Embedded Low Power Controller for Autonomous Landing of UAV Using Artificial Neural Network

Din, Ahmad; Bona, Basilio; Morrissette, Joel; Hussain, Moazzam; Violante, M.; Naseem, Mudassar

doi:10.1109/fit.2012.42

Cited by 10 publications

(28 citation statements)

References 17 publications

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“…[1][2][3][4]; also, for the aircraft trajectory's control during landing, the use of optimal control laws (H 2 , H ∞ , H 2 /H ∞ ), together with full-or reduced-order observers, provides good results [3,4]. Unfortunately, in the presence of unknown or partially known nonlinearities associated with aircraft or compensators' dynamics, the use of different adaptive control architectures such as those based on the dynamic inversion technique and neural networks (NNs), with or without Pseudo Control Hedging (PCH) blocks [3,[5][6][7][8][9] is needed. The adapting and the train of the NNs are based on the signals provided by the observers which receive information relative to the errors of the automatic control system (ACS).…”

Section: Antecedents and Motivationsmentioning

confidence: 99%

“…In this paper, we introduce a PCH block which limits the signal v with a component representing the actuator dynamics' estimation. The signal provided by PCH v h 1 ð Þ is a reference model's additional input [5].…”

Section: The Design Of the Two Alssmentioning

confidence: 99%

“…3.b, we impose negative roots for the characteristic equation of the linear system closed by unitary feedback after altitude H, having on its direct way the transfer function of the P.I.D. controller (described by (5)) in series with the transfer function of the linear system closed by unitary feedback after y 1 from Fig. 4.…”

Section: Numerical Simulation Setupmentioning

confidence: 99%

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Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

Lungu

2016

Asian Journal of Control

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This paper presents two new automatic landing systems (ALSs) for aircraft motion in longitudinal plane; the model of the landing geometry determines the flight trajectory and the aircraft calculated altitude; the flight trajectory during landing consists of two parts: the glide slope and the flare. Both designed ALSs have an adaptive system (ACS) for the aircraft output's control; for the first ALS, the output vector consists of the flying altitude and the longitudinal velocity, while, for the second ALS, the output variables are the pitch angle and the longitudinal velocity of aircraft. The second variant of ALS also contains an altitude controller providing the calculated pitch angle. The calculated altitude (for the first ALS), the calculated pitch angle (for the second ALS), and the desired flight velocity are provided to the ACS by means of a block consisting of two reference models. ACS is based on the dynamic inversion concept and contains an adaptive controller which includes a linear dynamic compensator, a state observer, a neural network, and a Pseudo Control Hedging block. The paper is focused both on the design of the two ALSs and on their complex software implementation and validation.

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Section: Antecedents and Motivationsmentioning

confidence: 99%

Section: The Design Of the Two Alssmentioning

confidence: 99%

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Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

Lungu

2016

Asian Journal of Control

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“…The controller demonstrated a good performance in simulation by driving the UAV within the required trajectory and attitude. An artificial neural network controller has been designed an ANN to track a real-time object and to detect and autonomously land on a safe area without the need for markers [16]. The proposed controller estimates the attitude and computes the horizontal displacement from the landing area.…”

Section: Introductionmentioning

confidence: 99%

“…Simulations were carried out and were experimentally verified showing the successful control of the UAV. In addition, an artificial neural network direct inverse control (DIC-ANN) to control a quadrotor UAV dynamics has been presented and applied in Reference [16]. The authors have presented a comparative study between the performance of the DIC-ANN and PID controller on the UAV attitude and dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets

Almeshal

Alenezi

2018

Robotics

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Time constraints is the most critical factor that faces the first responders' teams for search and rescue operations during the aftermath of natural disasters and hazardous areas. The utilization of robotic solutions to speed up search missions would help save the lives of humans who are in need of help as quickly as possible. With such a human-robot collaboration, by using autonomous robotic solutions, the first response team will be able to locate the causalities and possible victims in order to be able to drop emergency kits at their locations. This paper presents a design of vision-based neural network controller for the autonomous landing of a quadrotor on fixed and moving targets for Maritime Search and Rescue applications. The proposed controller does not require prior information about the target location and depends entirely on the vision system to estimate the target positions. Simulations of the proposed controller are presented using ROS Gazebo environment and are validated experimentally in the laboratory using a Parrot AR Drone system. The simulation and experimental results show the successful control of the quadrotor in autonomously landing on both fixed and moving landing platforms.

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Embedded Vision System for Automated Drone Landing Site Detection

Fraczek¹,

Mora

Kryjak³

2018

Computer Vision and Graphics

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Embedded Low Power Controller for Autonomous Landing of UAV Using Artificial Neural Network

Cited by 10 publications

References 17 publications

Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

A Vision-Based Neural Network Controller for the Autonomous Landing of a Quadrotor on Moving Targets

Embedded Vision System for Automated Drone Landing Site Detection

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