带配平翼航天器再入轨迹优化与制导问题研究
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摘要
带配平翼航天器是一种中等升阻比飞行器，相比于载人飞船有较高的升阻比，利用翼片能进行机动，提高落点精度。目前我国航天事业正在稳步向前，在研究可重复使用单级入轨、两级入轨航天器前可以选择带配平翼的航天器来作为一个过渡，以此来验证升力式再入技术、热防护技术、控制技术等关键技术。
给出了航天器再入的动力学模型，对航天器进入轨道进行了描述；给出了航天器近地返回情况和月球返回舱高速返回情况下的弹道特性分析，对不同升阻比、不同再入角情况下的再入轨迹进行了仿真，通过对弹道特性的分析，提出可以采用中等升阻比的航天器来执行近地任务以及月球任务。
给出了带配平翼航天器模型，对带配平翼航天器进行了气动仿真，得到了两组不同攻角下的升力系数、阻力系数。对仿真数据进行了比较分析。
对带配平翼航天器的跳跃再入轨迹进行了优化。针对不同的目的，对过载约束、热流约束、动压约束下的再入轨迹分别设计。为了航天员安全，必须要降低航天器再入时所承受的过载；为了减轻飞行器结构重量以及配平翼承受的载荷，对航天器动压限制下的再入轨迹进行了优化；为了防止再入时热流过高而损毁航天器，对热流限制下的再入轨迹进行了优化。
基于神经网络动态优化方法对再入轨迹进行了优化。首先，介绍了神经网络的一些基本概念，给出了神经网络优化的原理，详细介绍了优化流程。极大值原理在求解轨迹优化问题时存在对初始状态敏感的问题，将神经网络和极大值原理结合起来解决了该问题。最后对航天器再入轨迹进行了优化，仿真表明采用神经网络动态优化方法是可行的，当初始误差不大时，训练好的神经网络能在10秒时间内设计出优化的轨迹。
讨论了基于变论域模糊-PI控制的纵向预测制导律设计。介绍了模糊控制的基本概念，引入了变论域模糊控制的概念。然后将变论域模糊控制和PI控制结合起来设计了一种新的制导律。由于变论域模糊控制采用自适应变论域的方法，大大减少了模糊控制规则的复杂度。仿真结果表明本文设计的制导律对初始状态误差具有强鲁棒性，能将航程误差控制在30米范围之内。
The vehicle with trim flaps is a medium lift-to-drag ratio spacecraft, compared to current mannedspacecraft it has higher lift-to-drag ratio. The flaps can be used to provide enough aerodynamicmaneuverability landing the vehicle in a predetermined place. At present, the aerospace industry inour country is steadily forward, we can choose a vehicle with medium lift-to-drag ratio as anintermediate vehicle to test advanced lifting reentry technology, thermal protection and controltechnology before we start researching single-stage-to-orbit vehicles and two-stage-to-orbit vehicles.
The equations of motion for reentry vehicle are presented, and the entry trajectory is described. Thetrajectory characteristics of the vehicle returning from the near-earth orbit and moon are analyzed.Simulation results show that vehicles with medium lift-to-drag ratio can be used for the near-earthmission and lunar mission.
The configuration of the vehicle with trim flaps is presented, and flow fields of the vehicle isanalyzed with CFD method, the aerodynamic coefficient is calculated. Two groups of lift coefficientand drag coefficient data are obtained. The simulation data were compared and analyzed. Thelift-to-drag ratio of vehicle with trim flaps is about0.8.
The trajectories under different constrains are optimized by applying Pontryagin maximumprinciple on the performance of minimum heat. For the safety of the astronauts, overload constraint istaken under consideration. In order to reduce the dynamic pressure on the trim flaps, dynamicpressure constraint is taken under consideration. Heat rate constraint is taken under consideration toprotect the vehicle from being burned.
Reentry trajectory is optimized based on Neural Dynamic Optimization Method. Neural network isintroduced. The theory of NDO is presented and the implementation issues are discussed. Themaximum principle is sensitive to initial values of the state variable.The way combining neuralnetwork method and the maximum principle can solve the problem.The simulation results show thefeasibility of the NDO method. A properly trained NDO can generate feasible and acceptabletrajectory in10seconds when the initial error is not large.
