Entry Trajectory Optimization With General Polygonal No-Fly Zone Constraints

Trajectory optimization of a hypersonic glide vehicle (HGV) has a highly nonlinear model and complex constraints. Therefore, it is difficult to deal with polygonal no-fly zone (PNFZ) constraints that are common in actual flight environment of the HGV. To solve this problem, this article proposes a general PNFZ avoidance (GPNA) algorithm to solve the HGV trajectory optimization problem with general PNFZ (convex and concave PNFZs) constraints. To the author's knowledge, this problem has never been solved before. First, the trajectory optimization problem with the convex PNFZ constraint is considered. Inspired by sequential convex programming (SCP), direct linearization and relaxation techniques are used to transform this problem into a sequence of mixed-integer linear programming problems, which can be solved efficiently by state-of-the-art software. Different from the convexification process of SCP, this article also linearizes the nonlinear convex functions. At the same time, PNFZs of actual mission are usually concave, so concave PNFZ is also considered. The convex decomposition technique is used to decompose it into multiple convex PNFZs so that the GPNA algorithm can solve more practical problems. In addition, we also consider inter-sample obstacle avoidance constraints. Simulations in various scenarios verify the effectiveness of the GPNA algorithm. The GPNA algorithm has strong adaptability and high computational efficiency, and can generate trajectories avoiding the general PNFZ within 10 s, which has a strong application value.