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Variable Memory Recurrent Neural Networks for Nano Sat Launch Vehicle attitude control.

机译：用于纳米卫星运载火箭姿态控制的可变记忆递归神经网络。

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Launch Vehicles are governed by a complex set of nonlinear and highly coupled differential equations. In general, these equations are linearized about an equilibrium point and a linear controller is designed based upon these linearized dynamics. The linear controller is then applied to the actual system which is, of course, the original nonlinear system. This, in turn, leads to tracking errors and poor performance when the vehicle experiences significant deviation from the equilibrium condition. Also, the linear controller has difficulty handling unknown variations in the system parameters which also give rise to poor performance when applied to the actual vehicle.;A new type of neural network known as a Variable Memory Recurrent Neural Network (VMRNN) has been designed that, when used in conjunction with a linear PID controller, offers improved transient response characteristics when encountering uncertainty in the dynamic model and external disturbances.

机译：运载火箭由一组复杂的非线性和高度耦合的微分方程控制。通常，这些方程围绕平衡点线性化，并且基于这些线性化动力学设计线性控制器。然后将线性控制器应用于实际系统，该系统当然是原始非线性系统。当车辆经历明显偏离平衡条件的情况时，这又导致跟踪误差和较差的性能。此外，线性控制器很难处理系统参数中未知的变化，这在应用于实际车辆时也会导致性能下降。;一种新型的神经网络，称为可变记忆递归神经网络（VMRNN）被设计为与线性PID控制器结合使用时，遇到动态模型的不确定性和外部干扰时，可提供改进的瞬态响应特性。

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作者
Sclafani, Rodolfo J.;
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作者单位

California State University, Long Beach.;

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授予单位 California State University, Long Beach.;
学科 Aerospace engineering.
学位 M.S.
年度 2014
页码 79 p.
总页数 79
原文格式 PDF
正文语种 eng
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1. Design of Neural Network Variable Structure Reentry Control System for Reusable Launch Vehicle [J] . HU Wei-jun, ZHOU Jun 兵工学报（英文版） . 2008,第003期

机译：可重复使用运载火箭的神经网络变结构再入控制系统设计
2. Memory Augmented Neural Network-Based Intelligent Adaptive Fault Tolerant Control for a Class of Launch Vehicles Using Second-Order Disturbance Observer [J] . Haipeng Chen, Kang Chen, Wenxing Fu Mathematical Problems in Engineering: Theory, Methods and Applications . 2021,第a期

机译：使用二阶干扰观察者的一类发动车辆的内存增强基于神经网络的智能自适应容错控制
3. Ego-Vehicle Speed Prediction Using a Long Short-Term Memory Based Recurrent Neural Network [J] . Yeon Kyuhwan, Min Kyunghan, Shin Jaewook, International Journal of Automotive Technology . 2019,第4期

机译：基于长期内存的经常性神经网络的自我车辆速度预测
4. Variable Memory Recurrent Neural Networks For Launch Vehicle Attitude Control [C] . Rodolfo Sclafani, Praveen Shankar AIAA guidance, navigation, and control conference;AIAA SciTech forum . 2015

机译：用于运载火箭姿态控制的可变记忆递归神经网络
5. Recurrent neural networks applied to robotic motion control. [D] . Chen, Wen. 2002

机译：递归神经网络应用于机器人运动控制。
6. DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads [O] . Vladimír Boža, Broňa Brejová, Tomáš Vinař -1

机译：DeepNano：用于MinION纳米孔读取的碱基调用的深度递归神经网络
7. Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection [O] . Umit Cali, Vinayak Sharma 2019

机译：基于长短期内存的经常性神经网络模型和可变选择的短期风力预测

Variable Memory Recurrent Neural Networks for Nano Sat Launch Vehicle attitude control.

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