Attitude Control of a Quadrotor with Fuzzy Logic Controller on SO(3)

Ginting, Almido Haryanto; Doo, Samy Yeverson; Pollo, Don E. D. G.; Djahi, Hendrik; Mauboy, Evtaleny R.

doi:10.18196/jrc.v3i1.12956

Cited by 6 publications

(3 citation statements)

References 17 publications

(21 reference statements)

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“…SMC is a nonlinear control technique that offers robustness to disturbances and uncertainties [14]. Fuzzy Logic and Neural Networks are intelligent control techniques that allow for nonlinear mapping of inputs to outputs [15,16]. Backstepping is a recursive design method for designing controllers for nonlinear systems [17].…”

Section: Introductionmentioning

confidence: 99%

A PyQt6-Based Platform for Real-Time Control and Monitoring of a Quadrotor Multibody System Using ROS2 and Gazebo

Djizi¹,

Zahzouh²

2023

JESA

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This paper presents a PyQt6 server-based application design for controlling a quadrotor multibody system in a simulated environment using the Gazebo 3D model and ROS2 on Linux. The combination of PyQt6 with ROS2 offers an intuitive graphical interface that simplifies access to control parameters and flight modes. The system incorporates a unique Gazebo plugin that connects to a proportional-derivative (PD) controller, providing stable quadrotor flight control. Notably, this plugin facilitates precise quadrotor movements and establishes reliable communication between the server and quadrotor, distinguishing it from other plugins. Moreover, simulation results demonstrate the effectiveness of the proposed PyQt6 server-based application in real-time quadrotor control. The results exemplify the system's capability to achieve stable and precise quadrotor movement by effectively controlling motion along the three axes (x, y, and z) along with yaw. However, the primary contribution of the system presented in this paper lies in the development of a robust PyQt6 server-based application designed to control a quadrotor multibody system. Furthermore, the system exhibits inherent potential for extension to encompass the control of a physical quadrotor, thereby substantiating its viability in real-world applications.

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Section: Introductionmentioning

confidence: 99%

A PyQt6-Based Platform for Real-Time Control and Monitoring of a Quadrotor Multibody System Using ROS2 and Gazebo

Djizi¹,

Zahzouh²

2023

JESA

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“…Quadrotor or Quadcopter [1] is a type of Unmanned Aerial Vehicles (UAV) that has four rotors at the end of each frame [2]. It has been used in photography, shipping, delivery, mapping, military, education, hobby, as well as in search and rescue [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Altitude Control of UAV Quadrotor Using PID and Integral State Feedback

Ma’arif,

Suwarno,

Nur’aini

et al. 2023

BIO Web Conf.

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Applications of control techniques for stabilizing altitude in a UAV Quadrotor, along with a comprehensive performance comparison, are presented in this paper. The two compared control techniques are: a Proportional Integral Derivative (PID) and Integral State Feedback (ISF) controller. While PID control consists of a Proportional, an Integral and a Derivative Controller, the Integral State Feedback consists of an Integral and a State Feedback Controller. Each part of the control technique provides advantages and drawbacks in the controlled system performance. Numerical simulations in the research were performed on Simulink MATLAB to provide quantitative results in control performance comparison; thus, a quadrotor model was designed prior to the application of control techniques. Based on the numerical results, ISF control resulted in a better settling time with zero overshoot than PID. Meanwhile, the PID control had a better rise time with a big overshoot than ISF in its system response. Therefore, it can be concluded that the ISF Controller was better than PID regarding the settling time and the overshoot response.

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“…Fuzzy also can be combined with other methods or algorithms such as PID [11]- [13], B-Spline [14], particle swarm [15], and many others. Even though fuzzy is usually used for control [16]- [18], however recently, a lot of new fuzzy methods has been developed not only for control but also for other real-world problems, such as the multi-criteria decisionmaking (MCDM) problem [19]- [21], forecasting [22]- [24], clustering [25] and many others.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted

Hakim

Muwardi

Yunita

et al. 2022

IJEECS

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Fuzzy Mamdani knows as one of the modern control systems. It was known to have a better performance result when compared to conventional methods. However, because the input of this modern control system sometimes is based on human experience, therefore its performance is sometimes below the conventional one. We propose using the adaptive neuro fuzzy inference system assisted (ANFIS) approach to optimize the fuzzy Mamdani membership function input to overcome this problem. We have tested our hypotheses in water chiller applications based on microcontrollers. Even though it is still behind conventional methods to cool 200 ml water, which is 6 minutes, using fuzzy ANFIS methods, we manage to improve the speed performance in cooling water from 20 minutes to 17 minutes, which is from room temperature to just 24 <sup>o</sup>C.

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Attitude Control of a Quadrotor with Fuzzy Logic Controller on SO(3)

Cited by 6 publications

References 17 publications

A PyQt6-Based Platform for Real-Time Control and Monitoring of a Quadrotor Multibody System Using ROS2 and Gazebo

A PyQt6-Based Platform for Real-Time Control and Monitoring of a Quadrotor Multibody System Using ROS2 and Gazebo

Altitude Control of UAV Quadrotor Using PID and Integral State Feedback

Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted

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