Particle Swarm Optimization Based Optimal PID Controller for Quadcopters

Sonugür, Güray; Gçkçe, Celal Onur; Koca, Yavuz Bahadır; Inci, Şevket Semih

doi:10.7546/crabs.2021.12.11

Cited by 4 publications

(3 citation statements)

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“…Three of the outputs are position variables x, y and z, other three of the outputs are orientation variables ϴ, φ and ψ. The mathematical model of the quadcopter is as follows (Sonugur et al 2021):…”

Section: Mathematical Model Of Quadcoptermentioning

confidence: 99%

“…It is reported that simulation results agreed with real quadcopter experimental values. PSOoptimized PID control of quadcopter is implemented and tested for various disturbances by (Sonugur et al 2021). The studies in the literature are mostly based on classical controller architecture where the error signal which is the difference between reference and measured output is fed into the controller.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Quadcopter Control Using Artificial Neural Networks

GÖKÇE

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high dimensions. About ten percent improvement over state-of-the-art controllers is observed and results are reported both numerically and graphically. Promising results encourage to use the type of controller proposed for various real applications.

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Section: Mathematical Model Of Quadcoptermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Quadcopter Control Using Artificial Neural Networks

GÖKÇE

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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“…However, there are considerations that the PID method cannot be applied effectively in quadrotors due to its non-linear structure. According to these considerations, the PID method cannot always efficiently control systems due to changing conditions [11][12][13]. At this point, optimization methods may need to be included in the process to use the PID controller in nonlinear applications such as quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers

2023

Teh. vjesn.

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In the present study, different solution methods are discussed in order to control the quadrotor with the most optimal PID parameters for the determined purposes. One of these methods is to make use of meta-heuristic algorithms in control systems. There are some limitations of using a PID controller as a classical construct. However, it is thought that more successful results will be obtained by optimizing its parameters through meta-heuristic algorithms. Initially, the mathematical model of the vehicle was created in MATLAB/Simulink. Then, genetic algorithms (GA), artificial bee colony (ABC), particle swarm optimization (PSO) and firefly algorithms (FA) were determined respectively as optimization methods. And these optimization methods used to determine the PID control parameters are applied to the developed mathematical model in the MATLAB/Simulink environment. In addition, the performances of the optimization methods are evaluated according to the comparison criteria. As a result of the comparison carried out according to ITAE (Integral Time Absolute Error) fitness criteria, ABC (1.2% -4.4%) in terms of altitude, FA (4% -13%) in terms of roll angle, GA (13% -%21) in terms of pitch angle, and PSO (4% -%15) in terms of yaw angle has been more successful than other methods.

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PSO–PID Controller for Quadcopter UAV: Index Performance Comparison

Sahrir

Basri

2023

Arab J Sci Eng

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Particle Swarm Optimization Based Optimal PID Controller for Quadcopters

Cited by 4 publications

References 0 publications

Intelligent Quadcopter Control Using Artificial Neural Networks

Intelligent Quadcopter Control Using Artificial Neural Networks

Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers

PSO–PID Controller for Quadcopter UAV: Index Performance Comparison

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