Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

Nguyen, Ngoc Phi; Xuan-Mung, Nguyen; Thanh, Ha Le Nhu Ngoc; Huynh, Tuan Tu; Lam, Ngoc Tam; Hong, Sung Kyung

doi:10.1109/access.2021.3064883

Cited by 58 publications

(28 citation statements)

References 35 publications

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“…Nguyen et al proposed a fault-tolerant control method based on adaptive, nonsingular fast terminal sliding mode control and neural network approximation for four-axis aircraft. In addition, an external disturbance sliding mode control method was proposed for the altitude attitude system [6]. Yang et al [7] used interval data to reduce the dimension of flight data and realized real-time prediction of UAV faults through a BP neural network.…”

Section: Literature Review 21 Data Driven Fault Analysis Methodsmentioning

confidence: 99%

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

et al. 2021

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At present, the research on fault analysis based on text data focuses on fault diagnosis and classification, but it rarely suggests how to use that information to troubleshoot faults reported in unmanned aerial vehicles (UAVs). Selecting the exact troubleshooting procedure to address faults reported by UAVs generally requires experienced technicians with professional equipment. To improve the efficiency of UAV troubleshooting, this paper proposed a troubleshooting mode selection method based on SIF-SVM (Serial information fusion and support vector machine) using the text feature data from fault description records. First, Word2Vec was used in text data feature extraction. Second, in order to increase the amount of information in the modeling data, we used the information fusion method. SVM was then used to construct the classification model for troubleshooting mode selection. Finally, the effectiveness of the proposed model was verified by using the fault record data of a new fixed-wing UAV.

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Section: Literature Review 21 Data Driven Fault Analysis Methodsmentioning

confidence: 99%

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

et al. 2021

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“…Also, they used a novel time-varying sliding mode surface to eliminate the reaching phase, reduce the initial control effort, and meet the impact time requirement with a global sliding mode. In [32], sliding mode control was proposed based on the neural networks to stabilize the position and altitude of the quadcopter under external perturbations.…”

Section: Sliding Mode Algorithmmentioning

confidence: 99%

Attitude and Altitude Nonlinear Control Regulation of a Quadcopter Using Quaternion Representation

et al. 2022

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Controlling a quadcopter is a challenging task because of the inherent high nonlinearity of a quadcopter system. In this paper, a new quaternion-based nonlinear feedback controller for attitude and altitude regulation of a quadcopter is proposed. The dynamic model of the quadcopter is derived using Newton and Euler equations. The proposed controller is established based on a feedback linearization technique to control and regulate the quadcopter. Global asymptotic stability of the designed controller is verified using Lyapunov stability criterion. A comparison between the proposed controller and two states of the art quadcopter controller is performed to evaluate its efficiency and performance. The efficiency of the proposed controller is clearly shown when the quadcopter is in or near a critical position. Simulations are performed to assess the transient and steady state performance. Steady state error and Max error indicators demonstrate the validity and effectiveness of the proposed algorithm with different reference controllers.

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“…In Ref. [30], the fuzzy algorithm has been used to tackle parametric uncertainties and external disturbances. The reference [12] presents an adaptive SMC-fuzzy algorithm for trajectory tracking of the quadcopter.…”

Section: Introductionmentioning

confidence: 99%

Performance Examinations of Quadrotor with Sliding Mode Control-Neural Network on Various Trajectory and Conditions

Darwito¹,

Wahyuadnyana²

2022

MMEP

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In this article, the performance of the sliding mode control (SMC) that is combined with the backpropagation neural network (NN) as the main control of quadrotor’s dynamic systems was examined on various trajectories and conditions, through numerical simulation. The simulation is conducted with three different trajectories in the absence and presence of the time-varying external disturbances that were adopted from previous studies. The time-varying external disturbances are implemented for the roll, pitch, yaw, and altitude movement simultaneously with the gain set up at the value of 0.8. The simulation results show that the SMC-NN scheme was able to control the quadrotor either in the absence or in the presence of time-varying external disturbances, for each trajectory without any chattering or vibration issues in the quadrotor’s dynamic system. It can be concluded that the SMC-NN is one of the control strategies that are appropriate for the mission with various conditions and circumstances.

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Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network

Cited by 58 publications

References 35 publications

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

An Unmanned Aerial Vehicle Troubleshooting Mode Selection Method Based on SIF-SVM with Fault Phenomena Text Record

Attitude and Altitude Nonlinear Control Regulation of a Quadcopter Using Quaternion Representation

Performance Examinations of Quadrotor with Sliding Mode Control-Neural Network on Various Trajectory and Conditions

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