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) and Particle Swarm Optimization (PSO) is presented. In [ 7 ], Generalized Normal Distribution algorithm (GNDA), and in [ 8 ], Firefly Optimization Algorithm (FOA) along with GA are implemented to achieve better V dc

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. Comparison of ARGA results with various approaches for test case A Variables PSO RSM SA BA ARGA Δ P G 1 −8.61 −8.808 −9.076 −9.010 −8.80 Δ P G 2 10.40 2.647 3.133 13.969 15.10 Δ P G 3 3.03 2.953 3.234 0.102 0.00 Δ P G 4 0.02 3.063 2.968 0.301 0.10 Δ P G 5 0

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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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cloud using the metaheuristic method. Some researchers [ 6 ] proposed a Particle Swarm Optimization (PSO) based method to minimize global costs of the workflows scheduling in the cloud, which are transmission and execution cost. Several

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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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Innovation in Electrical Power Engineering, Communication, and Computing Technology , Bhubaneswar, India , 2019 , Lecture Notes in Electrical Engineering , vol.  630 , 2020 , pp. 521 – 529 . [12] A. G. Pillai and E. R. Samuel , “ PSO based LQR

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