Design of Optimal Attack-Angle for RLV Reentry Based on Quantum Particle Swarm Optimization

The attack-angle optimization is a key problem for reentry trajectory design of a gliding type reusable launch vehicle (RLV). In order to solve such a problem, the equations of motion are derived first. A physical programming (PP) method is briefly presented and the preference function is reflected in mathematical representation. The attack-angle optimization problem with four criteria (i.e., downrange, total heat, heat rate, and trajectory oscillation) is converted into a single-objective optimization problem based on the PP method. A winged gliding reentry RLV is chosen as a simulation example and the transformed single-objective problem is solved by the quantum-behaved particle swarm optimization (QPSO) algorithm based on two types of preference structures, longer range preference and smaller total heat preference. The constraints of maximizing heating rate, normal load factor, and dynamic pressure and minimizing terminal velocity are handled by a penalty function method. The simulation results demonstrate the efficiency of these methods. The physical causation of the optimal solution and the typical profiles are presented, which reflect the designer's preference. At last, the feasibility and advantages of QPSO are revealed by comparison with the results of genetic algorithm (GA) and standard particle swarm optimization (PSO) algorithm on this optimization problem.