Uncertainty Identification for a Nominal LPV Vehicle Model Based on Experimental Data

2006 ◽  
Author(s):  
G. Rodonyi ◽  
J. Bokor
Keyword(s):  
Experimental Data ◽  
Vehicle Model ◽  
Model Based

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

Modelling and Validation of Full Vehicle Model based on a Novel Multibody Formulation

2019 ◽  
Author(s):  
Alberto Parra ◽  
Dionisio Cagigas ◽  
Asier Zubizarreta ◽  
Antonio Joaquin Rodriguez ◽  
Pablo Prieto
Keyword(s):  
Vehicle Model ◽  
Full Vehicle ◽  
Model Based

scholarly journals Identification of a Generator Fault by Model-Based Diagnostic Techniques

2004 ◽  
Vol 10 (4) ◽  
pp. 293-300 ◽  
Author(s):  
P. Pennacchi ◽  
A. Vania
Keyword(s):  
Experimental Data ◽  
Power Plant ◽  
Diagnostic Techniques ◽  
Rotating Machines ◽  
Identification Methods ◽  
Model Based

Model-based diagnostic techniques can be used successfully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as noise in the signals and errors in the evaluation of the experimental vibrations that are caused only by the impending fault, can affect the accuracy of the fault identifications. This can make it difficult to identify the type of actual fault as well as to evaluate with care its severity and position. This article shows some techniques that have been developed by the authors to measure the accuracy of the results obtained with model-based identification methods aimed to diagnose faults in rotating machines. In this article, the results obtained by means of the analysis of experimental data collected in a power plant are described. Finally, the capabilities of the developed methods are shown and discussed.

scholarly journals Model-Based Optimization of Velocity Strategy for Lightweight Electric Racing Cars

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Mirosław Targosz ◽  
Wojciech Skarka ◽  
Piotr Przystałka
Keyword(s):  
Numerical Model ◽  
Energy Efficient ◽  
Atmospheric Conditions ◽  
Vehicle Model ◽  
Model Based ◽  
Race Track ◽  
Functional Features ◽  
Race Conditions ◽  
Vehicle Dynamic Model ◽  
And Control

The article presents a method for optimizing driving strategies aimed at minimizing energy consumption while driving. The method was developed for the needs of an electric powered racing vehicle built for the purposes of the Shell Eco-marathon (SEM), the most famous and largest race of energy efficient vehicles. Model-based optimization was used to determine the driving strategy. The numerical model was elaborated in Simulink environment, which includes both the electric vehicle model and the environment, i.e., the race track as well as the vehicle environment and the atmospheric conditions. The vehicle model itself includes vehicle dynamic model, numerical model describing issues concerning resistance of rolling tire, resistance of the propulsion system, aerodynamic phenomena, model of the electric motor, and control system. For the purpose of identifying design and functional features of individual subassemblies and components, numerical and stand tests were carried out. The model itself was tested on the research tracks to tune the model and determine the calculation parameters. The evolutionary algorithms, which are available in the MATLAB Global Optimization Toolbox, were used for optimization. In the race conditions, the model was verified during SEM races in Rotterdam where the race vehicle scored the result consistent with the results of simulation calculations. In the following years, the experience gathered by the team gave us the vice Championship in the SEM 2016 in London.

Host–Target Vehicle Model-Based Lateral State Estimation for Preceding Target Vehicles Considering Measurement Delay

2018 ◽  
Vol 14 (9) ◽  
pp. 4190-4199 ◽  
Author(s):  
Yafei Wang ◽  
Zhisong Zhou ◽  
Chongfeng Wei ◽  
Yahui Liu ◽  
Chengliang Yin
Keyword(s):  
State Estimation ◽  
Vehicle Model ◽  
Target Vehicle ◽  
Model Based ◽  
Measurement Delay ◽  
Host Target

scholarly journals Improving the accuracy of model-based quantitative nuclear magnetic resonance

2020 ◽  
Vol 1 (2) ◽  
pp. 141-153
Author(s):  
Yevgen Matviychuk ◽  
Ellen Steimers ◽  
Erik von Harbou ◽  
Daniel J. Holland
Keyword(s):  
Experimental Data ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Model Fitting ◽  
Adjustment Procedure ◽  
Peak Integration ◽  
Model Based ◽  
Numerous Model ◽  
Functional Forms ◽  
Nuclear Magnetic

Abstract. Low spectral resolution and extensive peak overlap are the common challenges that preclude quantitative analysis of nuclear magnetic resonance (NMR) data with the established peak integration method. While numerous model-based approaches overcome these obstacles and enable quantification, they intrinsically rely on rigid assumptions about functional forms for peaks, which are often insufficient to account for all unforeseen imperfections in experimental data. Indeed, even in spectra with well-separated peaks whose integration is possible, model-based methods often achieve suboptimal results, which in turn raises the question of their validity for more challenging datasets. We address this problem with a simple model adjustment procedure, which draws its inspiration directly from the peak integration approach that is almost invariant to lineshape deviations. Specifically, we assume that the number of mixture components along with their ideal spectral responses are known; we then aim to recover all useful signals left in the residual after model fitting and use it to adjust the intensity estimates of modelled peaks. We propose an alternative objective function, which we found particularly effective for correcting imperfect phasing of the data – a critical step in the processing pipeline. Application of our method to the analysis of experimental data shows the accuracy improvement of 20 %–40 % compared to the simple least-squares model fitting.

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