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首页> 外文期刊>Aerospace science and technology >A hybrid particle swarm optimization-gauss pseudo method for reentry trajectory optimization of hypersonic vehicle with navigation information model
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A hybrid particle swarm optimization-gauss pseudo method for reentry trajectory optimization of hypersonic vehicle with navigation information model

机译：具有导航信息模型的杂交粒子群优化 - 高音轨道轨迹优化的高斯伪方法

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摘要

Reentry trajectory optimization of hypersonic vehicle has been a hotspot in recent years, and the existing methods suffer from two drawbacks. First, the navigation error caused by the blackout zone is not considered in the reentry trajectory optimization model. Second, a single approach is usually applied to optimize the reentry trajectory, which fails to cover the shortage of it by combining with other approaches. To this end, a hybrid particle swarm optimization (PSO)-gauss pseudo method (GPM) algorithm, namely the hybrid PSO-GPM algorithm, is proposed to deal with the reentry trajectory optimization problem in this paper. The navigation information model reflecting the influence of the blackout zone on the global positioning system (GPS)/inertial navigation system (INS) is established first. In this model, the states of hypersonic vehicle are represented by random values obeying the normal distribution rather than the determined values, and the standard deviation is calculated from the error principle of INS. In the hybrid PSO-GPM algorithm, GPM works in the inner loop to solve the reentry trajectory optimization problem with a fast convergence and high precision under a provided initial guess. PSO plays a role in the outer loop to optimize the initial guess for GPM. Simulation results demonstrate that the established navigation information-based reentry trajectory optimization model is rational and can improve the safety level of flight. With the hybrid PSO-GPM algorithm, a better solution can be generated compared to the results when PSO algorithm and GPM are used separately. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译：近年来高超声速车辆的再入轨迹优化一直是热点，现有方法遭受两个缺点。首先，在再入轨迹优化模型中不考虑由遮光区域引起的导航误差。其次，通常应用单一方法以优化再入轨迹，这不能通过与其他方法组合来涵盖其短缺。为此，提出了一种混合粒子群优化（PSO）-Gauss伪方法（GPM）算法，即混合PSO-GPM算法，以处理本文的再入轨迹优化问题。首先建立反映在全球定位系统（GPS）/惯性导航系统（INS）上的遮光区影响的导航信息模型。在该模型中，超声车辆的状态由遵循正常分布而不是所确定的值的随机值表示，并且标准偏差由INS的误差原理计算。在混合PSO-GPM算法中，GPM在内循环中工作，以解决在提供的初始猜测中具有快速收敛和高精度的再入轨迹优化问题。 PSO在外循环中发挥作用，以优化GPM的初始猜测。仿真结果表明，已建立的导航信息的再入轨迹优化模型是合理的，可以提高飞行安全水平。利用混合PSO-GPM算法，与单独使用PSO算法和GPM时，可以将更好的解决方案生成更好的解决方案。 （c）2021 Elsevier Masson SAS。版权所有。

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