A hybrid particle swarm optimization-gauss pseudo method for reentry trajectory optimization of hypersonic vehicle with navigation information model

Reentry trajectory optimization of hypersonic vehicle has been a hotspot in recent years, and the existing methods suffer from two drawbacks. First, the navigation error caused by the blackout zone is not considered in the reentry trajectory optimization model. Second, a single approach is usually applied to optimize the reentry trajectory, which fails to cover the shortage of it by combining with other approaches. To this end, a hybrid particle swarm optimization (PSO)-gauss pseudo method (GPM) algorithm, namely the hybrid PSO-GPM algorithm, is proposed to deal with the reentry trajectory optimization problem in this paper. The navigation information model reflecting the influence of the blackout zone on the global positioning system (GPS)/inertial navigation system (INS) is established first. In this model, the states of hypersonic vehicle are represented by random values obeying the normal distribution rather than the determined values, and the standard deviation is calculated from the error principle of INS. In the hybrid PSO-GPM algorithm, GPM works in the inner loop to solve the reentry trajectory optimization problem with a fast convergence and high precision under a provided initial guess. PSO plays a role in the outer loop to optimize the initial guess for GPM. Simulation results demonstrate that the established navigation information-based reentry trajectory optimization model is rational and can improve the safety level of flight. With the hybrid PSO-GPM algorithm, a better solution can be generated compared to the results when PSO algorithm and GPM are used separately.