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1. 沈阳航空航天大学航空航天工程学部, 沈阳 110136;
2. 北京航空航天大学航空科学与工程学院, 北京 100083

Design of control law for ejection seat under adverse attitudes
MAO Xiaodong1 , LIN Guiping2, YU Jia2
1. College of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China;
2. School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
Received: 2015-03-31; Accepted: 2015-06-26; Published online: 2015-08-03 17:20
Corresponding author: Tel.:024-89738720 E-mail:mxdbh@163.com
Abstract: The escape performance of ejection seat under adverse attitudes is the key technology for the 4th generation ejection seat, and the design of control law algorithm is the core problem for attitude and trajectory adjustment. A new control law design method was presented. Firstly, a simulation model for the entire ejecting process was established and a control parameter optimization model was designed, through which an optimum parameter set was obtained as the discrete control law. Then, by utilizing multi-layer feedback of the error back propagation (BP) algorithm based neural network model, the ultimate continuous control law can be acquired under the whole ejecting conditions. The roll attitude ejecting condition was exampled to design and validate the approached method. The results indicate that the performance of ejection seat by adopting the control law designed by the proposed method is higher than the multi-mode control law and the K36Л-3.5 ejection seat, which also satisfies the performance demand of GJB under most conditions. Consequently, the design method is simple and clear, and the final algorithm is close to the theoretical optimum result. Therefore, it is proved to be a useful method for the design of the 4th generation ejection seat control law.
Key words: ejection seat     adverse attitude     numerical simulation     escape performance     control law     neural network

1 设计方案

 图 1 控制规律设计方案 Fig. 1 Design program for control law

2 弹射姿态轨迹计算 2.1 数学模型

2.2 数值仿真模型

 图 2 仿真模型示意图 Fig. 2 Simulation model chart
2.3 模型验证

 图 3 0 km·h-1弹射仿真与试验结果对比 Fig. 3 Comparison of simulation and experimental results at 0 km·h-1 ejection

3 控制规律设计 3.1 控制方案

 图 4 横滚姿态调整火箭安装 Fig. 4 Installation of roll adjusting rockets

3.2 优化计算模型

 图 5 优化算法计算流程 Fig. 5 Flowchart of optimization algorithm
3.3 神经网络模型

 图 6 神经元网络结构 Fig. 6 Structure of neural network

 图 7 神经元网络处理流程 Fig. 7 Processing flowchart of neural networks

4 结果分析 4.1 与多模态控制规律对比

 图 8 0 km·h-1弹射不同控制规律下轨迹高度对比 Fig. 8 Comparison of ejection height under different control law at 0 km·h-1
 图 9 400 km·h-1弹射不同控制规律下轨迹高度对比 Fig. 9 Comparison of ejection height under different control law at 400 km·h-1

4.2 与K36Л-3.5性能对比

 弹射速度/(km·h-1) 滚转角0° 滚转角60° 滚转角120° 滚转角180° MLHK/m MLHa/m MLHK/m MLHa/m MLHK/m MLHa/m MLHK/m MLHa/m 150 0 0 0 0 37 38.14 46 53.14 250 0 0 0 0 29 25.76 36 35.08 400 0 0 9 0 38 14.55 62 21.62 600 0 0 35 0 62 16.39 66 20.82 注：MLH—最低安全救生高度,即救生伞能够达到张满状态飞机应具有的最低飞行高度;带上标K的为K36Л-3.5型座椅数据;带上标a的为采用本文控制规律得到的数据。

4.3 与国军标要求对比

 弹射速度/(km·h-1) 滚转角45° 滚转角90° 滚转角180° MLHG/m MLHa/m MLHb/m MLHG/m MLHa/m MLHb/m MLHG/m MLHa/m MLHb/m 0 0 0 0 6 7.22 53.67 52 83.4 123.77 250 0 0 0 3 0 20.13 37 35.08 85.11 450 0 0 0 3 0 9.79 27 18.87 62.01 1 100 0 0 0 12 0 32.22 43 42.87 92.7 注：带上标G的为国军标对应参数；带上标b的为多模态控制规律计算结果。

5 结 论

1) 本文提出的弹射座椅控制规律设计方法切实可行，设计流程简单明确,结果算法便于实现。

2) 利用BP神经网络模型可以很好地处理控制规律算法中的连续非线性映射问题和模式识别问题,最终结果非常逼近理论最优值,且具有一定的容错性。

3) 以单滚转不利姿态弹射为例,进行了控制规律的设计验证。通过与多模态控制结果、K36Л-3.5型座椅性能参数以及国军标对最低安全救生高度的要求对比可知,本文得到的控制规律算法可以达到非常优异的控制效果。

4) 需要进一步研究存在俯冲、下沉等其他不利姿态时的控制方案及控制规律,同时对救生伞开伞时间展开优化计算。

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#### 文章信息

MAO Xiaodong, LIN Guiping, YU Jia

Design of control law for ejection seat under adverse attitudes

Journal of Beijing University of Aeronautics and Astronsutics, 2016, 42(3): 426-434.
http://dx.doi.org/10.13700/j.bh.1001-5965.2015.0188