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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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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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the CPU usage of PM using evolutionary Neural Networks and Particle Swarm Optimization (PSO), Differential Evolution (DE), and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) optimization techniques were used for network training. The

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applications ,” IEEE Trans. Power Deliv. , vol. 15 , no. 4 , pp. 1311 – 1317 , 2000 . https://doi.org/10.1109/61.891520 . [17] Improved STATCOM control to improve transient stability of power of a power system using PSO technique ”, J. Xidian Univ. , vol

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The study was designed to explore the antioxidative effect of pomegranate seed oil (PSO) at different concentrations (5 and 7%) against oxidation of plant-based oils (canola oil and sunflower oil) during storage (60 days) as compared to artificial antioxidant butylated hydroxyanisole (BHA, 200 ppm). Rancimat and Schaal oven analysis were employed for the assessment of potential consequences of PSO against oxidation in plant based oils. The variation in total phenolic contents (TPC), antioxidant activity, peroxide value (POV), and tocopherol contents during storage were evaluated by Schaal oven test at 62 °C. The substantially (P≤0.05) higher induction period (IP) values were observed for PSO blended oil samples as compared to blank oil samples. The addition of PSO in plant-based oils improved the oxidative stability by enhancing the antioxidant potential and TPC, decreasing POV, and slowing down the degradation of tocopherol contents during storage. The findings of the present study suggest that PSO might be used as an alternative potential antioxidant to synthetic antioxidants.

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have gained popularity in optimization and AI for their ability to handle complex real-world challenges where traditional methods may struggle. The optimization technique known as Particle Swarm Optimization (PSO) draws inspiration from social behaviors

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Particle Swarm Optimization (PSO) algorithm, a heuristic optimization method, to estimate the parameters that define the impact zone on a beam. This method was specifically designed to accurately locate a non-punctual impact zone. To overcome these

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and complicated calculation [ 7 ]. This incorporation of Particle Swarm Optimization (PSO) not only improved the prediction accuracy of the neural network model but also contributed to a more robust and precise characterization of the asphalt foaming

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. In this section, a speech scrambling system using Particle Swarm Optimization (PSO) is introduced which is implemented using Matlab. To solve a practical optimization problem, the number of particles is often set between 10 and 50. At first, the

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] Kanović Ž. , Bugarski V. , Bačkalić T. Ship lock control system optimization using GA , PSO and ABC: A comparative review, Promet -Traffic&Transportation , Vol. 26 , No. 1 , 2014 , pp. 23 ‒ 31

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