Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Villecco, Francesco; Pellegrino, Arcangelo

doi:10.3390/e19090475

Cited by 43 publications

(29 citation statements)

References 45 publications

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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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Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

2018

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“…Virtual reality represents an effective alternative solution [52]. The virtual model can be verified by the adding constraints and verifying functionality in interactive mode [53]. The design with NURBS cannot be represented with graphics hardware: it is necessary to convert CAD data into polygonal representation.…”

Section: Introductionmentioning

confidence: 99%

On the Evaluation of Errors in the Virtual Design of Mechanical Systems

Villecco

2018

Machines

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Abstract:In this article, the information value is used in numeric analysis as both a method for data approximation and a measure of data equality among a set of values. To this end, a surface segmentation, based on a study for constructing a hierarchy for vectors clustering using certain similarity criteria, is presented. The technique is based on the analysis of vectors representing regions associated with given types of critical points. An approach based on the Max Entropy in Metric Space (MEMS) is introduced in the paper, in order to extract a cluster of local features and to obtain an analysis of mechanical systems in the 2D and/or 3D spaces. The approach proposed in the paper can be effectively used in virtual prototyping and optimal designing of mechanical systems.

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“…One can, therefore, use the state vector z(t), as well as the generalized force vector u(t) for rewriting the differential set of dynamic equations and the algebraic measurement equations as follows: z(t) = A c z(t) + B c u(t), y(t) = Cz(t) + Du(t) (4) where A c denotes the state matrix represented in the continuous time domain, B c denotes the state influence matrix represented in the continuous time domain, C is referred to as the measurement influence matrix and D is called the direct transmission matrix. One can explicitly compute these state-space matrices starting from the system mechanical model represented in the configuration space in the following way:…”

Section: Representation Of the Dynamical Model In The Space Of Statesmentioning

confidence: 99%

“…A mathematical model of a dynamical system, therefore, allows for predicting the system dynamic behavior in response to a given set of external excitations [1]. The complexity of a dynamical model reflects the model dimension, its general flexibility and the capacity of representing the phenomena of interest in terms of mathematical laws [2][3][4][5]. The complexity of a given dynamical model is strongly correlated with the mathematical structure of the model and to the information content present in the observed dataset used in the model development.…”

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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Evaluation of Uncertainties in the Design Process of Complex Mechanical Systems

Cited by 43 publications

References 45 publications

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

Obstacle Avoidance System for Unmanned Ground Vehicles by Using Ultrasonic Sensors

On the Evaluation of Errors in the Virtual Design of Mechanical Systems

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

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