scholarly journals Comparison of approaches to unknown parameters identification in gyro drift model

2018 ◽  
pp. 887-893
Author(s):  
D.P. Ivanov ◽  
Yu.A. Litvinenko ◽  
V.A. Tupysev
Keyword(s):  
Parameters Identification ◽  
Unknown Parameters ◽  
Drift Model ◽  
Gyro Drift

scholarly journals Full Order Observer With Unmatched Constraint: Unknown Parameters Identification

2019 ◽  
Vol 3 (4) ◽  
pp. 1026-1031
Author(s):  
M. Fouka ◽  
L. Nehaoua ◽  
H. Arioui ◽  
S. Mammar
Keyword(s):  
Parameters Identification ◽  
Unknown Parameters

scholarly journals Locomotor Development Prediction Based on Statistical Model Parameters Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-5
Author(s):  
A. V. Wildemann ◽  
A. A. Tashkinov ◽  
V. A. Bronnikov
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Parameters Identification ◽  
Model Parameters ◽  
Large Set ◽  
Constrained Optimization Problem ◽  
Unknown Parameters ◽  
Locomotor Development ◽  
Feasible Solutions ◽  
The Individual

This paper introduces an approach for parameters identification of a statistical predicting model with the use of the available individual data. Unknown parameters are separated into two groups: the ones specifying the average trend over large set of individuals and the ones describing the details of a concrete person. In order to calculate the vector of unknown parameters, a multidimensional constrained optimization problem is solved minimizing the discrepancy between real data and the model prediction over the set of feasible solutions. Both the individual retrospective data and factors influencing the individual dynamics are taken into account. The application of the method for predicting the movement of a patient with congenital motility disorders is considered.

scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

scholarly journals Parameters Identification and Synchronization of Chaotic Delayed Systems Containing Uncertainties and Time-Varying Delay

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Zhongkui Sun ◽  
Xiaoli Yang
Keyword(s):  
Dynamical Systems ◽  
Functional Differential Equations ◽  
Parameters Identification ◽  
Time Varying ◽  
Unknown Parameters ◽  
Delayed Systems ◽  
Adaptive Scheme ◽  
Time Varying Delay ◽  
Functional Differential ◽  
Varying Delay

Time delays are ubiquitous in real world and are often sources of complex behaviors of dynamical systems. This paper addresses the problem of parameters identification and synchronization of uncertain chaotic delayed systems subject to time-varying delay. Firstly, a novel and systematic adaptive scheme of synchronization is proposed for delayed dynamical systems containing uncertainties based on Razumikhin condition and extended invariance principle for functional differential equations. Then, the proposed adaptive scheme is used to estimate the unknown parameters of nonlinear delayed systems from time series, and a sufficient condition is given by virtue of this scheme. The delayed system under consideration is a very generic one that includes almost all well-known delayed systems (neural network, complex networks, etc.). Two classical examples are used to demonstrate the effectiveness of the proposed adaptive scheme.

Investigation on Temperature Control Model of the Focal Cooling Human Physiological System

2015 ◽  
Author(s):  
Kenyu Uehara ◽  
Yasumi Ukida ◽  
Takahiro Murakami ◽  
Koji Mori ◽  
Takashi Saito
Keyword(s):  
Temperature Control ◽  
Cooling System ◽  
Temperature Response ◽  
Input Voltage ◽  
Parameters Identification ◽  
Control Surface ◽  
Model Parameters ◽  
Cooling Device ◽  
Unknown Parameters ◽  
Focal Cooling

For efficient temperature control of the cooling device for medical purposes, accurate modeling of the focal cooling system taking into account the human physiological reaction and nonlinearity of the thermoerectric device, is required. In this paper, we examined about model parameters identification in order to establish a mathematical model for a focal cooling device for a living body using a Peltier device. Cooling experiments applied input constant voltage were performed to identify the model parameters. The temperature response data are obtained for every 0.1V, from 0.1V to 1.8V. As a result of the parameters identification, it was shown that some unknown parameters vary with a certain tendency to the input voltage. As a result of comparison between simulation value using identified parameters and experimental value, it was shown that one can simulate results in the error range of the parameter identification in the control surface.

Parameters Identification Strategy for a Generalized Vehicle Concept Model for Ride and Handling Analysis

2013 ◽  
Author(s):  
Mariano Carpinelli ◽  
Marco Gubitosa ◽  
Domenico Mundo ◽  
Wim Desmet
Keyword(s):  
Degrees Of Freedom ◽  
Early Stage ◽  
Parameters Identification ◽  
Least Square ◽  
Identification Algorithm ◽  
Vehicle Concept ◽  
Unknown Parameters ◽  
Multibody Model ◽  
Concept Model ◽  
Predecessor Model

In this paper we propose a structured approach for the parameters identification of a multibody vehicle concept model to be used for the combined analysis of vertical and longitudinal dynamics. The model here proposed adopts eight degrees of freedom in the space. The wheels are connected to the sprung mass in an equivalent trailing arm configuration thus enabling to reproduce the squat and dive phenomena. This conceptual suspension representation allows determining the dynamic response of the vehicle during longitudinal acceleration or braking maneuvers. The identification procedure here suggested evaluates the unknown parameters of the model, being the global stiffness and damping coefficients of the suspensions and the positions of the pivot points of the trailing arms. The identification algorithm is based on non-linear least square costs that can be computed by having as reference the signals of a measurement campaign which is conducted on a real vehicle as well as on a virtual predecessor model. The results here shown make use of virtually measured quantities coming from ride maneuvers performed by means of a high fidelity multibody model of a passenger car. The presented concept model, showing good correlation with respect to the reference signals, is suggested as a reliable prediction and optimization tool in the early stage of the design phase of new vehicles.

Fixed-time synchronization of multi-links complex network

2017 ◽  
Vol 31 (02) ◽  
pp. 1750008 ◽  
Author(s):  
Hui Zhao ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jinghua Xiao ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Complex Network ◽  
Finite Time ◽  
Time Synchronization ◽  
Impulsive Control ◽  
Initial Conditions ◽  
Fixed Time ◽  
Parameters Identification ◽  
Linear Matrix ◽  
Control Input ◽  
Unknown Parameters

In the paper, the fixed-time and finite-time synchronizations of multi-links complex network are investigated. Compared with finite-time synchronization, the settling time of fixed-time synchronization is independent of initial conditions. For uncertain multi-links complex networks, this paper further analyzes synchronization mechanism and unknown parameters based on the drive-response concept and finite-time stability theory. Novel synchronization control criteria and the result of parameters identification are, respectively, obtained in a finite time by utilizing Lyapunov function and linear matrix inequality (LMI). Besides, we give other two versions of finite-time synchronization and parameters identification for uncertain multi-links complex network with impulsive control input. Finally, numerical examples are given to illustrate the effectiveness of our theoretical results.

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