Modeling, Control and Simulation of Quadrotor UAV

Bouaiss, Oussama; Mechgoug, Raihane; Ajgou, Riadh

doi:10.1109/ccssp49278.2020.9151687

Cited by 9 publications

(6 citation statements)

References 2 publications

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“…In this article, we consider the derivation of quadrotor aircraft dynamics subjected to parameter uncertainties and Gaussian wind gusts based on Newton's Euler formalism. The quadrotor structure is known as a cross rigid frame with four mounted rotors to produce the controlling thrust, modeling details can be found in Reference 32. The external unmodeled forces will all be considered disturbance and noise that will be covered by the adaptive control.…”

Section: Modeling Of Dynamicsmentioning

confidence: 99%

“…Taking into consideration of all applied forces and torques contributions to the model of quadrotor free spatial body, with assumptions as detailed in Reference 32, the dynamics may be written as follows:

$$ \left\{\begin{array}{l}\ddot{x}=\left( s...

…”

Section: Modeling Of Dynamicsmentioning

confidence: 99%

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Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

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SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

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Section: Modeling Of Dynamicsmentioning

confidence: 99%

$$ \left\{\begin{array}{l}\ddot{x}=\left( s...

…”

Section: Modeling Of Dynamicsmentioning

confidence: 99%

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Bouaiss

Mechgoug

Taleb‐Ahmed

et al. 2023

Adaptive Control & Signal

Self Cite

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“…The minimum and maximum variable ranges of the PID controller gains are obtained from the trial-and-error simulation using PID controller. The same values are used into equation ( 16), equation (17) and equation (18), and the Fuzzy PID controller is simulated as shown in Fig. 16, [32][38] [40].…”

Section: Fuzzy Controllermentioning

confidence: 99%

“…[18][22]. Based on Fig.3, rotation matrix of the body relative to the inertial frame, 𝑅 𝑇 = 𝑅𝜓𝑅𝜃𝑅𝜙 in equation (3) is resulted from the rotations on the linear independent axis[9][12][13][19][21][24].…”

mentioning

confidence: 99%

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Baharuddin,

Basri

2023

IJRCS

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Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems.

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“…Shifting to quadcopter control [3], the focus is on PID methodologies for quadcopter control. A linear PID controller is developed for altitude, attitude, heading, and position control.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Quadrotor UAV Efficiency Amidst Turbulent Winds in Mangrove Area: A Hybrid PID-Grey Wolf Optimizer Control Approach

Sadi,

Annisa,

Kamaruddin

2023

2023 IEEE 9th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

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Quadrotor Unmanned Aerial Vehicles (UAVs) offer versatile platforms for various applications including disaster response and environmental monitoring. However, their effective utilization in wind-disturbed environments such as mangroves poses unique challenges due to complex wind turbulence, impacting flight stability and navigation accuracy. Traditional control systems often fall short of ensuring robust and precise control in such conditions. This study presents a hybrid control approach combining a Proportional-Integral-Derivative (PID) control system with the Grey Wolf Optimizer (GWO) for enhanced UAV performance in challenging conditions. The PID controller, known for its effectiveness in industrial control systems, provides a control loop feedback mechanism to minimize flight errors, while the GWO, a bioinspired optimization algorithm, automates the process of tuning PID parameters. Preliminary results show that this hybrid PID-GWO system significantly improves the UAV's robustness and adaptability under varying wind conditions, outperforming the standalone PID controller. This research illuminates a new direction for optimizing UAV performance in wind-disturbed environments and suggests further exploration of bio-inspired optimization techniques in UAV control systems.

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Modeling, Control and Simulation of Quadrotor UAV

Cited by 9 publications

References 2 publications

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Enhancing Quadrotor UAV Efficiency Amidst Turbulent Winds in Mangrove Area: A Hybrid PID-Grey Wolf Optimizer Control Approach

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