Abstract
This paper proposes a modified particle swarm optimization guidance (MPSOG) for the pursuit-evasion optimization problem. If the line-of-sight (LOS) rate equals zero, the curvature of the missile’s trajectory would be smaller and the probability of intercept greater. Thus, we propose using a MPSOG to improve the guidance performance. The MPSOG uses a Kalman filter to predict a target’s dynamic. The lateral acceleration commands of the y- and z-axis are optimized by the particle swarm optimization (PSO) algorithm, respectively. Numerical simulation results show that the MPSOG have better guidance performance than proportional navigation guidance and particle swarm optimization guidance in miss distance, time-to-go, and final lateral acceleration commands.
Highlights
This paper proposes a modified particle swarm optimization guidance (MPSOG) for the pursuit-evasion optimization problem
We propose a modified PSO guidance (MPSOG) to improve the guidance performance
These results revealed that the MPSOG has a higher tracking ability and accuracy
Summary
Since the inception of missile technology, the research of guidance laws, which guide a missile to attack its target effectively, has attracted a lot of attention. With the development of control theory, optimal control theory has been utilized in missile guidance design. Song et al [10] proposed an on-line suboptimal midcourse guidance law which used neural networks for air-to-air mediumrange missiles. Since the inception of a particle swarm optimization (PSO) algorithm, its many advantages which include its few parameters, fast convergence, simple scheme and easy implementation have caused concern. Some studies, such as Parsopoulos et al [12] and Kuo et al [13] have pointed out that the capability of a PSO is similar to other AI algorithms.
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