Optimal Direct Adaptive Fuzzy Controller based on Bat Algorithm for UAV Quadrotor

Glida, Hossam Eddine; Abdou, Latifa; Chelihi, Abdelghani; Sentouh, Chouki

doi:10.1109/icsc47195.2019.8950585

Cited by 8 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where 0 < μ n,m < 1 is the firing strengths of rules R k for k = 1, …, 9 which satisfies the condition and ξ i ¼ ½ξ 1 i , …, ξ n, m i T are the vectors of the height of the membership functions of 37 Substituting the new control law (25), the closed-loop dynamics (17) becomes…”

Section: Model-free Fuzzy Controlmentioning

confidence: 99%

Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Glida

Abdou

Chelihi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

View full text Add to dashboard Cite

This article deals with the issue of designing a flight tracking controller for an unmanned aerial vehicle type of quadrotor based on an optimal model-free fuzzy logic control approach. The main design objective is to perform an automatic flight trajectory tracking under multiple model uncertainties related to the knowledge of the nonlinear dynamics of the system. The optimal control is also addressed taking into consideration unknown external disturbances. To achieve this goal, we propose a new optimal model-free fuzzy logic–based decentralized control strategy where the influence of the interconnection term between the subsystems is minimized. A model-free controller is firstly designed to achieve the convergence of the tracking error. For this purpose, an adaptive estimator is proposed to ensure the approximation of the nonlinear dynamic functions of the quadrotor. The fuzzy logic compensator is then introduced to deal with the estimation error. Moreover, the optimization problem to select the optimal design parameters of the proposed controller is solved using the bat algorithm. Finally, a numerical validation based on the Parrot drone platform is conducted to demonstrate the effectiveness of the proposed control method with various flying scenarios.

show abstract

Section: Model-free Fuzzy Controlmentioning

confidence: 99%

Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Glida

Abdou

Chelihi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

View full text Add to dashboard Cite

show abstract

“…The study offers a solution that employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) for identifying flight dynamics and utilizes genetic algorithms (GA) to optimize parameters for attitude control. In [17] an optimal direct, robust adaptive fuzzy controller for a quadrotor system using bat algorithm (BA) is presented. This approach aims to improve control by breaking down the system into four single-input and single-output subsystems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

Rodríguez-Abreo,

Rodríguez-Reséndiz,

García-Cerezo

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…For helicopter control design, more advanced and complicated control methods are studied in recent years and some of them are Pedro and Mathe (2015), Tang et al (2015); Cheng et al (2016), Yan et al (2016); Glida et al (2019), Hu et al (2020); and Shi and Cheng (2020). Also, a series of researches have been performed recently on the design of autopilot and morphing system for quadrotors and hexarotors (Köse and Oktay, 2020a, 2020b, 2021a, 2021b, 2021c).…”

Section: Introductionmentioning

confidence: 99%

A vision-aided fuzzy adaptive sliding mode controller for autonomous landing of a nonlinear model helicopter on a moving marine platform

Pakro

Nikkhah

2022

AEAT

View full text Add to dashboard Cite

Purpose A vision-assisted fuzzy adaptive sliding mode controller is presented in this research and implemented on a nonlinear helicopter model, which is about to land on a moving ship. Stabilization of the dynamics and tracking the landing path are required, at the same time. This study aims to take one step closer to fully autonomous landing, which is a growing trend. Design/methodology/approach An integrated guidance and control is considered for the model helicopter. A fuzzy logic is designed to adaptively choose the best control parameters for the sliding mode controller and solve the challenge of parameter tuning. A self-organizing matrix consisting of fuzzy sliding mode parameters is formed instead of a single parameter with the goal of enhancing controller tracking capability. A simple, precise and fast image recognition system based on OpenCV is used to detect the proper point for descending without getting any special data from the ship and by only using a general “H” sign. Findings The problem is simulated under intense disturbances, while the approach and landing performances are acceptable. Controller performance is compared and validated. Simulation results show the robustness, agility, stability and outperformance of the proposed controller. Originality/value The novelty of this paper is the designed procedure for using a simple image recognition system in the process of autonomous ship-landing, which does not use any special data sent from the ship. Besides, an improved nonlinear controller is designed for integrated guidance and control in this specific application.

show abstract

Optimal Direct Adaptive Fuzzy Controller based on Bat Algorithm for UAV Quadrotor

Cited by 8 publications

References 16 publications

Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Fuzzy logic controller for UAV with gains optimized via genetic algorithm

A vision-aided fuzzy adaptive sliding mode controller for autonomous landing of a nonlinear model helicopter on a moving marine platform

Contact Info

Product

Resources

About