Landing footprint computation based on particle swarm optimization

To enforce the flexibility of the landing footprint computation, a hybrid optimization method based on the constrained particle swarm optimization (PSO) and bank angle reversal logic is proposed for the lifting hypersonic reentry vehicles. The parameterized bank angle profile is developed in order to reduce the searching space of the unknown variables. An improved PSO algorithmis used to optimize the reentry gliding trajectory satisfying both the path and the terminal constraints. The set of bank angle commands is obtained by imposing the forward and backward bank angle reversal logics, which can fast generate the approximate landing footprint for hypersonic reentry vehicles. Based on the high-lifting common aerial vehicle (CAV-H) model, a numerical simulation is performed. The results show that the hybrid optimization method is simple in design and independent of the initial estimation of the unknown parameters. The results also demonstrate the feasibility of the proposed algorithm for rapid generation of the landing footprint.