Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

López-Franco, Carlos; Gómez-Avila, Javier; Alanis, Alma Y.; Arana‐Daniel, Nancy; Villaseñor, Carlos

doi:10.3390/s17081865

Cited by 22 publications

(10 citation statements)

References 31 publications

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“…At the same time, the PD control was compared with the proportional-integral-derivative (PID) control to verify the role of the integral term in the course control. The expression of the PID control is as follows [ 38 ]:

where

and

are the three positive control parameters, and

is the target course. The parameters of the PID control are

and

.…”

Section: Course Keeping Field Experimentsmentioning

confidence: 99%

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Wang

Fan

et al. 2018

Sensors

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This paper presents a complete scheme for research on the three degrees of freedom model and response model of the vector propulsion of an unmanned surface vehicle. The object of this paper is “Lanxin”, an unmanned surface vehicle (7.02 m × 2.6 m), which is equipped with a single vector propulsion device. First, the “Lanxin” unmanned surface vehicle and the related field experiments (turning test and zig-zag test) are introduced and experimental data are collected through various sensors. Then, the thrust of the vector thruster is estimated by the empirical formula method. Third, using the hypothesis and simplification, the three degrees of freedom model and the response model of USV are deduced and established, respectively. Fourth, the parameters of the models (three degrees of freedom model, response model and thruster servo model) are obtained by system identification, and we compare the simulated turning test and zig-zag test with the actual data to verify the accuracy of the identification results. Finally, the biggest advantage of this paper is that it combines theory with practice. Based on identified response model, simulation and practical course keeping experiments are carried out to further verify feasibility and correctness of modeling and identification.

show abstract

where

and

are the three positive control parameters, and

is the target course. The parameters of the PID control are

and

.…”

Section: Course Keeping Field Experimentsmentioning

confidence: 99%

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Wang

Fan

et al. 2018

Sensors

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show abstract

“…Engine malfunction or failures are among the common faults in multi-rotor drones, which apparently endanger the drone and the people's safety on the ground. In order to increase flight safety and reliability of drones, researchers are working on automation enhancement to safely recover the impaired drone (Lopez-Franco et al 2017;Mazeh et al 2018;Nguyen et al 2019).…”

Section: The Landing System Frameworkmentioning

confidence: 99%

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Turan

Avşar

Asadi

et al. 2021

Turkish Journal of Engineering

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Flight safety and reliability improvement is an important research issue in aerial applications. Multi-rotor drones are vulnerable to motor failures leading to potentially unsafe operations or collisions. Therefore, researchers are working on autonomous landing systems to safely recover and land the faulty drone in on a desired landing area. In such a case, a suitable landing zone should be detected rapidly in for emergency landing. Majority of the works related with autonomous landing utilize a marker and GPS signals to detect landing site. In this work, we propose a landing system framework that involves only the processing of images taken from the onboard camera of the vehicle. First, the objects in the image are determined by filtering and edge detection algorithm, then the most suitable landing zone is searched. The area that is free from obstacles and closest to the center of the image is defined as the most immediate and suitable landing zone. The method has been tested on 25 images taken from different heights and its performance has been evaluated in terms runtime on a single board computer and detection precision and recall values. The average measured runtime is 2.4923 seconds and 100% of precision and recall values are achieved for the images taken from 1m and 2m. The smallest precision and recall values are 79.1% and 81.2%, respectively.

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“…• Single neuron PID controllers Examples of this group are works [6,[18][19][20][21]. These adaptive controllers are base on a single neuron whose inputs are the proportional error (P), integral of the error (I), and derivative of the error (D) (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

et al. 2020

Self Cite

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In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

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Visual Servoing for an Autonomous Hexarotor Using a Neural Network Based PID Controller

Cited by 22 publications

References 31 publications

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model

Image processing based autonomous landing zone detection for a multi-rotor drone in emergency situations

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

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