Droop model

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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

Recurrent Trainable Neural Network

Model description: 

The RTNN model is described bythe following equations:

$$\begin{align*} X(k+1) &= JX(k) + BU(k)\\ Z(k) &= S[X(k)]\\ Y(k) &= S[CZ(k)]\\ J &\doteq \mathrm{blockdiag}(J_i); |J_i| <1, \end{align*}$$

here $X(\cdot)$ is a $n$-state vector of the RTTN; $U(\cdot)$ is a $m$-input vector; $Y(\cdot)$ is a $l$-output vector; $Z(\cdot)$ is an auxiliary vector variable with $l$ dimension; $S(\cdot)$ is a vector-valued smooth activation function (sigmoid, $tanh$, saturation) with appropriate dimensions; $J$ is a weigh-state block-diagonal matrix with $(1 \times 1)$ and $(2 \times 2)$ blocks; $J_i$ is an $i-th$ block of $J$ and $|J_i|<1$ is a stability condition.

Type: 

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Publication details: 

TitleAdaptive Neural Control of Nonlinear Systems
Publication TypeConference Paper
Year of Publication2001
AuthorsGarrido, Ruben
EditorBaruch, Ieroham, Flores Jose Martin, and Thomas Federico
Conference NameInternational Conference on Artificial Neural Networks - ICANN 2001
Date Published08/2001
PublisherSpringer
Conference LocationVienna, Austria
ISBN Number3-540-42486-5
URLhttp://dblp.uni-trier.de/rec/bib/conf/icann/2001

A Truck-Trailer System

Model description: 

Consider a truck-trailer system depicted in the attached image. Its dynamics is described by

$$\begin{align*} x_{1}(t+1) &=\left(1-\frac{vT}{L}\right)x_{1}(t)+\frac{vT}{l}u(t) \\ x_{2}(t+1) &=\frac{vT}{L}x_1(t)+x_{2}(t) \\ x_{3}(t+1) &=x_{3}(t)+vT\sin\left(\frac{vT}{2L}x_{1}(t)+x_{2}(t)\right)x_{1}(t), \end{align*}$$

where $x_1(t)$ : angle difference between truck and trailer. $x_2(t)$ : angle of trailer. $x_3(t)$ : vertical position of rear of trailer, $u(t)$ : steering angle, $T$ : sampling time. In this example, the parameters are $T=2.0s$, $l=2.8m$, $L=5.5m$, $v=-1.0m/s$.

Type: 

Form: 

Model order: 

3

Time domain: 

Linearity: 

Attachment: 

Publication details: 

TitleStabilization of discrete-time nonlinear control systems - Multiple fuzzy Lyapunov function approach
Publication TypeConference Paper
Year of Publication2009
AuthorsKau, Shih-Wei, Huang Xin-Yuan, Shiu Sheng-Yu, and Fang Chun-Hsiung
Conference NameInternational Conference on Information and Automation, 2009. ICIA '09.
Date Published06/2009
PublisherIEEE
Conference LocationZhuhai, Macau
ISBN Number978-1-4244-3607-1
Accession Number10837484
Keywordsdiscrete time systems, fuzzy control, linear matrix inequalities, Lyapunov methods, nonlinear control systems, stability
AbstractThis paper deals with the stabilization problem for discrete-time nonlinear systems that are represented by the Takagi - Sugeno fuzzy model. By the multiple fuzzy Lyapunov function and the three-index algebraic combination technique, a new stabilization condition is developed. The condition is expressed in the form of linear matrix inequalities (LMIs) and proved to be less conservative than existing results in the literature. Finally, a truck-trailer system is given to illustrate the novelty of the proposed approach.
DOI10.1109/ICINFA.2009.5204890

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

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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
Issue8
Start Page1056
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

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