Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

Yaohua, Liu; Zhou, Yimin; Li, Xiang

doi:10.1109/ijcnn.2018.8489118

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…Deep learning architectures such as a convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) have already been applied for UAV applications for example, attitude estimation, fault detection and target detection etc. [15][16][17]. The DL algorithms use multi-layered neural network architecture for mining the features in datasets from their lowest level to the highest level of training the data.…”

Section: Related Workmentioning

confidence: 99%

An intelligent age of information based self‐energized UAV‐assisted wireless communication system

Jeganathan,

Dhayabaran,

Jayakody

et al. 2023

IET Communications

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Internet‐of‐things is an enabling technology in the fourth‐generation industrial revolution. The freshness of the data sent by a sensor node (SN) is an important parameter in the Internet‐of‐things, unlike the throughput in cellular communications. A relatively new performance metric named age of information (AoI) is used in this paper to quantify the freshness of the data. The SN, located in the transport infrastructure, harvests energy from radio frequency signals transmitted by the FD‐UAV. This is used to transmit real‐time sensor observations to the data sink via FD‐UAV. The SN generates an update after replenishing the battery and transmits it by using the harvested energy. A closed‐form expression for average AoI is derived as a function of time allocated for energy harvesting. The optimal time allocation for energy harvesting that maximizes the freshness of data update is identified. A deep learning technique namely long‐short term memory is used to predict the average AoI. Simulation results demonstrate the usefulness of the performance bounds in terms of the freshness of data updates.

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Section: Related Workmentioning

confidence: 99%

An intelligent age of information based self‐energized UAV‐assisted wireless communication system

Jeganathan,

Dhayabaran,

Jayakody

et al. 2023

IET Communications

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“…In [9], [10], [11], [12] authors worked on feedback linearization based neural network for dynamical systems. The estimation of attitude using ANN is also studied, in [13], [14] authors proposed a long and short term memory neural network (LSTMNN) and a Modified Elman Recurrent Neural Network (MERNN) respectively used to control attitude and altitude of UAVs.…”

Section: Cruise To Hovermentioning

confidence: 99%

Transition control of a tail-sitter UAV using recurrent neural networks

Flores

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents the implementation of a Recurrent Neural Network (RNN) based-controller for the stabilization of the flight transition maneuver (hover-cruise and vice versa) of a tail-sitter UAV. The control strategy is based on attitude and velocity stabilization. For that aim, the RNN is used for the estimation of high nonlinear aerodynamic terms during the transition stage. Then, this estimate is used together with a feedback linearization technique for stabilizing the entire system. Results show convergence of linear velocities and the pitch angle during the transition maneuver. To analyze the performance of our proposed control strategy, we present simulations for the transition from hover to cruise and vice versa.

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“…In [6], a neural network was employed to approximate plant dynamics and external disturbances for use in combining active disturbance attenuation (ADA) with robust dynamic programming (RADP). Recurrent neural networks were used in [7] in the form of long short term memory neural networks (LSTM-NN) for an attitude estimation application to unmanned aerial vehicles (UAV)'s by training the network on attitude time-series data.…”

Section: Introductionmentioning

confidence: 99%

Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control

Lutes,

Krishnamurthy,

Nadendla

et al. 2021

Preprint

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Adaptive methods are popular within the control literature due to the flexibility and forgiveness they offer in the area of modelling. Neural network adaptive control is favorable specifically for the powerful nature of the machine learning algorithm to approximate unknown functions and for the ability to relax certain constraints within traditional adaptive control. Deep neural networks are large framework networks with vastly superior approximation characteristics than their shallow counterparts. However, implementing a deep neural network can be difficult due to size specific complications such as vanishing/exploding gradients in training. In this paper, a neuro-adaptive controller is implemented featuring a deep neural network trained on a new weight update law that escapes the vanishing/exploding gradient problem by only incorporating the sign of the gradient. The type of controller designed is an adaptive dynamic inversion controller utilizing a modified state observer in a secondary estimation loop to train the network. The deep neural network learns the entire plant model on-line, creating a controller that is completely model free. The controller design is tested in simulation on a 2 link planar robot arm. The controller is able to learn the nonlinear plant quickly and displays good performance in the tracking control problem.

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Attitude Estimation of Unmanned Aerial Vehicle Based on LSTM Neural Network

Cited by 14 publications

References 24 publications

An intelligent age of information based self‐energized UAV‐assisted wireless communication system

An intelligent age of information based self‐energized UAV‐assisted wireless communication system

Transition control of a tail-sitter UAV using recurrent neural networks

Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control

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