变信赖域序列凸规划RLV再入轨迹在线重构

针对可重复使用运载器（RLV）的再入轨迹重构问题,提出一种基于变信赖域序列凸规划的RLV再入轨迹快速求解方法. 首先,通过离散化及对非凸约束的线性化处理,将RLV的非凸轨迹优化问题转换为凸优化问题,然后通过序列凸规划方法对凸优化问题进行求解. 在序列凸规划求解过程的初始迭代中,采用预测校正算法对初值猜测轨迹进行设计,确定轨迹求解的终端时间；在后续迭代过程中,设计基于优化性能指标的信赖域更新策略,提升算法的收敛性能. 在轨迹快速求解方法的基础上,考虑RLV再入过程中可能发生的突发事件,如实际轨迹大幅度偏离参考轨迹或目标点变更,基于变化的初值约束及终端约束在线重构轨迹,并结合重构轨迹和LQR（Linear quadratic regulator）方法设计再入制导律实现对重构轨迹的有效跟踪. 最后,将此设计方法与Gauss伪谱法及传统序列凸规划算法进行仿真对比验证. 仿真结果表明:变信赖域序列凸规划方法相较于伪谱法和传统的序列凸规划方法在轨迹求解实时性及收敛性方面有较大的提升,具备应用于轨迹在线重构的能力,此外,所提出的轨迹在线重构方法具备良好的鲁棒性以及抗扰性.

Variable trust region sequential convex programming for RLV online reentry trajectory reconstruction

Aiming at the reentry trajectory reconstruction problem of reusable launch vehicle (RLV), a fast solving method based on variable trust region sequential convex programming (SCP) was proposed. First, the non-convex trajectory optimization problem was convexified by discretization and linearizing the non-convex constraints. Then the convex optimization problem was solved using the SCP method. In the initial iteration of SCP, a predictor corrector algorithm was applied to design the initial guessing trajectory and determine the terminal time. In the subsequent iteration, a variable trust region strategy was proposed based on optimization performance indexes, which improved the convergence performance of the algorithm. On the basis of the fast solving method, considering the unexpected events that may occur during the RLV reentry process, such as large deviation and target point changing, the trajectory was reconstructed online taking the changed initial and terminal conditions into account and was tracked effectively using LQR (Linear quadratic regulator) method. Finally, the designed method was compared with the Gauss pseudospectral method and traditional SCP algorithm. Simulation results show that compared with the pseudo-spectrum method and the traditional SCP method, the variable trust region SCP method greatly improved the real-time and convergence of the trajectory solution, and has the ability to be applied to online trajectory reconstruction. Besides, the proposed online trajectory reconstruction method has good robustness and immunity.