Active Front Steering (Part 1): Mathematical Modeling and Parameter Estimation

2004 ◽  
Author(s):  
Willy Klier ◽  
Wolfgang Reinelt
Keyword(s):  
Mathematical Modeling ◽  
Parameter Estimation ◽  
Active Front Steering

Composite Nonlinear Feedback Control with Multi-objective Particle Swarm Optimization for Active Front Steering System

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Liyana Ramli ◽  
Yahaya Md. Sam ◽  
Zaharuddin Mohamed ◽  
M. Khairi Aripin ◽  
M. Fahezal Ismail
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Nonlinear Feedback ◽  
Nonlinear Gain ◽  
Composite Nonlinear Feedback ◽  
Swarm Optimization ◽  
Vehicle Handling ◽  
Multi Objective ◽  
Yaw Rate ◽  
Active Front Steering

The purpose of controlling the vehicle handling is to ensure that the vehicle is in a safe condition and following its desire path. Vehicle yaw rate is controlled in order to achieve a good vehicle handling. In this paper, the optimal Composite Nonlinear Feedback (CNF) control technique is proposed for an Active Front Steering (AFS) system for improving the vehicle yaw rate response. The model used in order to validate the performance of controller is nonlinear vehicle model with 7 degree-of-freedom (DOF) and a bicycle model is implemented for the purpose of designing the controller. In designing an optimal CNF controller, the parameter estimation of linear and nonlinear gain becomes very important to produce the best output response. An intelligent algorithm is designed to minimize the time consumed to get the best parameter. To design an optimal method, Multi Objective Particle Swarm Optimization (MOPSO) is utilized to optimize the CNF controller performance. As a result, transient performance of the yaw rate has improved with the increased speed of in tracking and searching of the best optimized parameter estimation for the linear and the nonlinear gain of CNF controller.  

scholarly journals Parameter estimation in mathematical models of viral infections using R

2017 ◽  
Author(s):  
Van Kinh Nguyen ◽  
Esteban A. Hernandez-Vargas
Keyword(s):  
Mathematical Modeling ◽  
Parameter Estimation ◽  
Mathematical Models ◽  
Viral Infections ◽  
Free Software ◽  
Virus Dynamics ◽  
Biological Applications ◽  
Simulation Results ◽  
Underlying Mechanisms ◽  
Mathematical Relations

AbstractIn recent years, mathematical modeling approaches have played a central role to understand and to quantify mechanisms in different viral infectious diseases. In this approach, biological-based hypotheses are expressed via mathematical relations and then tested based on empirical data. The simulation results can be used to either identify underlying mechanisms, provide predictions on infection outcomes, or evaluate the efficacy of a treatment.Conducting parameter estimation for mathematical models is not an easy task. Here we detail an approach to conduct parameter estimation and to evaluate the results using the free software R. The method is applicable to influenza virus dynamics at different complexity levels, widening experimentalists capabilities in understanding their data. The parameter estimation approach presented here can be also applied to other viral infections or biological applications.

scholarly journals Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm

2019 ◽  
Vol 20 (1) ◽  
pp. 149 ◽  
Author(s):  
Marcia De Fatima Brondani Binelo ◽  
Airam Teresa Zago Romcy Sausen ◽  
Paulo Sérgio Sausen ◽  
Manuel Osório Binelo
Keyword(s):  
Mathematical Modeling ◽  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Visual Analysis ◽  
Lithium Ion ◽  
Electrical Model ◽  
Pulsed Discharge ◽  
Battery Lifetime ◽  
Agile Process ◽  
Discharge Curves

In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is used for the mathematical modeling of the Lithium-ion Polymer batteries lifetime. The model is also parametrized using the conventional procedures, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and also is a more agile process than the conventional one, been less subject to subjective aspects.

scholarly journals Mathematical modeling and parameter estimation of levodopa motor response in patients with parkinson disease

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229729 ◽  
Author(s):  
Mauro Ursino ◽  
Elisa Magosso ◽  
Giovanna Lopane ◽  
Giovanna Calandra-Buonaura ◽  
Pietro Cortelli ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Parameter Estimation ◽  
Parkinson Disease ◽  
Motor Response
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