...
首页> 外文期刊>Aerospace science and technology >Reentry guidance with constrained impact for hypersonic weapon by novel particle swarm optimization
【24h】

Reentry guidance with constrained impact for hypersonic weapon by novel particle swarm optimization

机译:基于新型粒子群算法的高超音速武器约束撞击再入制导

获取原文
获取原文并翻译 | 示例
           

摘要

The novel reentry guidance law is proposed for hypersonic weapons to strike a stationary ground target at a constrained impact angle. This guidance law is based on proportional navigation guidance (PNG) whose gain is set as the function of range-to-go. To be resistant to disturbances and adaptive to various missions, the gain is refreshed in every guidance cycle. To lessen the control effort, the gain is optimized by a novel particle swarm optimization (NPSO) algorithm. NPSO needs a small number of particles for it can convert infeasible particles into feasible ones. Moreover, a mutation mechanism is introduced to accelerate convergence. In addition, there is only one single terminal constraint, that is, the impact angle in the optimization problem, because the terminal position is automatically identified using PNG. Compared with existing guidance laws, the proposed one needs neither complex derivation nor prior assumption. It also takes into consideration the constraints in lateral acceleration and look angle, which are often neglected in PNG-based laws. The adaptability under different scenarios, the robustness under disturbances and the potential for online application are demonstrated by simulation results. Numerical examples also show the superiority of NPSO when compared with the GPOPS. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译:对于高超音速武器,提出了一种新颖的折返制导律,以限制的冲击角撞击固定的地面目标。该制导律基于比例导航制导(PNG),其增益被设置为可移动范围的函数。为了抵抗干扰并适应各种任务,在每个制导周期中都会刷新增益。为了减少控制工作,通过新型粒子群优化(NPSO)算法对增益进行了优化。 NPSO需要少量颗粒,因为它可以将不可行的颗粒转化为可行的颗粒。此外,引入了一种突变机制以加速收敛。另外,由于使用PNG自动识别了终端位置,因此只有一个终端约束,即优化问题中的碰撞角度。与现有的指导法相比,拟议的法律既不需要复杂的推导,也不需要事先假设。它还考虑了在基于PNG的法则中经常忽略的横向加速度和视角限制。仿真结果表明了在不同情况下的适应性,在干扰下的鲁棒性以及在线应用的潜力。数值算例也显示了NPSO与GPOPS相比的优越性。 (C)2018 Elsevier Masson SAS。版权所有。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号