Modelling and Genetic Algorithm Based-PID Control of H-Shaped Racing Quadcopter

Alkamachi, Ahmed; Erçelebi, Ergün

doi:10.1007/s13369-017-2433-2

Cited by 43 publications

(36 citation statements)

References 17 publications

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“…Although the simplicity of the controllers' design (PID), the effect of proposed technique and designing the controllers by GA is shown in terms of tracking errors and stability, even with the big angles, subsequently, high velocities response and high dynamic performances, this is in contrast to works that used the PID with linearized model [12], [14], [15], [16], the variation of the angles is limited between -10° and 10° which is the linear range, so, limit the dynamics by low performance. Moreover, compared of [34], which is a similar work, when the GA was used to tune the PID's parameters, but in the evaluation's phase, they didn't use a high dynamic trajectory and the angles never exceeded the linear range. Even some works, where a nonlinear control is designed in, the used trajectory to evaluate the controller doesn't contain high dynamics with big range of angles; [1], [6], [7], and [35].…”

Section: Discussionmentioning

confidence: 99%

Decentralized PID Control by Using GA Optimization Applied to a Quadrotor

Hasseni¹,

Abdou²

2018

JAMRIS

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Quadrotors represent an effective class of aerial robots because of their abilities to work in small areas. We suggested in this research paper to develop an algorithm to control a quadrotor, which is a nonlinear MIMO system and strongly coupled, by a linear control technique (PID), while the parameters are tuned by the Genetic Algorithm (GA). The suggested technique allows a decentralized control by decoupling the linked interactions to effect angles on both altitude and translation position. Moreover, the using a meta-heuristic technique enables a certain ability of the system controllers design without being limited by working on just the small angles and stabilizing just the full actuated subsystem. The simulations were implemented in MATLAB/Simulink tool to evaluate the control technique in terms of dynamic performance and stability. Although the controllers design (PID) is simple, it shows the effect of the proposed technique in terms of tracking errors and stability, even with large angles, subsequently, high velocity response and high dynamic performances with practically acceptable rotors speed.

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Section: Discussionmentioning

confidence: 99%

Decentralized PID Control by Using GA Optimization Applied to a Quadrotor

Hasseni¹,

Abdou²

2018

JAMRIS

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“…If a traditional PID structure is represented by blocks, it is as follows: (11) where K ph , K ih , K dh hover PID coefficients, respectively. K pθ , K iθ , K dθ longitudinal flight PID coefficients, respectively.…”

Section: Control Systemmentioning

confidence: 99%

“…In [11] A. Alkamachi a trajectory tracking controller was proposed, in which four PID controllers are designed to stabilize the quadrotor and to achieve the required altitude and orientation. However, a nested loop PID controllers are designed to track the desired x and y position of the quadrotor.…”

Section: Introductionmentioning

confidence: 99%

Farkli Uçuş Durumlari Için Quadcopter Dinamik Modeli ve Simulasyonu

Oktay¹,

Köse²

2019

European Journal of Science and Technology

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In this paper, a four-rotor unmanned aerial vehicle was modeled, a control system was designed and performance evaluations were made. For the control system, a separate mathematical model of the unmanned aerial vehicle longitudinal, lateral and vertical takeoff and landing operations is omitted and is expressed as a state space model. The mathematical model of the wind disturbances that will affect the unmanned aerial vehicle during the flight was created and the situation was added to the space model. Proportional Integral Derivative (PID) control algorithm was used as the control. Unmanned aerial vehicle modeling was done in Solidworks and simulations were done in Matlab / Simulink program.

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“…In this section, in order to verify the effectiveness of the controller design algorithm proposed in this paper, simulations by using Matlab/Simulink for three control schemes are performed: (1) the adaptive neural network sliding mode controller (ANNSMC) proposed in this paper; (2) the classical neural network sliding mode controller (CNNSMC) proposed in [30]; (3) the dynamic inverse PID controller (DIPID) proposed in [31]. The designed controller is applied to the attitude control of the QTRA with respect to different reference signals: step signal, random noise, external disturbances, and hybrid superposition signal.…”

Section: Simulationsmentioning

confidence: 99%

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

2017

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A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS) is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA). Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF) neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

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Modelling and Genetic Algorithm Based-PID Control of H-Shaped Racing Quadcopter

Cited by 43 publications

References 17 publications

Decentralized PID Control by Using GA Optimization Applied to a Quadrotor

Decentralized PID Control by Using GA Optimization Applied to a Quadrotor

Farkli Uçuş Durumlari Için Quadcopter Dinamik Modeli ve Simulasyonu

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

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