Neural network based integral sliding mode optimal flight control of near space hypersonic vehicle
Neural network based integral sliding mode optimal flight control of near space hypersonic vehicle
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DOI：
10.1016/j.neucom.2019.10.038
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发表时间：
2020-02
期刊：
影响因子：
6
通讯作者：
Rongsheng Xia;Mou Chen;Qingxian Wu;Yuhui Wang
中科院分区：
文献类型：
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作者：
Rongsheng Xia;Mou Chen;Qingxian Wu;Yuhui Wang
基于积分滑模方法和自适应动态规划（ADP）算法，针对存在未知建模误差、外部干扰和输入饱和的临近空间高超声速飞行器（NSHV）系统，提出了一种鲁棒最优跟踪控制方案。首先，结合神经网络、辅助系统和积分滑模控制方法，设计了自适应积分滑模控制律，保证系统轨迹趋于定义的积分滑模面，消除了建模不确定性、外部干扰和控制输入饱和等因素的影响。然后，将原系统的鲁棒最优跟踪控制问题转化为标称系统的最优控制问题，利用单评价网络ADP方法获得相应的最优控制器。此外，李雅普诺夫分析方法表明，包含AISMC规律和最优控制器的总体控制输入可以确保闭环系统中的所有信号在一致最终有界（UUB）意义下稳定。最后，通过对NSHV姿态飞行控制的仿真，验证了所提控制方案的有效性。
In this paper, based on the integral sliding mode method and adaptive dynamic programming (ADP) algorithm, a robust optimal tracking control scheme is presented for near space hypersonic vehicle (NSHV) system in the presence of unknown modeling error, external disturbance, and input saturation. Firstly, combining neural network, auxiliary system and integral sliding mode methods, an adaptive integral sliding mode control (AISMC) law is designed to guarantee system trajectories tend to a defined integral sliding surface and the effects of modeling uncertainty, external disturbance, and control input saturation are eliminated. Then, the robust optimal tracking control problem of original system is converted into the optimal control problem of a nominal system, and an ADP method with single critic network is utilized to acquire the corresponding optimal controller. Furthermore, Lyapunov analysis method shows that the overall control input which contains AISMC law and optimal controller can ensure all the signals in closed-loop system are stable in the sense of uniform ultimate boundedness (UUB). Finally, simulation results about attitude flight control of NSHV are given to verify the effectiveness of the proposed control scheme.