Intelligent fuzzy controller of a quadrotor

Santos, Matilde; López, Victoria; Morata, Franciso

doi:10.1109/iske.2010.5680812

Cited by 161 publications

(75 citation statements)

References 11 publications

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“…Logika ini menggunakan pendekatan alamiah layaknya manusia dalam menentukan suatu keputusan [7]. Logika fuzzy dapat digunakan untuk mengoptimalkan respon dari sistem kendali PID dengan merujuk pada karakteristik konstanta Kp, Ki, dan Kd [8].…”

Section: Analisis Sistemunclassified

Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy

Kusuma¹,

Dharmawan

2017

IJEIS

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Section: Analisis Sistemunclassified

Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy

Kusuma¹,

Dharmawan

2017

IJEIS

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“…The result of the experiment showed that the fuzzy can be used to control a motor on quadrotor. The other researchers named Santos et al [8] have applied fuzzy logic control with mamdani method to control the x, y, and z positions in quadrotor. This algorithm is used and modified for membership output members with 5 members to control the positions of x, y, and z in quadrotor in an undisturbanced environment.…”

Section: Hovering Control Of Quadrotor Based On Fuzzy Logic (Nia Mahamentioning

confidence: 99%

Hovering Control of Quadrotor Based on Fuzzy Logic

Raharja¹,

Firmansyah²,

Cahyadi³

et al. 2017

IJPEDS

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Quadrotor is one of rotary wing UAV types which is able to perform a hover position. In order to take off, landing, and hover, it needs controllers. Conventional controllers have been widely applied in quadrotor, yet they have drawbacks namely overshoot. This paper presents attitude and altitude control algorithm in order to obtain a response as quadrotor hovered optimally within minimum overshoot, rise time, and settling time. The algorithm used is Fuzzy Logic Controller (FLC) algorithm with Mamdani method. By using the algorithm, the quadrotor is able to hover with minimum overshoot and maximum rise time. The advantage of the algorithm is that it does not require linearization model of the quadrotor. Keyword:Altitude control Attitude control Fuzzy logic controller Mamdani Quadrotor Copyright © 2017 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Nia Maharani Raharja, Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia. Email: nia.sie13@mail.ugm.ac.id INTRODUCTIONQuadrotor is basically unstable because it is a non-linear model that has many variables with 6 degrees of freedom affected by four actuators. Previous researchers created quadrotor models in dynamics and kinematics represented in two models, namely nonlinear and linear. Quadrotor modeling needs some consideration, namely solid frames, rotor placement on each angle having symmetrical distance, and the position of the center of gravity (COG) is right at the midpoint of the crossing frame arm. Euler-Lagrange and Newton-Euler methods are generally used in aircraft modeling, however, Newton-Euler is more widely used because it is more easily understood.Researches on quadrotor have been widely conducted by previous researchers in the field of control by using conventional traditional controls as practiced by Bouabdallah et al [1] using the configuration Plus for microquadrotor OS4 model weighing 240 grams controlled by PID and LQR control methods by referring to six states of roll, pitch, and yaw based attitude. In designing PID control of non-linear model of quadrotor obtained from the dynamics and kinematics, it is firstly linearized to obtain a state variable in that the stability can be identified.Quadrotor modeling has been successfully developed by Bouabdallah & Siegwart [2] by modeling quadrotor from 6 to 12 states. In addition they modify backstepping algorithm by adding integral then called integral-backstepping. The algorithm is used to control the attitude and altitude, so that it can take off and landing autonomously. The algorithm is also used to control the position of the quadrotor in order to perform fly autonomously and avoid obstacles. With the algorithm, there is no overshoot when it hovers at the desired height but it takes a long raise time.One of the criteria for good performance in the control of hover is a quick or minimum response. However, the rapid response tends to cause oscillation and requires a fairly large control signaling. I...

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“…Coza and Macnab [7] provides a new method to design the adaptive-fuzzy controller to achieve stabilization of a quadrotor helicopter in the presence of sinusoidal wind disturbance. Santos et al [41] also implemented a fuzzy controller to control the quadrotor. And [19] proved its FLC can obtain the expected performances in the UAV control and navigation tasks.…”

Section: Related Workmentioning

confidence: 99%

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Olivares-Méndez

Suarez-Fernandez

et al. 2013

J Intell Robot Syst

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In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS-denied environments using Monocular Visual-Inertial SLAM-based strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate localization information. In the Matlab Simulink-based training framework, the Scaling Factor (SF) is adjusted according to the collision avoidance task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the control performances. After obtained the optimal SF and MF, 64 % of rules has been reduced (from 125 rules to 45 rules), and a large number of real see-and-avoid tests with a quadcopter have done. The simulation and experiment results show that this new proposed FLC can precisely navigates the Fail-Safe UAV to avoid the obstacle, obtaining better performances compared to only SF optimization-based FLC. To our best knowledge, this is the first work to present the optimized FLC using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.

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Intelligent fuzzy controller of a quadrotor

Cited by 161 publications

References 11 publications

Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy

Pengendalian Kestabilan Ketinggian pada Penerbangan Quadrotor dengan Metode PID Fuzzy

Hovering Control of Quadrotor Based on Fuzzy Logic

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

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