Optimal Fuzzy Adaptive Backstepping Controller for Attitude Control of a Quadrotor Helicopter

Glida, Hossam-Eddine; Abdou, Latifa; Chelihi, Abdelghani

doi:10.1109/iccad46983.2019.9037915

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Yin et al have used the fireworks algorithm for parameter tuning in a hypersonic vehicle sliding mode control [537]. Glida et al have used flower pollination algorithm to optimize a fuzzy adaptive backstepping controller for quadrotor attitude control [538]. A similar approach has been used by Basri and Noordin using gravitational search optimization [539].…”

Section: Optimal Guidance and Controlmentioning

confidence: 99%

“…[530] 2020 Control parameter tunning CS [531] 2016 Control parameter tunning DE [532] 2016 Control parameter tunning DE [533] 2021 Control parameter tunning FA [534] 2021 Control parameter tunning FA [535] 2015 Control parameter tunning FA [536] 2022 Control parameter tunning FA [537] 2018 Control parameter tunning FAO [538] 2019 Control parameter tunning FPA [539] 2020 Control parameter tunning GSO [540] 2017 Control parameter tunning GSO [541] 2021 Control parameter tunning HS [542] 2020 Control parameter tunning HHO [543] 2023 Control parameter tunning PIO [544] 2022 Control parameter tunning PSO [545] 2022 Swarm motion and formation BeeA [546] 2019 Swarm motion and formation DE [547] 2019 Swarm motion and formation DE [548] 2020 Swarm motion and formation GWO [549] 2022 Swarm motion and formation MFO [550] 2023 Swarm motion and formation PSO [551] 2020 Swarm motion and formation GA [552] 2023 Swarm motion and formation ACO, DE [553] 2017 Swarm motion and formation HS [554] 2022 Swarm mission planning and task allocation FAO 2019 Swarm motion and formation DE [547] 2019 Swarm motion and formation DE [548] 2020 Swarm motion and formation GWO [549] 2022 Swarm motion and formation MFO [550] 2023 Swarm motion and formation PSO [551] 2020 Swarm motion and formation GA [552] 2023 Swarm motion and formation ACO, DE [553] 2017 Swarm motion and formation HS [554] 2022 Swarm mission planning and task allocation FAO [555] 2022 Swarm mission planning and task allocation FAO [556] 2022 Swarm mission planning and task...…”

Section: Reference Publication Year Application Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

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This paper reviews a majority of the nature-inspired algorithms, including heuristic and meta-heuristic bio-inspired and non-bio-inspired algorithms, focusing on their source of inspiration and studying their potential applications in drones. About 350 algorithms have been studied, and a comprehensive classification is introduced based on the sources of inspiration, including bio-based, ecosystem-based, social-based, physics-based, chemistry-based, mathematics-based, music-based, sport-based, and hybrid algorithms. The performance of 21 selected algorithms considering calculation time, max iterations, error, and the cost function is compared by solving 10 different benchmark functions from different types. A review of the applications of nature-inspired algorithms in aerospace engineering is provided, which illustrates a general view of optimization problems in drones that are currently used and potential algorithms to solve them.

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Section: Optimal Guidance and Controlmentioning

confidence: 99%

Section: Reference Publication Year Application Algorithmmentioning

confidence: 99%

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Darvishpoor,

Darvishpour,

Escarcega

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…They can be obtained through input-output system analysis, such as artificial neural networks (ANNs), or based on expertise (such as in fuzzy logic controllers (FLCs) [24]. On the other hand, a quadrotor is a multi-input-multi-output (MIMO) system that shows non-linear dynamic behavior such as a high coupling degree and unknown nonlinearities [25]. By using a hierarchical fuzzy method, the complexity of the MIMO systems can be reduced [26].…”

Section: Introductionmentioning

confidence: 99%

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

Ginting¹,

Doo

Pollo

et al. 2022

Journal of Robotics and Control

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A quadrotor is an unmanned aerial vehicle (UAV) with two pairs of rotors rotating in opposite directions. Some of its unique abilities are hovering and vertical take-off and landing (VTOL). Most recent works carried out the UAVs' rotation parametrization using Euler angles and a quaternion. Those UAVs suffer from singularities and ambiguities. A geometric control is generally used to deal with those problems. Exponential coordinate in the geometric control maps R3 into SO(3). This paper presented a fuzzy logic controller on SO(3) to control the attitude of the quadrotor. The input of the fuzzy logic controller is the angular velocity (ω) and exponential coordinate error of rotation (ζ), while the output is torque (τ). The error function in this controller is a rotation matrix on SO(3). This proposed controller can control the attitude of the quadrotor based on the expected attitude for maneuvers both on one axis and all axis with a steady-state error of about 0.02 rad.

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“…12. Intelligent controllers such as fuzzy logic control (FLC), [13][14][15] and neural network control [16][17][18] are also investigated with the previous approaches to solve such control problems.…”

Section: Introductionmentioning

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:

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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.

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Optimal Fuzzy Adaptive Backstepping Controller for Attitude Control of a Quadrotor Helicopter

Cited by 6 publications

References 11 publications

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones

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

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

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