scholarly journals Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

2021 ◽  
pp. 1-10
Author(s):  
Aaron Kandel ◽  
Mohamed Wahba ◽  
Hosam Fathy
Keyword(s):  
Vehicle Dynamics ◽  
Relevant Literature ◽  
Experimental Designs ◽  
Estimation Accuracy ◽  
Active Safety ◽  
Identifiability Analysis ◽  
Fundamental Limits ◽  
Parameter Identifiability ◽  
Road Grade ◽  
Vehicle Chassis

Abstract This paper investigates the theoretical Cram´er-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.

Combined State and Parameter Identifiability for a Model of Drug-Resistant Cancer Dynamics

2021 ◽  
Author(s):  
Mahsa Doosthosseini ◽  
Hosam Fathy
Keyword(s):  
Drug Administration ◽  
Estimation Accuracy ◽  
Drug Resistant ◽  
State Variables ◽  
Identifiability Analysis ◽  
Parameter Identifiability ◽  
Resistant Population ◽  
The Impact ◽  
Sensitive Population ◽  
Administration Rate

Abstract This article analyzes the combined parameter and state identifiability for a model of a cancerous tumor's growth dynamics. The model describes the impact of drug administration on the growth of two populations of cancer cells: a drug-sensitive population and a drug-resistant population. The model's dynamic behavior depends on the underlying values of its state variables and parameters, including the initial sizes and growth rates of the drug sensitive and drug-resistant populations, respectively. The article's primary goal is to use Fisher identifiability analysis to derive and analyze the Cram´er-Rao theoretical bounds on the best-achievable accuracy with which this estimation can be performed locally. This extends previous work by the authors, which focused solely on state estimation accuracy. This analysis highlights two key scenarios where estimation accuracy is particularly poor. First, a critical drug administration rate exists where the model's state observability is lost, thereby making the independent estimation of the drug-sensitive and drug-resistant population sizes impossible. Second, a different critical drug administration rate exists that brings the overall growth rate of the drug-sensitive population to zero, thereby worsening model parameter identifiability.

Vehicle Dynamics Control Based on Linear Quadratic Regulator

2009 ◽  
Vol 16-19 ◽  
pp. 876-880
Author(s):  
Si Qi Zhang ◽  
Tian Xia Zhang ◽  
Shu Wen Zhou
Keyword(s):  
Control Strategy ◽  
Vehicle Dynamics ◽  
Linear Quadratic Regulator ◽  
Active Safety ◽  
Vehicle Stability ◽  
Steering Control ◽  
Linear Quadratic ◽  
Yaw Rate ◽  
Vehicle Dynamics Control ◽  
Yaw Moment

The paper presents a vehicle dynamics control strategy devoted to prevent vehicles from spinning and drifting out. With vehicle dynamics control system, counter braking are applied at individual wheels as needed to generate an additional yaw moment until steering control and vehicle stability were regained. The Linear Quadratic Regulator (LQR) theory was designed to produce demanded yaw moment according to the error between the measured yaw rate and desired yaw rate. The results indicate the proposed system can significantly improve vehicle stability for active safety.

Estimating Road Angles With the Knowledge of the Vehicle Yaw Angle

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Ling-Yuan Hsu ◽  
Tsung-Lin Chen
Keyword(s):  
Vehicle Dynamics ◽  
Longitudinal Velocity ◽  
Lateral Acceleration ◽  
Vehicle Body ◽  
Estimation Accuracy ◽  
The Road ◽  
Displacement Sensors ◽  
State Observers ◽  
Acceleration Sensors ◽  
Yaw Angle

This paper presents a method of estimating road angles using state observers and three types of sensors (lateral acceleration sensors, longitudinal velocity sensors, and suspension displacement sensors). The proposed method differs from those in most existing literature in three aspects. First, a “full-state” vehicle model is used to describe nonlinear vehicle dynamics on a sloped road. Second, “switching observer” techniques are used to suggest suitable sensors and to construct state observers. Lastly, the road angles are described by three Euler angles, and two of them are estimated simultaneously. The analysis indicates that (1) road angles affect vehicle dynamics through components of the gravitational force acting on the vehicle body. These gravitational forces can be correctly estimated with an estimation accuracy less than 7.5%, even when road angles vary with time. (2) Those road angles can be correctly estimated only when the vehicle yaw angle is known.

scholarly journals Identifiability analysis for models of the translation kinetics after mRNA transfection

2021 ◽  
Author(s):  
Susanne Pieschner ◽  
Jan Hasenauer ◽  
Christiane Fuchs
Keyword(s):  
Differential Equation ◽  
Experimental Data ◽  
Ode Model ◽  
Kinetic Rate ◽  
Identifiability Analysis ◽  
Parameter Identifiability ◽  
Ode Models ◽  
Kinetic Rate Constants ◽  
Mrna Transfection ◽  
Model Equations

Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.

scholarly journals Identifiability analysis for stochastic differential equation models in systems biology

2020 ◽  
Author(s):  
Alexander P Browning ◽  
David J Warne ◽  
Kevin Burrage ◽  
Ruth E Baker ◽  
Matthew J Simpson
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Synthetic Data ◽  
Mcmc Methods ◽  
Parameter Estimates ◽  
Practical Case ◽  
Identifiability Analysis ◽  
Parameter Identifiability ◽  
Ode Models ◽  
Reliable Parameter

AbstractMathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly-adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically-motivated synthetic data and Markov-chain Monte Carlo (MCMC) methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.

The attack and defense mechanisms: Perspectives from behavioral economics and game theory

2019 ◽  
Vol 42 ◽  
Author(s):  
Subhasish M. Chowdhury
Keyword(s):  
Game Theory ◽  
Behavioral Economics ◽  
Defense Mechanisms ◽  
Relevant Literature ◽  
Experimental Designs ◽  
Neuroscience Research ◽  
Attack And Defense ◽  
Economics Research

Abstract This commentary complements the article by De Dreu and Gross (2019) from the perspectives of behavioral economics and game theory. It aims to provide a bridge between psychology/neuroscience research and economics research in attack-and-defense by stipulating relevant literature, clarifying theoretical structures, and suggesting improvements in experimental designs and possible further investigations.

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