A new approach for designing robust adaptive fuzzy sliding mode controller

Barghandan, Saeed; Badamchizadeh, Mohammad Ali

doi:10.1109/iraniancee.2013.6599584

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The process of changing the parameters of backstepping takes a considerable time, so that to accelerate the process requires adaptive artificial intelligence algorithms such as fuzzy logic. The other modern control algorithms such as slidding mode algorithm combined with fuzzy algorithm by Barghandan & Badamchizadeh [6] to control the quadrotor attitude. The fuzzy algorithm is used to change the parameter values contained in slidding mode control, so that the control is more optimal.…”

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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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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A simulation approach for closed-loop control of coupled four tank system

Ullah

Alsharef

2021

Int. J. Model. Simul. Sci. Comput.

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The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations.

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Adaptive Dynamic Inversion Controller for Quad Rotor Aerial Robot

章¹

2014

DSC

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This paper presents an adaptive dynamic inversion controller for trajectory tracking of quad rotor aerial robot with X type configuration. In order to enhance the robustness of dynamic inversion controller, an adaptive dynamic variable vector is used to compensate the model inversion error and achieve the asymptotic stability. On the base of a nonlinear dynamic model of the quad rotor, adaptive dynamic inversion nonlinear controller is synthesized for the purpose of stabilization and trajectory tracking. The proposed control method can compensate the deviation of model inaccuracy. It behaves perfectly at the reference altitude and trajectory track in MATLAB simulation.

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A new approach for designing robust adaptive fuzzy sliding mode controller

Cited by 4 publications

References 13 publications

Hovering Control of Quadrotor Based on Fuzzy Logic

Hovering Control of Quadrotor Based on Fuzzy Logic

A simulation approach for closed-loop control of coupled four tank system

Adaptive Dynamic Inversion Controller for Quad Rotor Aerial Robot

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