Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer | |
Gao, Haiyan2; Tang, Weiqiang1; Fu, Rong2 | |
刊名 | IEEE Access |
2022 | |
卷号 | 10页码:69333-69345 |
关键词 | Altitude control Control theory Hypersonic aerodynamics Hypersonic vehicles Knowledge acquisition Learning systems Navigation Network layers Sliding mode control Disturbance observer Extreme learning machine Learning machines Network disturbances Neural network disturbance observer Neural-networks Offset-free tracking Sliding-mode control Uncertainty Vehicle's dynamics |
ISSN号 | 2169-3536 |
DOI | 10.1109/ACCESS.2022.3185256 |
英文摘要 | The novel extreme learning machine (ELM) neural network disturbance observer (NNDO) -based sliding mode control (SMC) strategy is proposed for the precise tracking control of a hypersonic vehicle (HV) under various disturbance situations. By converting nonlinear dynamics into state-dependent linear model, the control law design process is simplified, and the sliding mode control law based on the power function reaching rate is designed to suppress the chattering effect. Considering the disturbances, the ELM-NNDO is designed based on the single-hidden layer feedforward network (SLFN). Different from conventional ELM using least square optimization approach, the output weight here is updated based on the Lyapunov synthesis approach. In addition, the influences of the disturbances on the velocity and altitude are attenuated by the direct feedback compensation (DFC), and the offset-free tracking control is realized for the output reference signal. Comparison of simulation results verify the superior control performance of the proposed method. © 2013 IEEE. |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
WOS记录号 | WOS:000838380700001 |
内容类型 | 期刊论文 |
源URL | [http://ir.lut.edu.cn/handle/2XXMBERH/159385] |
专题 | 电气工程与信息工程学院 |
作者单位 | 1.Lanzhou University of Technology, College of Electrical and Information Engineering, Lanzhou; 730050, China 2.Xiamen University of Technology, School of Electrical Engineering and Automation, Xiamen; 361024, China; |
推荐引用方式 GB/T 7714 | Gao, Haiyan,Tang, Weiqiang,Fu, Rong. Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer[J]. IEEE Access,2022,10:69333-69345. |
APA | Gao, Haiyan,Tang, Weiqiang,&Fu, Rong.(2022).Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer.IEEE Access,10,69333-69345. |
MLA | Gao, Haiyan,et al."Sliding Mode Control for Hypersonic Vehicle Based on Extreme Learning Machine Neural Network Disturbance Observer".IEEE Access 10(2022):69333-69345. |
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