Neural Network Modeling and Backstepping Control for Quadrotor

Yu, Xiao; Lv, Zongyang; Wu, Yuhu; Sun, Xi‐Ming

doi:10.1109/cac.2018.8623432

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…12 Backpropagation neural network-based estimation merged with backstepping control design to address the wind disturbance like unmodelled factor again, but deviation of quadrotor has been observed at 33rd seconds in the simulation within finite time of simulation. 13 In the same contribution, memory constraints were also observed that does not accommodate the online data set from inertial measurement unit for further estimation.…”

Section: Hybrid Control Designs With Observer Design Methodsmentioning

confidence: 99%

Effects of unmodelled dynamic factors on an under-actuated quadrotor: A review of hybrid observer design methods

Abro

Asirvadam

Zulkifli

et al. 2020

Measurement and Control

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Unmodelled dynamic factors are either the left-over or the estimated factors such as lower or upper bound values while modelling any mechatronic system. Hence, with the inclusion of under-actuation in a system such as fewer number of actuators as compared to degrees of freedom, this will lead the system to high instability. These factors are changing instantly during multiple flights of quadrotor that is, the values of these factors in path following may vary from the values in hovering mode. Hence, it is one of the strenuous tasks to tackle these unmodelled dynamic factors for the multiple flight modes of an underactuated quadrotor craft. One of the better ways for tracking control of a quadrotor aerial vehicle with unmodelled dynamics is to observe and estimate the instant change in parameters. Thus, this paper exhibits an extensive review of several hybrid observer design methods being fused with some novel control strategies. In addition to this, the survey paper also summarises the limitations of the current state of the art approaches. This paper demonstrates an unexplored field of study where researchers must need to evaluate the performance of hybrid observer design methods.

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Section: Hybrid Control Designs With Observer Design Methodsmentioning

confidence: 99%

Effects of unmodelled dynamic factors on an under-actuated quadrotor: A review of hybrid observer design methods

Abro

Asirvadam

Zulkifli

et al. 2020

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…To achieve an even more accurate model, GTA-V allows researchers to easily create a large dataset for testing and training neural networks. In flight control, Yu et al [9] designed a controller for a quadrotor. To evaluate the effectiveness of their method, a virtual maze using the Airsim software platform was created.…”

Section: Overview Of Deep Reinforcement Learningmentioning

confidence: 99%

Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games

et al. 2019

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This study proposed a reinforcement Q-learning-based deep neural network (RQDNN) that combined a deep principal component analysis network (DPCANet) and Q-learning to determine a playing strategy for video games. Video game images were used as the inputs. The proposed DPCANet was used to initialize the parameters of the convolution kernel and capture the image features automatically. It performs as a deep neural network and requires less computational complexity than traditional convolution neural networks. A reinforcement Q-learning method was used to implement a strategy for playing the video game. Both Flappy Bird and Atari Breakout games were implemented to verify the proposed method in this study. Experimental results showed that the scores of our proposed RQDNN were better than those of human players and other methods. In addition, the training time of the proposed RQDNN was also far less than other methods.

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Section: Introductionmentioning

confidence: 99%

“…Most quadcopters apply the classical control method, so they are driven by proportional-integral-derivative-PID controllers [1][2][3][4][5][6][7][8][9][10]. PID controller method and its usability in different systems are well described in many research papers [1][2][3][4][5][6][7][8][9][10]. There are several other algorithms that control quadcopters, such as LQR, Backstepping, Sliding mode, Linearized Feedback, Fuzzy logic, Optimal, L1, H∞, and Adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Quadcopter Stabilization using Neural Network Model from Collected Data of PID Controller

Gotov

Tengis

Choimaa

et al. 2022

The journal invites submissions from across the world.

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There are a lot of methods for the stabilization of quadcopters and the newest are based on AI. A neural network is a simplified model that imitates the human brain's processes. In the research paper, we present a neural network control model for quadcopter stabilization. A single hidden layer network model was estimated to investigate the dynamics of the UAV. A control system with a classical PID controller was used to train the neural network model. This method is used for examining how the neural network imitates the stabilization of the quadcopter in real flight mode. The novelty of the work was to design of small size 3 layers NN model that runs in real-time in a quadcopter. The PID and machine learning controllers' operation results were compared to each other andshown in the experiment.

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Neural Network Modeling and Backstepping Control for Quadrotor

Cited by 10 publications

References 15 publications

Effects of unmodelled dynamic factors on an under-actuated quadrotor: A review of hybrid observer design methods

Effects of unmodelled dynamic factors on an under-actuated quadrotor: A review of hybrid observer design methods

Using a Reinforcement Q-Learning-Based Deep Neural Network for Playing Video Games

Quadcopter Stabilization using Neural Network Model from Collected Data of PID Controller

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