SA-PSO算法在飞行器追逃中的应用

Application of SA-PSO algorithm in the pursuit-evasion problem of vehicles

针对飞行器追逃对抗问题,应用微分对策理论,提出了基于模拟退火-粒子群算法(SA-PSO)的非线性模型预测控制(NMPC)方法,得到追逃双方的近似反馈最优策略,避免了复杂的HJI方程求解。将模拟退火思想引入PSO算法,建立SA-PSO算法模型,通过自适应改变的惯性权重和学习因子提高PSO算法的全局寻优能力,将改进的SA-PSO作为优化技术用于预测过程,然后进行在线滚动优化。仿真结果表明了所提出方法的有效性,对初始条件和噪声具有较好的鲁棒性。

To solve the problem of aircraft pursuit-evasion, a novel approach for the implementation of nonlinear model predictive control （ NMPC ） is proposed using simulated annealing and particle swarm optimization based on the theory of differential game, optimal feedback strategies of both are obtained, avoi- ding the solving of complex HJI equations. The simulated annealing is introduced into the PSO algorithm, and then SA-PSO algorithm model is established, the global optimization ability of SA-PSO is improved by adaptively changing the inertia weight and learning factors. The improved SA-PSO is used as optimization techniques for predicting process, followed by online rolling optimization. Simulation results show the effectiveness of the proposed method, which has good robustness to initial conditions and noise.