Evolutionary Method Based Hybrid Entry Guidance Strategy for Reentry Vehicles

This paper presents a hybrid approach combining Pigeon Inspired Optimization (PIO) with Gauss-Newton method for entry guidance of winged vehicles. The bank angle modulation is considered as the primary control. In the hybrid guidance approach, PIO algorithm is initially used to find a bank angle that satisfies a predefined cost function. In the second phase, the corresponding bank angle is updated to correct the terminal errors using Gauss-Newton algorithm. Advantages of PIO algorithm are that it does not require an initial guess and that equality and inequality constraints can be incorporated, apart from the fact that it has global convergence and randomness. Gauss-Newton method, however, is deterministic and ensures global convergence with high accuracy given an initial guess. Thus, hybrid guidance algorithm exploits the benefits of both and determines an optimal bank angle profile that steers the vehicle to destination accurately, satisfying the path constraints. The simulation results show effectiveness of the proposed algorithm.