom helicopter system.In particular,a radial basis function NN is adopted to solve uncertainty in the helicopter system.Furthermore,an event-triggering mechanism(ETM)with a switching threshold is proposed to alleviate the communication burden on the system.By proposing an adaptive parameter,a bounded estimation,and a smooth function approach,the effect of network measurement errors is effectively compensated for while simultaneously avoiding the Zeno phenomenon.Additionally,the developed adaptive finite-time control technique based on an NN guarantees finitetime convergence of the tracking error,thus enhancing the control accuracy of the system.In addition,the Lyapunov direct method demonstrates that the closed-loop system is semiglobally finite-time stable.Finally,simulation and experimental results show the effectiveness of the control strategy.

关 键 词：Adaptive neural-network control event-triggering mechanism(ETM) finite time two-degree-of-freedom helicopter 

分 类 号：V275.1[航空宇航科学与技术—飞行器设计] TP183[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]