OGY方法的轨道引导改进

Improvement of OGY method in rail guiding

为减少OGY方法前期等待时间,结合自适应混沌粒子群算法(ACPOS)设计一种新的OGY控制器(ACPOS-OGY).用ACPOS对混沌系统轨道做初始引导,将其引导到不稳定不动点的邻域内,然后再对系统参数进行微调,最终使系统轨道达到稳定的轨道上.ACPOS-OGY不仅吸取粒子群算法和OGY方法的优点,而且自适应调整策略和混沌化处理粒子群算法过程也避免了粒子群算法的缺点,使前期引导轨道更快更准.仿真实验结果表明:改进算法是有效的,并且克服了PSO引导中的人为因素的影响.

In order to reduce the waiting time of OGY method in the prior period, a new OGY controller（ACPOS-OGY） was designed, which was combined with the adaptive chaotic particle swarm optimization（ACPOS）. Chaotic system track was initially guided to a small neighborhood of the unstable fixed point by ACPOS, then system parameters were adjusted slightly, and final system track achieved a stable orbit. ACPOS-OGY not only absorbed the advantages of PSO algorithm and OGY method, but also avoided the shortcomings of the PSO through adaptive adjustment strategy and treating PSO process by chaos, thus making prophase guide rail was quicker, and more accurate. The simulation results show that the improved algorithm is effective, and overcomes human factors influence of PSO guiding.