Mathematical Modeling and Experimental Identification of an Unmanned Helicopter Robot with Flybar Dynamics

Kim, S. K.; Tilbury, Dawn M.

doi:10.1002/rob.20002

Cited by 92 publications

(48 citation statements)

References 12 publications

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“…It requires a measured output, a known input and system model, and the assumed process and measurement noise statistics. The goal is to characterize the ability of an EKF to identify the unknown parameters of Equation (18). To simplify analysis, the parameters 14 and kk in Equation (18) are set to zero.…”

Section: Extended Kalman Filter Methodsmentioning

confidence: 99%

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Minimal Models to Capture the Dynamics of a Rotary Unmanned Aerial Vehicle

Choi

Hann

Chen

2013

J Intell Robot Syst

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This paper presents a method for characterising the primary dynamics of a rotary unmanned aerial vehicle. Based on first principles and basic aerodynamics, a mathematical model which explains the rigid body dynamics of a model-scale helicopter is developed. This model is reduced to three simplified decoupled models of attitude dynamics. Empirical test data is collected from a field experiment with significant wind disturbances. The method worked accurately on both uncoupled and fully coupled attitude models. An integral based parameter identification method is presented to identify the unknown intrinsic helicopter parameters as well as model of wind disturbance. An extended Kalman filter system identification method and common nonlinear regression are used for comparison. The EKF was found to be highly dependent on the initial states, so is not suitable for this application which contains significant disturbance and modelling errors. Nonlinear regression proved to be sufficiently accurate but computationally expensive. The proposed integral based parameter identification method was shown to be fast and accurate and is well suited to this application.

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Section: Extended Kalman Filter Methodsmentioning

confidence: 99%

“…The Flybar flapping angle derivation starts from defining dynamics of flybar in the Euler equation [18]:…”

Section: Modelling Structurementioning

confidence: 99%

Minimal Models to Capture the Dynamics of a Rotary Unmanned Aerial Vehicle

Choi

Hann

Chen

2013

J Intell Robot Syst

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“…X-29 [3], X-31A [4], Eurofighter [5], as well as unmanned aerial vehicles e.g. helicopter-based Yamaha R-50 [6], Ikarus ECO [7] or even more complex aircrafts that recently catch a great deal of interest, such as Quadcopters [8] and Cyclocopters [9].…”

Section: Introductionmentioning

confidence: 99%

Output Error Method for Tiltrotor Unstable in Hover

Lichota¹,

Szulczyk²

2017

Archive of Mechanical Engineering

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This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

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“…Miniature helicopter models are characterized by strongly coupled, multi-varieties and time delay nonlinear systems [4], therefore the design of an elaborated control system for autonomous flight is a challenging task. Flight control system design is considered one of the core issues in the development of a fully functional unmanned helicopter, many control system designs have been developed for controlling the dynamic process of miniature helicopter; few among those designs are readily suitable for applying [5].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Attitude Control System for a Mini Helicopter Expanded Non Linear Mathematical Model

Al-Shehabi¹

2016

JAEST

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Full nonlinear mathematical model for a mini helicopter is very complex, inflexible and has big influence on the designed control system performance. Analytical approach for autonomous helicopter modeling including both swash plate and mixer dynamics additional to miniature helicopter equations of motion is introduced. Attitude control system without linearization process is directly designed using fuzzy logic concepts, fuzzy controllers are implemented in both stability and command augmentation loops. Scaling factors are adjusted to minimize the error. The performance of control system for miniature helicopter expanded nonlinear model using fuzzy logic controllers is evaluated through simulation.

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Mathematical Modeling and Experimental Identification of an Unmanned Helicopter Robot with Flybar Dynamics

Cited by 92 publications

References 12 publications

Minimal Models to Capture the Dynamics of a Rotary Unmanned Aerial Vehicle

Minimal Models to Capture the Dynamics of a Rotary Unmanned Aerial Vehicle

Output Error Method for Tiltrotor Unstable in Hover

Fuzzy Logic Attitude Control System for a Mini Helicopter Expanded Non Linear Mathematical Model

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