Evolutionary method based integrated guidance strategy for reentry vehicles

摘要

In this paper, the guidance problem of winged re-entry vehicle with path constraints has been solved using an integrated guidance strategy that combines evolutionary method based pigeon inspired optimization (PIO) with gradient based Gauss Newton (GN) optimization algorithm. Re-entry phase is an unpowered flight that has bank angle modulation as the primary control variable. The bank angle is parametrized to be linear with respect to energy. This reduces the guidance problem to single parameter search problem. In the first phase of the integrated guidance scheme, PIO is used to find a bank angle that satisfies a predefined objective function. The corresponding bank angle is further updated by the GN algorithm to minimize the terminal error in the range-to-go. GN algorithm is used as a part of predictor–corrector guidance algorithm that requires an initial guess of the bank angle in each guidance cycle. The choice of initial guess has been eliminated in the proposed algorithm by incorporating PIO. Results of the proposed algorithm have been compared with the traditional predictor–corrector (PC) algorithm. It has been observed that the performance of proposed algorithm is as good as that of PC algorithm with added advantage of being insensitive to initial guess requirement and also overcomes the divergence issues.