Abstract:

The utilization of reusable launch vehicles has emerged as a prominent research focus owing to their significantly lower launch costs. Vertical sub-stage recovery technology is one of the most successful rocket recovery technologies at present， which requires that the recovery section needs to control the attitude of the rocket to keep it perpendicular to the ground. However， in the terminal deceleration phase of recovery， controlling the rocket relies solely on engine gimballing angles to obtain control moments， thus encountering constraints related to moment saturation. Moreover， deviations in thrust estimation contribute to errors in control moments. In this paper， we introduce an intelligent model predictive control algorithm to achieve attitude control during the terminal deceleration phase. Building upon traditional model predictive control， it employs neural networks to approximate the optimal value function， effectively reducing the computational load of model predictive control. Furthermore， in consideration of potential engine faults， along with constraints related to saturation and thrust estimation errors， a fault-tolerant optimal control allocation algorithm based on quadratic programming is devised. Simulation results substantiate the effectiveness of the proposed methodology.

Key words: reusable launch vehicles, engine faults, fault tolerant control, control allocation, model predictive control, neural network

摘要：

可回收运载火箭由于发射成本低的显著优势已经成为当前新的研究热点。子级垂直回收技术是目前最为成功的火箭回收技术之一，要求回收段控制火箭姿态保持与地面垂直。然而火箭在回收的末减速段只能依靠发动机摆动角度获取控制力矩，因而存在力矩饱和约束；同时推力估计的误差也会带来控制力矩的偏差。基于上述问题，设计了智能模型预测控制算法实现了末减速段的姿态控制任务。在传统模型预测控制的基础上，采用神经网络逼近最优的值函数，有效降低了模型预测控制的计算量；考虑到发动机出现故障的情况，考虑饱和约束和推力估计偏差，基于二次规划设计了容错最优控制分配算法，实现了故障和饱和情况下的姿态控制任务。仿真结果说明了所提出方法的有效性。

关键词: 可回收火箭, 发动机故障, 容错控制, 控制分配, 模型预测控制, 神经网络