Evasion-Faced Predictive Optimal Guidance Algorithms for Air-Breathing Hypersonic Vehicles

Abstract

This paper investigates the evasion-faced predictive optimal guidance algorithm for the Air-Breathing Hypersonic Vehicle (AHV). Firstly, in order to minimize the evasion cost of the AHV, the energy-optimized issue has been taken into consideration, by taking full advantages of the specified miss distance (SMD). Secondly, in view of the acceleration constraint of the AHV, the bang-bang control is adopted to derivate the guidance strategy which could maximize the miss distance. Thirdly, by adjusting the weight coefficients of the energy-optimized evasion strategy to control the AHV's acceleration saturation time, switching between the above two evasion strategies, the generated composite evasion guidance command can strike a balance between acceleration constraints and energy optimization. In order to further enhance the evasion capability of the AHV, on the basis of the composite guidance command, an acceleration predictor is introduced to predict the information of the pursuing accelerations. Finally, an enhanced energy-optimized evasion predictive guidance law with stronger robustness and adaptability is synthesized. Simulation results show that the proposed guidance algorithm can enable the AHV to win its advantage in the limited time of pursuit-evasion (PE) game and successfully evade the pursuer while considering acceleration constraints and energy optimization.