A Three-Mass System

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A smooth nonlinear system (2)

Model description: 

The system

$$\begin{align*} \dot{x}_1 & = x_4^2 + x_3^3 + u_1 + au_2 \\ \dot{x}_2 & = x_3 \\ \dot{x}_3 & = \sin{x_4}+\cos{x_1}+bu_1 + u_2 \\ \dot{x}_4 & = -x_4 \\ y_1 &= x_1 \\ y_2 &=x_2. \end{align*}$$

Type: 

Form: 

Model order: 

4

Time domain: 

Linearity: 

Publication details: 

TitleInput-output models for a class of nonlinear systems
Publication TypeConference Paper
Year of Publication1997
AuthorsAtassi, A.N., and Khalil H.K.
Conference NameProceedings of the 36th IEEE Conference on Decision and Control, 1997.
Date Published12/1997
PublisherIEEE
Conference LocationSan Diego, CA
ISBN Number0-7803-4187-2
Accession Number5863848
Keywordsnonlinear control systems
AbstractWe investigate the possibility of having an input-output model that has a specific structure for multivariable input-output linearizable systems
DOI10.1109/CDC.1997.657850

A smooth nonlinear system (1)

Model description: 

The system

$$\begin{align*} \dot{x}_1 & = x_2 \\ \dot{x}_2 & = x_3^2 + x_4 + u_1 + au_2 \\ \dot{x}_3 & = x_4 + bu_1 + u_2 \\ \dot{x}_4 & = -x_4 \\ y_1 &= x_1 \\ y_2 &=x_3 \end{align*}$$

with $ab \neq 1$, has a well-defined vector relative degree (2, 1) and a nonsingular decoupling matrix $\begin{bmatrix} 1 & a \\ b & 1 \end{bmatrix}$.

Type: 

Form: 

Model order: 

4

Time domain: 

Linearity: 

Publication details: 

TitleInput-output models for a class of nonlinear systems
Publication TypeConference Paper
Year of Publication1997
AuthorsAtassi, A.N., and Khalil H.K.
Conference NameProceedings of the 36th IEEE Conference on Decision and Control, 1997.
Date Published12/1997
PublisherIEEE
Conference LocationSan Diego, CA
ISBN Number0-7803-4187-2
Accession Number5863848
Keywordsnonlinear control systems
AbstractWe investigate the possibility of having an input-output model that has a specific structure for multivariable input-output linearizable systems
DOI10.1109/CDC.1997.657850

MAGLEV

Model description: 

The model of the MAGLEV system is unstable and nonlinear

$$ m\ddot{x}=mg-\dfrac{K_{c}V^{2}}{x^{2}}, $$

where $x$ is the metal ball position being the system output, $V$ is the system input as the voltage. Other parameters are $m$ as the mass of the metal ball, $K_c$ as constant for magnet circuit, and $g$ is the gravitational acceleration of 9.8 m/s$^2$. A free-body diagram is shown also in the attached image.

Type: 

Form: 

Model order: 

2

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleIdentification of a class of unstable processes
Publication TypeConference Paper
Year of Publication2009
AuthorsShahab, M., and Doraiswami R.
Conference Name5th IEEE GCC Conference & Exhibition, 2009.
Date Published03/2009
PublisherIEEE
Conference LocationKuwait City
ISBN Number978-1-4244-3885-3
Accession Number11875656
Keywordsconstraint theory, identification, least squares approximations, magnetic levitation, transfer functions
AbstractIdentification of a practical process, especially if unstable, is challenging as its model is generally stochastic and nonlinear. In this work we consider a class of unstable processes where the model is identified in a closed-loop operating regime. Important issues in identification are addressed, namely: identification scheme, the closed loop identification of unstable plants, choice of sampling period, and constraints on the estimated model parameters. Further the structure of the identified model may not be identical to that of the physical system due to noise artifacts, and inability to capture faster dynamics. Generally least-squares identification is employed to estimate the parameters of the system wherein all the coefficients of numerator and the denominator coefficients of system transfer function are estimated. In many practical system there are constraints on the model parameters. The identified coefficients using the conventional scheme may not obey the constraint. In this work a novel constrained least-squares identification scheme is proposed where in a priori known structural constraint is factored in parameter estimation. This scheme is evaluated on a physical magnetic lévitation system.
DOI10.1109/IEEEGCC.2009.5734284

Droop model

Model description: 

The behavior of phytoplankton cells in a continuous reactor is usually described by the Droop model. Cell growth is limited by a nutrient with concentration $S$. The biomass has a concentration $N$ and $Q$ represents the cell quota of assimilated nutrient, expressed as the amount of intracellular nutrient per biomass unit. The dilution rate $D$ corresponds to the flow rate of renewal medium over the volume of the reactor, and $D$ is the input of the system.

