A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

Author:

Li Chuanfeng12ORCID,Wu Hao3,Yang Zhile4ORCID,Wang Yongji5ORCID,Sun Zeyu1ORCID

Affiliation:

1. School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China

2. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT9 5AH, UK

3. Beijing Aerospace Automation Control Institute, Beijing 100854, China

4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China

5. School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract

Hypersonic vehicle is a typical parameter uncertain system with significant characteristics of strong coupling, nonlinearity, and external disturbance. In this paper, a combined system modeling approach is proposed to approximate the actual vehicle system. The state feedback control strategy is adopted based on the robust guaranteed cost control (RGCC) theory, where the Lyapunov function is applied to get control law for nonlinear system and the problem is transformed into a feasible solution by linear matrix inequalities (LMI) method. In addition, a nonfragile guaranteed cost controller solved by LMI optimization approach is employed to the linear error system, where a single hidden layer neural network (SHLNN) is employed as an additive gain compensator to reduce excessive performance caused by perturbations and uncertainties. Simulation results show the stability and well tracking performance for the proposed strategy in controlling the vehicle system.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neural Network-Based Information Transfer for Dynamic Optimization;IEEE Transactions on Neural Networks and Learning Systems;2020-05

2. Nonlinear Trajectory Controller with Improved Performances for Waveriders;Complexity;2019-03-24

3. Butterfly Recognition Based on Faster R-CNN;Journal of Physics: Conference Series;2019-03

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