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Aerodynamic/reaction-jet compound control of hypersonic reentry vehicle using sliding mode control and neural learning

机译:使用滑模控制和神经学习高超声反应式车辆的空气动力/反应射流控制

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Considering the saturation of the aerodynamic surface (ADS) and the limitation of the actuator input, the paper investigates the aerodynamic/reaction-jet compound attitude control for the hypersonic reentry vehicle subject to poor aerodynamic maneuverability. The attitude control torque is preferentially allocated to the ADS. If the ADS is saturated, the remaining control torque is distributed to the reaction control system (RCS) through the control distribution algorithm to assist the ADS. For the dynamics uncertainty in the calculation of control torque, the terminal sliding mode (TSM) controller based on the back-stepping frame is designed to improve the robust performance and achieve the finite-time convergence. Furthermore, the predefined-time TSM controller is constructed with the online-data neural learning and the disturbance observer, which guarantee the predefined-time convergence and complete the effective approximation of system dynamics uncertainty. The stability of the attitude system is mathematically proved via Lyapunov function, while the simulation results are provided to show the effectiveness of the proposed controllers. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译:考虑到空气动力学表面(ADS)的饱和度和致动器输入的限制,该纸张研究了超声波再入式车辆的空气动力/反应射流姿态控制,其受到差的空气动力转放性。姿态控制扭矩优先分配给广告。如果ADS饱和,则通过控制分布算法将其余控制扭矩分布到反应控制系统(RCS)以帮助广告。对于控制扭矩计算的动态不确定性,终端滑动模式(TSM)控制器基于后台踏板帧设计用于提高鲁棒性能并实现有限时间的收敛。此外,预定义时间TSM控制器由在线数据神经学习和扰动观察者构建,其保证预定义 - 时间收敛并完成系统动态不确定性的有效近似。姿态系统的稳定性通过Lyapunov函数来证明,提供了模拟结果以显示所提出的控制器的有效性。 (c)2021 Elsevier Masson SAS。版权所有。

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