系统工程与电子技术

• 制导、导航与控制 • 上一篇    下一篇

RLV末端能量管理段的在线轨迹规划算法

穆凌霞1, 李平1,2, 李乐尧3, 王新民1, 谢蓉1   

  1. 1. 西北工业大学自动化学院, 陕西 西安 710072; 2. 辽宁石油化工大学信息与
    控制工程学院, 辽宁 抚顺 113001; 3. 北京控制工程研究所, 北京 100190
  • 出版日期:2017-02-25 发布日期:2010-01-03

Onboard trajectory planning algorithm for terminal area energy management phase of a RLV

MU Lingxia1, LI Ping1,2, LI Leyao3, WANG Xinmin1, XIE Rong1   

  1. 1. School of Automation, Northwestern Polytechnical University, Xi’an 710072, China;
    2. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
    3. Beijing Institute of Control Engineering, Beijing 100190, China
  • Online:2017-02-25 Published:2010-01-03

摘要:

针对传统的可重复使用运载器(reusable launch vehicle, RLV)末端能量管理段轨迹生成算法存在的在线规划能力有限和制导精度不够的问题,提出了一种基于内核提取协议和微分平坦理论的三维在线轨迹规划算法。首先,推导给出了以高度为自变量、动压为状态变量的RLV三自由度运动方程组;然后,利用新方程组的平坦特性,选取纵程、横程和动压为相应的平坦输出,建立了不包含微分方程的最优控制问题;随后,采用分段B样条参数化平坦输出,将最优控制问题离散化得到非线性规划问题;最后,采用基于序列二次规划算法的稀疏非线性优化器进行求解。仿真结果验证了该末端能量管理段在线轨迹规划算法的有效性。

Abstract:

Considering the insufficient capability of online planning and limited guidance precision of traditional three-dimensional offline, or two-dimensional onboard algorithms for terminal area energy management (TAEM) phase of reusable launch vehicles (RLVs), a three-dimensional onboard trajectory planning algorithm based on kernel extraction protocol (KEP) and differential flatness theory is proposed. Firstly, the KEP three-degree-offreedom dynamic equation is derived, where the height is deemed to be an independent variable and the dynamic pressure rather than velocity is chosen as a state variable. Secondly, three flat outputs (crossrange, downrange, and, dynamic pressure) are generated based on the flatness properties of KEP equation, and an optimal control problem (OCP) without differential equation constraints is established. Thirdly, flat outputs and their derivatives are parameterized in terms of Bspline curves, and the OCP is discretized to render a nonlinear programming (NP) problem. Finally, the resulting NP problem is solved by sparse nonlinear optimizer (SNOPT). Numerical simulations are presented to illustrate the effectiveness of the proposed approach.