Robust neural network‐based backstepping landing control of quadrotor on moving platform with stochastic noise

2021 ◽  
Author(s):  
Navid Vafamand ◽  
Mohammad Mehdi Arefi
Keyword(s):  
Neural Network ◽  
Stochastic Noise ◽  
Moving Platform ◽  
Landing Control

Robust Image-based Landing Control of a Quadrotor on an Unknown Moving Platform Using Circle Features

2021 ◽  
Author(s):  
Jie Lin ◽  
Yaonan Wang ◽  
Zhiqiang Miao ◽  
Hang Zhong ◽  
Jingmou Nie ◽  
...  
Keyword(s):  
Robust Image ◽  
Moving Platform ◽  
Landing Control

scholarly journals Neural Network Analysis of Pilot Landing Control in Real Flight

2008 ◽  
Vol 1 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Ryota MORI ◽  
Shinji SUZUKI ◽  
Kazuya MASUI ◽  
Hiroshi TOMITA
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Neural Network Analysis ◽  
Landing Control

Analysis of Pilot Landing Control Using Neural Network

2006 ◽  
Author(s):  
Yuki Sakamoto ◽  
Ryota Mori ◽  
Shinji Suzuki ◽  
Hiroshi Takahara
Keyword(s):  
Neural Network ◽  
Landing Control

scholarly journals Energy Consumption Prediction for 3-RRR PPM through Combining LSTM Neural Network with Whale Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yin Gao ◽  
Ke Chen ◽  
Hong Gao ◽  
Hongmei Zheng ◽  
Lei Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Loss Function ◽  
Optimization Algorithm ◽  
Movement Time ◽  
Training Data ◽  
Whale Optimization ◽  
Moving Platform ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

In the process of minimizing the energy consumption of a 3-RRR planar parallel manipulator (3-RRR PPM) and even general parallel kinematic manipulators, obtaining optimal results usually depends on particular functional relation between the instantaneous position of the moving platform and the kinetic time, which is called a displacement model (DM). Nevertheless, it is likely that although the movement time and path of a moving platform are the same, different amounts of energy are consumed for different DMs of the moving platform. To address this, a method of using long short-term memory neural network (LSTM-NN) instead of a complex theoretical model to predict the energy consumption of a 3-RRR PPM was presented. Subsequently, inverse dynamic equations of 3-RRR PPM were established based on the Newton–Euler method and solved using QR decomposition. Meanwhile, energy consumption between any two points in workspace of the 3-RRR PPM was programmed to provide the LSTM-NN with abundant precise training data. In view of time-varying characteristics of energy consumption prediction, the network architecture was developed based on the principle of LSTM-NN, and root-mean-square error (RMSE) was taken as the loss function. After acquiring training data, the RMSE of the LSTM-NN reached 0.00041 using whale optimization algorithm (WOA) with no need for the gradient of the loss function, so the lack of solving precision in training LSTM-NN was effectively improved. Finally, two different DMs of a moving platform with the same path and movement time were chosen to compare the total energy consumption of the 3-RRR PPM from the simulations, predictions, and experiments. The results showed that the relative error between predicted and experimental data was less than 2.50%. Therefore, the energy consumption prediction based on the LSTM-NN will be useful for achieving the intelligent application of 3-RRR PPMs.

Application of generalised neural network for aircraft landing control system

2002 ◽  
Vol 6 (6) ◽  
pp. 441-448 ◽  
Author(s):  
D. K. Chaturvedi ◽  
R. Chauhan ◽  
P. K. Kalra
Keyword(s):  
Neural Network ◽  
Control System ◽  
Aircraft Landing ◽  
Landing Control

3802 Analysis of Pilot Landing Control using Neural Network by Video Data

2006 ◽  
Vol 2006.5 (0) ◽  
pp. 469-470
Author(s):  
Ryota MORI ◽  
Masaru NARUOKA ◽  
Takeshi TSUCHIYA ◽  
Shinji SUZUKI
Keyword(s):  
Neural Network ◽  
Video Data ◽  
Landing Control
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