Multibody Model of a UAV in Presence of Wind Fields

Simone, Marco Claudio De; Russo, Serena; Rivera, Zandra B.; Guida, Domenico

doi:10.1109/iccairo.2017.26

Cited by 33 publications

(20 citation statements)

References 19 publications

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“…The research of the authors is finalized with the development of new methods for performing accurate analytic modeling [26][27][28][29][30], numerical parameter identification using experimental data [31][32][33][34][35], finite element analysis in the presence of dry friction [36][37][38][39][40][41][42][43][44][45] and control optimization for dynamic models of retrofitted mechanical systems [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. The main theories that describe the squeal phenomenon ascribe the increase of vibration amplitudes to the stick-slip mechanism or to the geometrical instability of the brake assembly.…”

Section: Discussionmentioning

confidence: 99%

Modal Coupling in Presence of Dry Friction

Simone

Guida

2018

Machines

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Abstract:In this paper, we analyze the behavior of a single pad system in the presence of dry friction. The goal is to investigate the path that leads a stable mechanical system to unstable behavior. In doing so, we studied the behavior of a discrete three DOF model, a continuous model and a finite element model of the pad. The numerical results are consistent with the experimental investigation conducted on a brake disk for railway application.

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Section: Discussionmentioning

confidence: 99%

Modal Coupling in Presence of Dry Friction

Simone

Guida

2018

Machines

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“…AΨ =ΨΛ (37) whereΛ indicates the identified eigenvalue matrix andΨ denotes the identified eigenvector matrix both represented in the discrete time domain. By converting the identified discrete-time state-space model to the identified continuous-time state-space model, the modal decomposition of the identified system state matrixÂ c leads to the identification of the diagonal matrixΛ c of dimensionn byn containing the system eigenvalues, which can be used for obtaining the system natural frequencies, as well as the damping ratios of the mechanical system.…”

Section: State-space System Identification Numerical Proceduresmentioning

confidence: 99%

“…On the other hand, in the frequency domain analysis, the experimental data are filtered and used by means of a Fourier transform, and subsequently, they are used in a system identification numerical procedure based on the frequency domain. In both cases, the analyst is interested in the calculation of the number of normal modes and natural frequencies necessary for constructing a reliable dynamic model of a given mechanical system without directly deriving the system equations of motion [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

Pappalardo

Guida

2018

Machines

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Abstract:The goal of this investigation is to construct a computational procedure for identifying the modal parameters of linear mechanical systems. The methodology employed in the paper is based on the Eigensystem Realization Algorithm implemented in conjunction with the Observer/Kalman Filter Identification method (ERA/OKID). This method represents an effective and efficient system identification numerical procedure based on the time domain. The algorithm developed in this work is tested by means of numerical experiments on a full-car vehicle model. To this end, the modal parameters necessary for the design of active and semi-active suspension systems are obtained for the vehicle system considered as an illustrative example. In order to analyze the performance of the methodology developed in this investigation, the system identification numerical procedure was tested considering two case studies, namely a full state measurement and an incomplete state measurement. As expected, the numerical results found for the identified dynamical model showed a good agreement with the modal parameters of the mechanical system model. Furthermore, numerical results demonstrated that the proposed method has good performance considering a scenario in which the signal-to-noise ratio of the input and output measurements is relatively high. The method developed in this paper can be effectively used for solving important engineering problems such as the design of control systems for road vehicles.

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“…Furthermore, the use of low-cost electronic components does not affect the possibility to obtain good models by means of identification techniques [20][21][22][23][24]. The identification of models and very detailed multi-body models, created by using 3D CAD models [25][26][27][28][29][30][31], are the starting point for designing optimal control laws [32][33][34][35][36][37][38][39][40][41][42]. Most multi-body simulation software considers systems made up of rigid bodies, but it is possible to study the behavior of flexible multi-body systems by using ANCFtechniques [43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

2018

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Artificial intelligence is the ability of a computer to perform the functions and reasoning typical of the human mind. In its purely informatic aspect, it includes the theory and techniques for the development of algorithms that allow machines to show an intelligent ability and/or perform an intelligent activity, at least in specific areas. In particular, there are automatic learning algorithms based on the same mechanisms that are thought to be the basis of all the cognitive processes developed by the human brain. Such a powerful tool has already started to produce a new class of self-driving vehicles. With the projections of population growth that will increase until the year 2100 up to 11.2 billion, research on innovating agricultural techniques must be continued. In order to improve the efficiency regarding precision agriculture, the use of autonomous agricultural machines must become an important issue. For this reason, it was decided to test the use of the "Neural Network Toolbox" tool already present in MATLAB to design an artificial neural network with supervised learning suitable for classification and pattern recognition by using data collected by an ultrasonic sensor. The idea is to use such a protocol to retrofit kits for agricultural machines already present on the market.

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Multibody Model of a UAV in Presence of Wind Fields

Cited by 33 publications

References 19 publications

Modal Coupling in Presence of Dry Friction

Modal Coupling in Presence of Dry Friction

System Identification Algorithm for Computing the Modal Parameters of Linear Mechanical Systems

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

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