Supervisory Control of Autonomous UAVs in Networked Environments

Howitt, Sara; Richards, Dale; White, Alan

doi:10.2514/6.2005-7023

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Thus there is limited available research on low-cost, small-sized fixed wing UAVs for commercial applications. Currently, there are a few universities working towards the development and implementation of different algorithms on UAVs in real-time [4,5]. One such fixed-wing platform is under development at the School of Aerospace, Civil and Mechanical Engineering in UNSW@ADFA.…”

Section: Introductionmentioning

confidence: 99%

Real-time validation of a dual neural network controller for a low-cost UAV

Puttige

Anavatti

Samal

2009

2009 IEEE International Conference on Industrial Technology

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This paper describes a novel indirect adaptive control technique based on neural networks for Unmanned Aerial Vehicles (UAV). Two neural networks are amalgamated to provide faster tracking of the commanded reference. A pre-trained internal model network and an online trained controller network form the core of the dual neural network (DNN) architecture. A Telemaster UAV equipped with various sensors and an onboard data-logger and control (ODC) unit forms the experimental platform. The plant model and the controller are designed in numerical simulations based on data obtained from experimental flights of the UAV. The DNN controller is validated on a real-time hardware in loop (HIL) simulation. Results from the numerical simulations and HIL validations are provided.

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Section: Introductionmentioning

confidence: 99%

Real-time validation of a dual neural network controller for a low-cost UAV

Puttige

Anavatti

Samal

2009

2009 IEEE International Conference on Industrial Technology

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show abstract

“…Hence there is limited research available on lowcost, small-sized fixed wing UAVs for commercial applications. Currently, there are few universities working towards the development and implementation of different algorithms on UAVs in real-time [6]- [8]. One such fixedwing platform is under development at the School of Aerospace, Civil and Mechanical Engineering (ACME) in UNSW@ADFA.…”

Section: Introductionmentioning

confidence: 99%

Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

Puttige¹,

Anavatti²

2008

JCP

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In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to environmental variations. This task of accurate modelling has been achieved with a multi-network architecture. The multi-network with dynamic selection technique allows a combination of online and offline neural network models to be used in the architecture where the most suitable outputs are selected based on a given criterion. The neural network models are based on the autoregressive technique. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. The multi-network dynamic selection technique has been validated on real-time hardware in the loop (HIL) simulation and the results show the superiority in performance compared to the individual models.

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Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV

Puttige

Anavatti

Ray

AI 2007: Advances in Artificial Intelligence

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Supervisory Control of Autonomous UAVs in Networked Environments

Cited by 5 publications

References 13 publications

Real-time validation of a dual neural network controller for a low-cost UAV

Real-time validation of a dual neural network controller for a low-cost UAV

Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

Comparative Analysis of Multiple Neural Networks for Online Identification of a UAV

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