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Pollack Periodica
Authors:
Ali Kareem Abdulrazzaq
,
György Bognár
, and
Balázs Plesz

. This paper uses the particle swarm optimization algorithm to solve this equation and determine the five parameters. PSO is one of the well-regarded algorithms in the literature for optimization tasks and is widely used in both science and industry. PSO

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], particle swarm optimization (PSO) [ 20 ], and Grey-wolf optimization [ 21 ] are examples of common optimization methods that are given and used in exoskeleton devices. Algorithm for ant colony optimization [ 22 ] was also used. GA is easy to use and capable

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to be approximated by H BP and k is the coefficients (k 1 , k 2 , k 3 ) of FO BPF function given in (4) . Fig. 2. Flow Chart of the bi-level PSO algorithm

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drilling data was used from Khangiran field to calculate the difference between the actual penetration rate and the predicted one by Particle Swarm Optimization (PSO) [ 9 ], Dynamic Differential Annealing Optimization, (DDAO) [ 10 ], Artificial Bee Colony

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techniques such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Butterfly Optimization Algorithm (BOA), and Gray Wolf Optimization (GWO) to tune the design parameters of the proposed observer for further improvement [ 28

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Abstract

In this paper a complete methodology of modeling and control of quad-rotor aircraft is exposed. In fact, a PD on-line optimized Neural Networks Approach (PD-NN) is developed and applied to control the attitude of a quad-rotor that is evolving in hostile environment with wind gust disturbances and should maintain its position despite of these troubles. Whereas PD classical controllers are dedicated for the positions, altitude and speed control. The main objective of this work is to develop a smart Self-Tuning PD controller for attitude angles control, based on neural networks capable of controlling the quad-rotor for an optimized performance thus following a desired trajectory. Many problems could arise if the quad-rotor is evolving in hostile environments presenting irregular troubles such as wind gusts modeled and applied to the overall system. The quad-rotor has to rapidly achieve tasks while guaranteeing stability and precision and must behave quickly with regards to decision making fronting turbulences. This technique offers some advantages over conventional control methods such as PD controllers. Simulation results are achieved with the use of Matlab/Simulink environment and are established on a comparative study between PD and PD-NN controllers founded on wind disturbances application. These obstacles are applied with numerous degrees of strength to test the quad-rotor comportment. Experimental results are reached with the use of the V-REP environment with which some trajectories are tracked and then applied on a BLADE Inductrix FPV+. These simulations and experimental results are acceptable and have confirmed the efficiency of the proposed PD-NN approach. In fact, this controller has fairly smaller errors than the PD controller and has an improved ability to reject troubles. Moreover, it has confirmed to be extremely vigorous and efficient fronting disturbances in the form of wind disturbances.

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International Review of Applied Sciences and Engineering
Authors:
Ayad Q. Al-Dujaili
,
Amjad J. Humaidi
,
Daniel Augusto Pereira
, and
Ibraheem Kasim Ibraheem

, 2020 . Available : https://doi.org/10.1080/01969722.2020.1758467 . [35] A. J. Humaidi , and H. Mustafa , “ Development of a new adaptive backstepping control design for a non-strict and under-actuated system based on a PSO tuner ,” Inform. J

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on PSO tuner ,” in 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) , 2021 , pp. 323 – 328 . [31] A. J. Humaidi and M. R. Hameed , “ Development of a new adaptive backstepping control design for a non

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produced quantity matrix, will contain 130 variables. 3 Distance and movement In the research, the swarm methods like the Particle Swarm Optimization (PSO) [ 7 ] and the Firefly Algorithm (FA) [ 8 ] are very often used; both of them are common and widely

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respondents named TÉSZ (Producer Sales Organization or PSO hereinafter) as the most important business relationship. After identifying the first-round customers, the aim was to identify further customers, i.e. to which the participant producer’s customer

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