基于神经网络的RLV再入段有限时间自适应姿态控制

摘要

A neural network based finite-time-stable adaptive attitude control strategy for a reusable launch vehicle (RLV) reentry is proposed.Firstly,the traditional RLV dynamic model is improved by separating out the uncertainty,coupled dynamic and disturbance together as combined disturbance.Then,adaptive estimation for the combined disturbance based on radical basis function neural network (RBFNN) is introduced into a nominal controller as feed-forward compensation.Moreover,the terminal attractor is used to improve the controller so that the desired system state could be tracked in finite time,and a robust control function is also introduced so as to reduce the impact on control accuracy from the error of RBFNN estimation.Only the nominal parameters of the system rather than the precise value and bounds of disturbance are utilized for the proposed controller.Finally,the effectiveness of the controller is demonstrated by the numerical simulations.%针对可重复使用运载器(RLV)再入段的姿态控制问题,提出了一种基于神经网络的有限时间自适应姿态跟踪控制方法.首先,在传统RLV建模的基础上将模型不确定性、耦合及扰动力矩分离作为复合扰动;然后,利用径向基神经网络(RBFNN)对其在线估计并在标称控制器中进行动态前馈补偿;最后,利用终端吸引子改进控制器实现了对期望状态的有限时间跟踪,并通过引入鲁棒项降低了RBFNN估计误差对控制精度的影响.设计的姿态控制器无需获知精确的气动数据与扰动范围而仅需某飞行状态下的标称值.仿真结果表明提出的控制方法对RLV再入姿态跟踪具有较好的控制效果.