Quadrotor position and attitude control via backstepping approach

Matouk, Djihad; Gherouat, Oussama; Abdessemed, Foudil; Hassam, Abdelouahab

doi:10.1109/icmic.2016.7804228

Cited by 23 publications

(15 citation statements)

References 4 publications

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“…Kemampuan quadrotor yang memiliki fleksibilitas baik serta kemampuan manuver yang andal dalam pengoperasiannya meningkatkan popularitas quadrotor di kalangan penggemar, pemerhati militer, serta peneliti. Penggunaan quadrotor sering diimplementasikan pada banyak kalangan sebagai alat untuk misi pemetaan, pencarian, penyelamatan, dan sebagainya [1].…”

Section: Pendahuluanunclassified

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Rahani

Priyambodo

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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One type of unmanned aircraft that is often used today is quadrotor. This type of aircraft has the ability to take off vertically. This study implemented an altitude control system on the z-axis quadrotor. The control used is the full state method of Linear Quadratic Regulator (LQR) with artificial neural networks. The LQR full state feedback method used in this system is 12-states with each feedback constant K tuned to the neural network method. This study implements the artificial neural network method to change the feedback constant on the z-axis. Artificial neural network architecture used 12 input layers, 48 hidden layers, and 1 output layer. This study compares the value of the results of the simulation with the response value of the system implementation results applied to the quadrotor. Testing with full state LQR feedback using artificial neural networks improves the system response to ±0.77 seconds and improves steady state error values up to ±12 cm. Based on the results of these studies, this system can be implemented to control other systems. Intisari-Salah satu jenis pesawat tanpa awak yang sering digunakan saat ini adalah quadrotor. Pesawat jenis ini memiliki kemampuan untuk lepas landas secara vertikal. Makalah ini mengimplementasikan sistem kendali ketinggian pada sumbu z quadrotor. Kendali yang digunakan yaitu metode full state feedback Linear Quadratic Regulator (LQR) dengan jaringan saraf tiruan. Metode full state feedback LQR yang digunakan pada sistem ini adalah dua belas state dengan masing-masing konstanta feedback K ditala dengan metode jaringan saraf tiruan. Makalah ini mengimplementasikan metode jaringan saraf tiruan untuk mengubah konstanta feedback pada sumbu z. Arsitektur jaringan saraf tiruan yang digunakan yaitu dua belas input layer, 48 hidden layer, dan satu output layer. Makalah ini membandingkan nilai hasil simulasi dengan nilai respons sistem hasil implementasi yang diterapkan pada quadrotor. Pengujian dengan full state feedback LQR menggunakan jaringan saraf tiruan memperbaiki respons sistem hingga ±0,77 detik serta perbaikan nilai steady state error hingga ±12 cm. Berdasarkan hasil penelitian tersebut, sistem ini dapat diimplementasikan kendali pada sistem yang lain.

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Section: Pendahuluanunclassified

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Rahani

Priyambodo

2019

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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“…In [13], an UAV control system was augmented with the multilayer neural networks, and the weights of the neural networks was adjusted online according to an adaptive law. A backstepping approach was proposed for UAV with a nonlinear control strategy in [14], and the corresponding back stepping controller was divided into three subcontrollers, i.e., attitude controller, altitude controller, and position controller. A combining integral sliding mode and backstepping sliding mode controller in a double-loop control structure was proposed for UAV in [15].…”

Section: Introductionmentioning

confidence: 99%

Stochastic H_∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations

et al. 2021

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In this article, a robust decentralized tracking control scheme for a large-scale unmanned aerial vehicle (UAV) formation team networked control system (NCS) is proposed to overcome a non-scalable or even infeasible design problem due to high computational complexity by the centralized control. At the outset, wind external disturbance, intrinsic fluctuation, coupling from other UAV subsystems, timevarying network-induced delay and packet dropout are taken into account in the design procedure of large-scale team formation UAV network system. Moreover, an event-triggered mechanism is embedded in NCS to reduce the number of transmitted control signals to save the network traffic especially for a large-scale team formation network of UAVs. To effectively reduce the above uncertainties, a robust H ∞ decentralized tracking controller is proposed for the large-scale UAV team formation NCS which is constructed by virtual leader-follower tracking network of UAV subsystems. By the Takagi-Sugeno (T-S) fuzzy interpolation method, the H ∞ robust virtual leader-follower decentralized tracking control design problem of the large-scale UAV team formation NCS is transformed to an independent Hamilton-Jacobin inequality (HJI)-constrained optimization problem for each UAV, which is effectively solved by a linear matrix inequality (LMI)-constrained optimization problem. Finally, a large-scale UAV team formation example of 25 UAVs is proposed to validate the effectiveness of the proposed robust decentralized tracking controller of large-scale UAV team formation NCS.INDEX TERMS Unmanned aerial vehicle (UAV) networked control system (NCS), event-triggered control, stochastic control, virtual structure formation control, decentralized stochastic robust H ∞ fuzzy tracking control.

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“…In order to enlarge the basin of attraction, nonlinear control techniques have also been considered. Examples are sliding model control Luque-Vega et al [9], backstepping Bouabdallah and Siegwart [10], and adaptive control Matouk et al [11]. Moreover, a global fast dynamic terminal sliding mode control method was proposed for position and attitude tracking control in Xiong and Zhang [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hovering Control of Unmanned Aerial Vehicles

Lúa

García

Gennaro

et al. 2020

Mathematical Problems in Engineering

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In this paper, the design of a controller for the altitude and rotational dynamics is presented. In particular, the control problem is to maintain a desired altitude in a fixed position. The unmanned aerial vehicle dynamics are described by nonlinear equations, derived using the Newton–Euler approach. The control problem is solved imposing the stability of the error dynamics with respect to desired position and angular references. The performance and effectiveness of the proposed control are tested, first, via numerical simulations, using the Pixhawk Pilot Support Package simulator provided by Mathworks. Then, the controller is tested via a real-time implementation, using a quadrotor Aircraft F-450.

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Quadrotor position and attitude control via backstepping approach

Cited by 23 publications

References 4 publications

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Stochastic H_∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations

Real-Time Hovering Control of Unmanned Aerial Vehicles

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Quadrotor position and attitude control via backstepping approach

Cited by 23 publications

References 4 publications

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor

Stochastic H∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations

Real-Time Hovering Control of Unmanned Aerial Vehicles

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Stochastic H_∞ Robust Decentralized Tracking Control of Large-Scale Team Formation UAV Network System With Time-Varying Delay and Packet Dropout Under Interconnected Couplings and Wiener Fluctuations