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A platform for research: civil engineering, architecture and urbanism
Design an Optimal ANFIS Controller using Bee Colony Optimization for Trajectory Tracking of a Quadrotor UAV
https://orcid.org/http://orcid.org/0000-0003-4169-0872
https://orcid.org/http://orcid.org/0000-0002-4870-5531
https://orcid.org/http://orcid.org/0000-0003-2953-1772
This paper presents a new neuro-fuzzy bee colony optimization method to find the optimal distribution of the membership functions (MFs) in the design of neuro-fuzzy controllers for complex nonlinear systems. The paper proposes an intelligent controller based on adaptive network-based fuzzy inference system (ANFIS) and bee colony optimization (BCO) algorithm to govern the behavior of a three-degree-of-freedom quadrotor unmanned aerial vehicle. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D vertical plane, and the BCO algorithm aims to dynamically find the optimal distribution of the MFs at the input and output ends of the ANFIS in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed BCO-tuned ANFIS controller, a comparison between the proposed ANFIS-BCO controller and other controller’s performance such as ANFIS and proportional-integral-derivative controllers is illustrated using the same system. The comparison of results shows that the proposed ANFIS-BCO method outperforms the other methods already developed in the same study.
Design an Optimal ANFIS Controller using Bee Colony Optimization for Trajectory Tracking of a Quadrotor UAV
https://orcid.org/http://orcid.org/0000-0003-4169-0872
https://orcid.org/http://orcid.org/0000-0002-4870-5531
https://orcid.org/http://orcid.org/0000-0003-2953-1772
This paper presents a new neuro-fuzzy bee colony optimization method to find the optimal distribution of the membership functions (MFs) in the design of neuro-fuzzy controllers for complex nonlinear systems. The paper proposes an intelligent controller based on adaptive network-based fuzzy inference system (ANFIS) and bee colony optimization (BCO) algorithm to govern the behavior of a three-degree-of-freedom quadrotor unmanned aerial vehicle. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D vertical plane, and the BCO algorithm aims to dynamically find the optimal distribution of the MFs at the input and output ends of the ANFIS in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed BCO-tuned ANFIS controller, a comparison between the proposed ANFIS-BCO controller and other controller’s performance such as ANFIS and proportional-integral-derivative controllers is illustrated using the same system. The comparison of results shows that the proposed ANFIS-BCO method outperforms the other methods already developed in the same study.
Design an Optimal ANFIS Controller using Bee Colony Optimization for Trajectory Tracking of a Quadrotor UAV
https://orcid.org/http://orcid.org/0000-0003-4169-0872
https://orcid.org/http://orcid.org/0000-0002-4870-5531
https://orcid.org/http://orcid.org/0000-0003-2953-1772
This paper presents a new neuro-fuzzy bee colony optimization method to find the optimal distribution of the membership functions (MFs) in the design of neuro-fuzzy controllers for complex nonlinear systems. The paper proposes an intelligent controller based on adaptive network-based fuzzy inference system (ANFIS) and bee colony optimization (BCO) algorithm to govern the behavior of a three-degree-of-freedom quadrotor unmanned aerial vehicle. The quadrotor was chosen due to its simple mechanical structure; nevertheless, these types of aircraft are highly nonlinear. Intelligent control such as fuzzy logic is a suitable choice for controlling nonlinear systems. The ANFIS controller is used to reproduce the desired trajectory of the quadrotor in 2D vertical plane, and the BCO algorithm aims to dynamically find the optimal distribution of the MFs at the input and output ends of the ANFIS in order to reduce learning errors and improve the quality of the controller. To evaluate the performance of the proposed BCO-tuned ANFIS controller, a comparison between the proposed ANFIS-BCO controller and other controller’s performance such as ANFIS and proportional-integral-derivative controllers is illustrated using the same system. The comparison of results shows that the proposed ANFIS-BCO method outperforms the other methods already developed in the same study.
Design an Optimal ANFIS Controller using Bee Colony Optimization for Trajectory Tracking of a Quadrotor UAV
J. Inst. Eng. India Ser. B
Selma, Boumediene (author) / Chouraqui, Samira (author) / Selma, Belkacem (author) / Abouaïssa, Hassane (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1505-1519
2022-10-01
15 pages
Article (Journal)
Electronic Resource
English
Optimal Design for Fuzzy Controller by Ant Colony Algorithm
| British Library Online Contents | 2010