We denote $D = D_0 + u$, and the system fits

$$\sum_D \begin{cases} \dot{x}_i = f(x) + ug(x)\\ y=h(x_1) \end{cases}$$

with

$f(x)=\begin{pmatrix} a_2\left(1-\dfrac{1}{x_2}\right)x_1 - D_0x_1\\ a_3\dfrac{x_3}{a_1+x_3} - a_2(x_2 - 1)\\ D_0(1-x_3)-\dfrac{x_1x_3}{a_1+x_3} \end{pmatrix}$

$g(x)=\begin{pmatrix} -x_1\\ 0\\ 1-x_3 \end{pmatrix}$, and $h(x_1)=x_1$, where

$ x_1 = (\rho_m N/S_i);\\ x_2 = (Q/K_Q);\\ x_3 = (S/S_i);\\ a_1 = (K_{\rho}/S_i);\\ a_2 = \mu_m;\\ a_3 = (\rho_m/K_Q). $

Type: 

Form: 

Model order: 

3

Time domain: 

Linearity: 

Publication details: 

TitleNonlinear observers for a class of biological systems: application to validation of a phytoplanktonic growth model
Publication TypeJournal Article
Year of Publication1998
AuthorsBernard, O., Sallet G., and Sciandra A.
JournalIEEE Transactions on Automatic Control
Volume43
Start Page1056
Issue8
Pagination1056-1065
Date Published08/1998
ISSN0018-9286
Accession Number6002262
Keywordsbiocybernetics, living systems, nonlinear systems, observability, observers, physiological models
AbstractThe authors construct nonlinear observers in order to discuss the validity of biological models. They consider a class of systems including many classical models used in biological modeling. They formulate the nonlinear observers corresponding to these systems and prove the conditions necessary for their exponential convergence. They apply these observers on the well-known Droop model which describes the growth of a population of phytoplanktonic cells. The validity of this model is discussed based on the performance of the observers working on experimental data
DOI10.1109/9.704977

A Three-Mass System

Model description: 

The transfer function of the three-mass-system is much more complex than it is for one dominant elasticity (two-mass-system).

$${G_{\rm mech}}(s) = \underbrace{{\dfrac{1} {T_{ \Sigma} \cdot s}}}_{G_{\rm rs}(s)} \cdot \underbrace{ \dfrac{ a_{7} \cdot s^{4} + a_{6} \cdot s^{3}+a_{5} \cdot s^{2} + a_{4} \cdot s + 1}{a_{3} \cdot s^{4} + a_{2} \cdot s^{3}+ a_{1} \cdot s^{2} + a_{4} \cdot s + 1}} _{G_{\rm nrs}(s)}$$

with

$T_{\Sigma} = T_{\rm M} + T_{{\rm L}1} + T_{{\rm L}2}$

and

$\begin{align*} a_{1}&=d_{1}d_{2}T_{{\rm C}1}T_{{\rm C}2}+T_{{\rm L}2}\left(T_{\rm M}+T_{{ \rm L}1}\right) \cdot \frac{T_{C2}}{T_{\Sigma }}+T_{\rm M}\left(T_{{\rm L}1}+T_{{\rm L}2}\right) \cdot \frac{T_{{\rm C}1}}{T_{\Sigma}} \\ a_{2}&=\frac{T_{{\rm C}1}T_{{\rm C}2}}{T_{\Sigma}}\cdot\left(d_{1}T_{{\rm L}2}\left(T_{\rm M}+T_{{\rm L}1}\right)+d_{2}T_{\rm M} \left(T_{{\rm L}1}+T_{{\rm L}2}\right)\right) \\ a_{3}&=\frac{T_{\rm M}T_{{\rm L}1}T_{{\rm L}2}T_{{\rm C}1}T_{{\rm C}2}}{T_{\Sigma}} \\ a_{4}&=d_{1}T_{{\rm C}1}+d_{2}T_{{\rm C}2} \\ a_{5}&=d_{1}d_{2}T_{{\rm C}1}T_{{\rm C}2}+T_{{\rm L}2}T_{{\rm C}2}+\left(T_{{\rm L}1}+T_{{\rm L}2}\right)\cdot T_{{\rm C}1} \\ a_{6}&=\left(\left(d_{1}+d_{2}\right)T_{{\rm L}2}+d_{2}T_{{\rm L}1}\right)\cdot T_{{\rm C}1}T_{{\rm C}2} \\ a_{7}&=T_{{\rm L}1}T_{{\rm L}2}T_{{\rm C}1}T_{{\rm C}2}. \end{align*}$

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Publication details: 

TitleApplication of the Welch-Method for the Identification of Two- and Three-Mass-Systems
Publication TypeJournal Article
Year of Publication2008
AuthorsVillwock, S., and Pacas M.
JournalIEEE Transactions on Industrial Electronics
Volume55
Start Page457
Issue1
Pagination457-466
Date Published01/2008
ISSN0278-0046
Accession Number9756566
Keywordselectric drives, frequency response, identification, machine control, spectral analysis
AbstractThis paper deals with the measurement of the frequency response of the mechanical part of a drive for the parameter identification of a plant. The system is stimulated by pseudorandom binary signals. The measurement of the frequency response is part of a system identification procedure being carried out during an automatic commissioning of the drive. For the calculation of the frequency response of the mechanics, the Welch-method is applied for spectral analysis. The Welch-method is known from the fields of communications and measurement engineering. This paper addresses the application of this powerful method for the identification of electrical drives. Investigations have pointed out that the pure utilization of conventional identification strategies does not yield satisfying experimental results. Experimental results presented in this paper point out clearly the efficiency and flexibility of the proposed Welch-method. This paper contains many practical aspects and realization details that are important for their implementation on industrial systems. Although in principle, commercial software tools can be utilized for identifying the parameters of the plant, this paper addresses the implementation of the necessary identification algorithms on the embedded control electronics of the drives. The utilization of the Levenberg-Marquardt-algorithm yields excellent results for the identified parameters on the basis of the measured frequency response data.
DOI10.1109/TIE.2007.909753

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