A longitudinal predictive reentry guidance law based on the variable universe fuzzy-PI control isdiscussed. Fuzzy control theory is introduced and variable universe fuzzy control is presented. Thecomplexity of the fuzzy control rules is greatly reduced by using variable universe fuzzy controlmethod, simulation results show that the proposed guidance law is very robust to the initial errors and can control the downrange error within30meters.
给出了航天器再入的动力学模型，对航天器进入轨道进行了描述；给出了航天器近地返回情况和月球返回舱高速返回情况下的弹道特性分析，对不同升阻比、不同再入角情况下的再入轨迹进行了仿真，通过对弹道特性的分析，提出可以采用中等升阻比的航天器来执行近地任务以及月球任务。
给出了带配平翼航天器模型，对带配平翼航天器进行了气动仿真，得到了两组不同攻角下的升力系数、阻力系数。对仿真数据进行了比较分析。
对带配平翼航天器的跳跃再入轨迹进行了优化。针对不同的目的，对过载约束、热流约束、动压约束下的再入轨迹分别设计。为了航天员安全，必须要降低航天器再入时所承受的过载；为了减轻飞行器结构重量以及配平翼承受的载荷，对航天器动压限制下的再入轨迹进行了优化；为了防止再入时热流过高而损毁航天器，对热流限制下的再入轨迹进行了优化。
基于神经网络动态优化方法对再入轨迹进行了优化。首先，介绍了神经网络的一些基本概念，给出了神经网络优化的原理，详细介绍了优化流程。极大值原理在求解轨迹优化问题时存在对初始状态敏感的问题，将神经网络和极大值原理结合起来解决了该问题。最后对航天器再入轨迹进行了优化，仿真表明采用神经网络动态优化方法是可行的，当初始误差不大时，训练好的神经网络能在10秒时间内设计出优化的轨迹。
讨论了基于变论域模糊-PI控制的纵向预测制导律设计。介绍了模糊控制的基本概念，引入了变论域模糊控制的概念。然后将变论域模糊控制和PI控制结合起来设计了一种新的制导律。由于变论域模糊控制采用自适应变论域的方法，大大减少了模糊控制规则的复杂度。仿真结果表明本文设计的制导律对初始状态误差具有强鲁棒性，能将航程误差控制在30米范围之内。
The vehicle with trim flaps is a medium lift-to-drag ratio spacecraft, compared to current mannedspacecraft it has higher lift-to-drag ratio. The flaps can be used to provide enough aerodynamicmaneuverability landing the vehicle in a predetermined place. At present, the aerospace industry inour country is steadily forward, we can choose a vehicle with medium lift-to-drag ratio as anintermediate vehicle to test advanced lifting reentry technology, thermal protection and controltechnology before we start researching single-stage-to-orbit vehicles and two-stage-to-orbit vehicles.
The equations of motion for reentry vehicle are presented, and the entry trajectory is described. Thetrajectory characteristics of the vehicle returning from the near-earth orbit and moon are analyzed.Simulation results show that vehicles with medium lift-to-drag ratio can be used for the near-earthmission and lunar mission.
The configuration of the vehicle with trim flaps is presented, and flow fields of the vehicle isanalyzed with CFD method, the aerodynamic coefficient is calculated. Two groups of lift coefficientand drag coefficient data are obtained. The simulation data were compared and analyzed. Thelift-to-drag ratio of vehicle with trim flaps is about0.8.
The trajectories under different constrains are optimized by applying Pontryagin maximumprinciple on the performance of minimum heat. For the safety of the astronauts, overload constraint istaken under consideration. In order to reduce the dynamic pressure on the trim flaps, dynamicpressure constraint is taken under consideration. Heat rate constraint is taken under consideration toprotect the vehicle from being burned.
Reentry trajectory is optimized based on Neural Dynamic Optimization Method. Neural network isintroduced. The theory of NDO is presented and the implementation issues are discussed. Themaximum principle is sensitive to initial values of the state variable.The way combining neuralnetwork method and the maximum principle can solve the problem.The simulation results show thefeasibility of the NDO method. A properly trained NDO can generate feasible and acceptabletrajectory in10seconds when the initial error is not large.
A longitudinal predictive reentry guidance law based on the variable universe fuzzy-PI control isdiscussed. Fuzzy control theory is introduced and variable universe fuzzy control is presented. Thecomplexity of the fuzzy control rules is greatly reduced by using variable universe fuzzy controlmethod, simulation results show that the proposed guidance law is very robust to the initial errors and can control the downrange error within30meters.
引文
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