Estimation of the Order and Parameters of a Fractional Order Model From a Noisy Step Response Data1

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Mahsan Tavakoli-Kakhki ◽  
Mohammad Saleh Tavazoei
Keyword(s):  
Numerical Simulation ◽  
Fractional Order ◽  
Closed Loop ◽  
Open Loop ◽  
Measurement Noise ◽  
Step Response ◽  
Order Model ◽  
Response Data ◽  
Fractional Order Model ◽  
Simulation Results

This paper deals with integral based methods to estimate the order and parameters of simple fractional order models from the extracted noisy step response data of a process. This data can be obtained from both open-loop and closed-loop tests. Numerical simulation results are presented to verify the robustness of these proposed methods in the presence of the measurement noise.

DYNAMICAL ANALYSIS OF FRACTIONAL ORDER ZIKV MODEL

2021 ◽  
Vol 10 (5) ◽  
pp. 2469-2481
Author(s):  
N.A. Hidayati ◽  
A. Suryanto ◽  
W.M. Kusumawinahyu
Keyword(s):  
Numerical Simulation ◽  
Equilibrium Point ◽  
Fractional Order ◽  
Equilibrium Points ◽  
Dynamical Analysis ◽  
Stability Conditions ◽  
Order Model ◽  
Fractional Order Model ◽  
The Stability ◽  
S Model

The ZIKV model presented in this article is developed by modifying \cite{Bonyah2016}’s model. The classical order is changed into fractional order model. The equilibrium points of the model are determined and the stability conditions of each equilibrium point have been done using Routh-Hurwitz conditions. Numerical simulation is presented to verify the result of stability analysis result. Numerical simulation is also used to shows the effect of the order $\alpha$ to the stability of the model’s equilibrium point.

Controller design for a fractional-order nonlinear glucose-insulin system using feedback linearization

2020 ◽  
Vol 42 (13) ◽  
pp. 2372-2381 ◽  
Author(s):  
Muhammad Waleed Khan ◽  
Muhammad Abid ◽  
Abdul Qayyum Khan ◽  
Ghulam Mustafa
Keyword(s):  
Nonlinear Systems ◽  
Fractional Order ◽  
Human Body ◽  
Feedback Linearization ◽  
Controller Design ◽  
Artificial Pancreas ◽  
Glucose Regulation ◽  
Order Model ◽  
Fractional Order Model ◽  
Simulation Results

In this paper, the system of glucose regulation in a human body is discussed. Nonlinear Bergman’s minimal model representing this system is taken, and converted to fractional-order model using the Caputo definition. After that, method of feedback linearization is put forward for fractional-order nonlinear systems, and then applied to design observer based controller for an artificial pancreas for a patient with diabetes. Using FOTF toolbox in MATLAB, the designed controller, observer, and the system are simulated. Using simulation results, it is shown that the designed controller and observer are stable, and the desired level of glucose concentration is being tracked faithfully.

Fractional Order Model Identification for a Billet Unloading Robotic Arm

2016 ◽  
Vol 841 ◽  
pp. 234-239 ◽  
Author(s):  
Iulia Clitan ◽  
Vlad Mureşan ◽  
Andrei Florin Clitan ◽  
Mihail Abrudean
Keyword(s):  
Experimental Data ◽  
Fractional Order ◽  
Model Identification ◽  
Step Response ◽  
Robotic Arm ◽  
Positioning System ◽  
Order Model ◽  
Integer Order ◽  
Fractional Order Model ◽  
The Mean

This paper presents a fractional model identification for a billet unloading robotic arm’s positioning system. First, an integer order model is obtained using a graphical identification method based on a set of experimental data. The experimental data represents the robotic arm’s position, measured using an encoder, at a constant billet displacement. The integer order model was obtained based on the overall performances of the measured robotic arm’s step response. The mean square error between the measured data and the model step response is high, thus, in order to decrease the error and to obtain a more accurate model, a fractional order model is determined using an iterative procedure.

Data-driven I-PD Gain Tuning using Closed-loop Step Response Data

2019 ◽  
Vol 139 (8) ◽  
pp. 882-888
Author(s):  
Shiro Masuda ◽  
Jongho Park ◽  
Yoshihiro Matsui
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Step Response ◽  
Response Data
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