| Summary: | 博士 === 國防大學理工學院 === 國防科學研究所 === 104 === Missile has already become a sharp weapon, and could deter enemy actions. However, along with the improvement of fighter maneuver performance, the performance of missile has much challenge. In which, the guidance law is the one of the key factors. Therefore, the missile guidance law that constructs good guidance performances has much attention. Artificial intelligence (AI) algorithms and machine learning have been successful used in solving nonlinear, dynamic and multi-objectives optimization problem since the advent of AI algorithms and machine learning. With the spread of AI algorithms and machine learning, intelligent missile guidance is becoming more widespread. However, the intelligence missile guidance law that is designed by particle swarm optimization (PSO) algorithm is few and far. Accordingly, this work first discusses the PSO guidance (PSOG) for an air-to-air missile (AAM). The improved PSOG (IPSOG) and the proportional navigation - improved particle swarm optimization guidance (PN-IPSOG) are then presented to enhance the guidance performance. Furthermore, based on the disturbance observer designed by the input estimator (IE), the IE-IPSOG I is also presented. The simulation results indicate that the proposed models have good guidance performance and robustness for intercepting the maneuvering target.
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