Flight Reliability of Multi Rotor UAV Based on Genetic Algorithm

Huang, Ronghui; Li, Xun; Zhang, Xin; Pei, Huikun

doi:10.12928/telkomnika.v14i2a.4354

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The three parameters are proportion of error (P), integral of error (I) and differential of error (D) control. PID is in feedback control mechanism which continuously calculates error as the difference between set point and measured variable [20][21][22].…”

Section: Pid Controllermentioning

confidence: 99%

Improvement of Pitch Motion Control of an Aircraft Systems

et al. 2018

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The movement of the aircraft pitch is very important to ensure the passengers and crews are in intrinsically safe and the aircraft achieves its maximum stability.The objective of this study is to provide a solution to the control system that features particularly on the pitch angle motion of aircraft systemin order to have a comfort boarding. Three controllers were developed in these projects which wereproportional integral derivative (PID), fuzzy logic controller (FLC), and linear quadratic regulator (LQR) controllers. These controllers will help improving the pitch angle and achievingthe target reference. By improving the pitch motion angle, the flight will be stabilized and in steady cruise (no jerking effect), hence provides all the passengers withthe comfort zone. Simulation results have been done and analyzed using Matlab software. The simulation results demonstrated LQR and FLC were better than PID in the pitch motion system due to the small error performance. In addition, withstrong external disturbances, a single controller is unable to control the system, thus, the combination of PID and LQR managed to stabilize the aircraft.

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Section: Pid Controllermentioning

confidence: 99%

Improvement of Pitch Motion Control of an Aircraft Systems

et al. 2018

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“…where 𝜆 = [𝜆 1 , 𝜆 2 , 𝜆 3 , 𝜆 4 ] is the parameter adjusting vector which is determined by the PSO. The different characteristics of fitness function affect how easy or difficult the problem is offered for a PSO algorithm [20]. The most commonly used fitness function is minimizing the errors, which is between the output and input signal for the torpedo object.…”

Section: Fuzzy Controller Design Based On Particle Swarm Optimizationmentioning

confidence: 99%

Optimal Control for Torpedo Motion based on Fuzzy-PSO Advantage Technical

Do¹,

Dang²

2018

TELKOMNIKA

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The torpedo is a nonlinear object which is very difficult to control. Via to manage the rudder angle yaw, the diving plane angle, and the fin shake reduction, the torpedo yaw horizontal, the depth vertical and roll damping of the system are controlled accurately and steadily. In this paper, the particle swarm optimization is used to correct the imprecision of architecture fuzzy parameters. The coverage width of membership function and the overlap degree influence of neighboring membership functions are considered in the method to adjust dynamically from the system errors. Thereby optimizing the control signal and enhancing the torpedo motion quality. The proposed method results in a better performance compared to the other control method such as adaptive fuzzy-neural that proved effective of the proposed controller.

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“…The fundamental elements of GA were first proposed by Holland [25]. Later on, other literatures were reported [26][27][28] discussing its remarkable concept and applications. It is basically a nature inspired stochastic search algorithm based on natural evolution and selection which favours the fact that stronger individuals bear the greater potential to strive against all odds in a competing environment and the weaker one gradually extinct.…”

Section: Genetic Algorithmmentioning

confidence: 99%