A heuristic predictive LOS guidance law based on trajectory learning, ant colony optimization and tabu search

Abstract

A heuristic predictive line-of-sight (LOS) guidance law is introduced to intercept a high-speed maneuvering target. A combination of continuous ant colony system and tabu search optimization algorithms is proposed to generate the optimal predictive commands of LOS guidance law. Prediction is driven by the previous positions of the target to estimate the next positions of it. Thus, the guidance system is continually solving a dynamic optimization problem in order to determine the acceleration commands by minimizing a cost function subject to actuators saturation. This innovation distinguishes the proposed guidance law from the classic LOS guidance, described by a simple relation between the deviation from the LOS and the guidance commands and use only the current measurement. The performance of the proposed guidance law is verified by carrying out several simulations of three dimensional scenarios. By still requiring simple and low-cost hardware in the new guidance law, simulation results show that the proposed heuristic predictive line-of-sight guidance law can prepare comparable performance in the whole case studies which show the robustness and adaptability of the proposed scheme.