Identification and PID control for a quadrocopter

Pérez, Irma; Flores-Araiza, Daniel; Fortoul-Diaz, Jesus Anselmo; Maximo, R.; González-Hernández, Hugo G.

doi:10.1109/conielecomp.2014.6808571

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…The derivative response can lead to control system instability if the sensor feedback signal is noisy or if the control loop rate is excessively slow. [19,20] Moreover, here the error is the difference between the face of the person and the bound box of the Haar algorithm. The PID algorithm tries to reduce the distance between these two points by founding a stable point that makes the done be stable enough to make it move to roll, pitch, and yaw to follow the face correctly.…”

Section: Matrials and Methodsmentioning

confidence: 99%

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Abdallah

2023

Cihan U Erbil SCI J

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Drones have been frequently used for photography in recent years at significantly cheaper rates. However, the most modern drones are exceedingly error-prone and require precise manual control to take high-quality photos or films. We suggest using the AI method of Haar cascades with a PID controller to give drones vision, enabling them to do autonomous tracking and detection. This project aims to improve photography fields. The proposed system tries to detect the face and track the person's movements. This system will help photographers and journalists upgrade their work, even if it is used in surveillance and the military. The algorithm's results show that the DJI Tello tiny drone's camera is capable of detecting and tracking faces. The micro drone was picked since it is lightweight and compact, making its use safe and enabling testing to take place inside. Additionally, the DJI Tello may be easily programmed using Python. The position of the drone is contrasted with the set point in the center of the image to identify errors, allowing control signals for calculating forward/backwards, right/left, and yaw movements. The proposed system results show that the drone can detect and track the face very well, and the PID values are stable.

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Section: Matrials and Methodsmentioning

confidence: 99%

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Abdallah

2023

Cihan U Erbil SCI J

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“…A second flight excites z axis and a last flight moves around ψ direction. It is known from parameter identification theory that the chosen input signal should have enough frequencies in order to excite all the modes in the system, as highlighted in the work of Carro Pérez et al (2014). Therefore, we have chosen a sinusoidal signal with two non-multiple frequencies as…”

Section: Mathematical Model Of a Commercial Quadrotormentioning

confidence: 99%

Parameter Estimation Based on Linear Regression for Commercial Quadrotors

Benevides

Inoue

Paiva

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Over the last decade, the rapid evolution in communications technology, mechanics and electronics has propelled access to Unmanned Aerial Vehicles (UAVs). Among this class of robots, quadrotors stand out due to maneuverability and Vertical Take Off and Landing (VTOL) capability. This work proposes a methodology to solve the model parameter estimation problem of a commercial quadrotor. Mathematical models are derived and implementation details are given. Experimental results, obtained in an open source environment associated with a motion capture system, verify that the estimated dynamic model approximates to the real dynamics.

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“…In addition to the mentioned control methods, control models such as model predictive control, backstepping control and model reference adaptive control are also used [10,11]. Another popular method used for drone control is the proportional-integral-derivative (PID) controller [12]. Tuning the gain parameters of the PID controller is important, although the most commonly used method for tuning these parameters is trial and error.…”

Section: Introductionmentioning

confidence: 99%

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

Ulu,

Savaş,

Ergin

et al. 2024

Sensors

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Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional–integral–derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10−5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

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Identification and PID control for a quadrocopter

Cited by 6 publications

References 4 publications

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Parameter Estimation Based on Linear Regression for Commercial Quadrotors

Tuning the Proportional–Integral–Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach

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