Isothermal Continuous Stirred Tank Reactor

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Nonlinear Models of Biological Systems (2)

Model description: 

This example deals with a model describing the control of insulin on glucose utilization in humans. The model is shown in Fig. 2. The experiment consists of an impulse input of glucose labeled with a tracer and of the measurement in plasma of glucose, labeled glucose and insulin concentrations. The measured insulin concentration acts as model input $u$, while the model output $y$ is the measured tracer glucose concentration. The control by insulin on the glucose system is exerted by insulin in a remote compartment $(x_3)$. The glucose system is described by two compartments which represent, respectively, glucose in rapidly $(x_1)$ and slowly equilibrating tissues $(x_2)$ which include the muscle tissues. Insulin control is exerted on glucose utilization in compartment 3 (insulin-dependent tissues) while glucose utilization in compartment 1 refers to insulin-independent tissues.

The system-experiment model is

$$\begin{align*} \dot{x}_1(t) &= - \left(k_p + \frac{F_{01}/V_1}{g(t)} + k_{21}\right)x_1(t) + k_{12}x_2(t)\\ \dot{x}_2(t) &= k_{21}x_1(t) - (k_{02} + x_3(t) + k_{12})x_2(t) \\ \dot{x}_3(t) &= -k_bx_3(t) + k_au(t)\\ y_1(t) &= x_1(t)/V \\ \end{align*}$$

The initial conditions are $x_1(0)=i_1,x_2(0)$, and $x_3(0)=0$. System parameters are presented in the table below.

$x_1,x_2,x_3$ glucose masses in compartments 1 and 2 and the concentration of insulin in a remote compartment 3, respectively;
$u$ plasma insulin concentration;
$g$ known plasma glucose concentration;
$y$ plasma tracer glucose concentration;
$V_1$ volume of the accessible compartment;
$F_{01}$ constant component of insulin-independent glucose uptake with $k_p$ accounting for the proportional component;
$k_a,k_b,k_{21},k_{12},k_{02}$ constant rate parameters

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3

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Publication details: 

TitleGlobal identifiability of nonlinear models of biological systems
Publication TypeJournal Article
Year of Publication2001
AuthorsAudoly, S., Bellu G., D'Angio L., Saccomani M.P., and Cobelli C.
JournalIEEE Transactions on Biomedical Engineering
Volume48
Pagination55-65
Date PublishedJan
ISSN0018-9294
Keywordsa priori global identifiability, algebra, algorithm, Algorithm design and analysis, Algorithms, Biological, Biological system modeling, biological system models, Biological systems, Biology computing, characteristic set, computer algebra techniques, differential algebra, Glucose, Humans, Insulin, Models, nonlinear dynamic models, Nonlinear dynamical systems, Nonlinear Dynamics, Nonlinear equations, nonlinear models, parameter estimation, Pharmacokinetics, physiological models, physiological systems, solution uniqueness, Testing, Time varying systems, time-varying parameters, zero initial conditions
AbstractA prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.
DOI10.1109/10.900248

Nonlinear Models of Biological Systems (1)

Model description: 

The two compartment model describes the kinetics of a drug in the human body. The drug is injected into the blood (compartment 1) where it exchanges linearly with the tissues (compartment 2); the drug is irreversibly removed with a nonlinear saturative characteristic from compartment 1 and with a linear one from compartment 2. The I/O experiment takes place in compartment 1.

The system-experiment model is

$$\begin{align*} \dot{x}_1(t) &= - \left(k_{21} + \frac{V_M}{K_m + x_1}\right)x_1(t) + k_{12}x_2(t) + b_1u(t) \\ \dot{x}_2(t) &= k_{21}x_1(t) - (k_{02} + k_{12})x_2(t) \\ y(t) &= c_1x_1(t) \\ \end{align*}$$

The initial conditions are $x_1(0) = 0$ and $x_2(0) = 0$. System parameters are presented in the table below.

$x_1$, $x_2$ drug masses in compartment 1 and 2, respectively;
$u$ drug input;
$y$ measured drug outtup;
$k_{12}$, $k_{21}$ and $k_{02}$ constant rate parameters;
$V_M$ and $K_m$ classical Michaelis-Mentel parameters;
$b_1$ and $c_1$ input and output parameters, respectively

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Model order: 

2

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Publication details: 

TitleGlobal identifiability of nonlinear models of biological systems
Publication TypeJournal Article
Year of Publication2001
AuthorsAudoly, S., Bellu G., D'Angio L., Saccomani M.P., and Cobelli C.
JournalIEEE Transactions on Biomedical Engineering
Volume48
Pagination55-65
Date PublishedJan
ISSN0018-9294
Keywordsa priori global identifiability, algebra, algorithm, Algorithm design and analysis, Algorithms, Biological, Biological system modeling, biological system models, Biological systems, Biology computing, characteristic set, computer algebra techniques, differential algebra, Glucose, Humans, Insulin, Models, nonlinear dynamic models, Nonlinear dynamical systems, Nonlinear Dynamics, Nonlinear equations, nonlinear models, parameter estimation, Pharmacokinetics, physiological models, physiological systems, solution uniqueness, Testing, Time varying systems, time-varying parameters, zero initial conditions
AbstractA prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global identifiability of nonlinear models of biological and physiological systems are presented.
DOI10.1109/10.900248

Ball and Plate System

Model description: 

The ball and plate system is a system, where a metal ball stays on a rigid square plate with each side length of 1m. The slope of the plate can be manipulated by two perpendicularly installed step motors, so that the tilting of the plate will make the ball moving.

$$\begin{align*} \begin{bmatrix} \dot{x}_1 \\ \dot{x}_2\\ \dot{x}_3\\ \dot{x}_4\\ \dot{x}_5\\ \dot{x}_6\\ \dot{x}_7\\ \dot{x}_8\\ \end{bmatrix} &= \begin{bmatrix} x_2 \\ B(x_1x_4^2 + x_4x_5x_8 - g \sin x_3)\\ x_4\\ 0\\ x_6\\ B(x_5x_8^2 + x_1x_4x_8 - g \sin x_7)\\ x_8\\ 0\\ \end{bmatrix} + \begin{bmatrix} 0 & 0\\ 0 & 0\\ 0 & 0\\ 1 & 0\\ 0 & 0\\ 0 & 0\\ 0 & 0\\ 0 & 1\\ \end{bmatrix} \begin{bmatrix} u_x\\ u_y\\ \end{bmatrix}, \\ Y &= h(X) = (x_1,x_5)^{\mathrm T}, \end{align*}$$

where $B= m/(m + J/R^2)$ and $X = (x_1; x_2; x_3; x_4; x_5; x_6; x_7; x_8)^{\mathrm T} = (x; \dot{x}; \theta_x; \dot{\theta}_x; y; \dot{y}; \theta_y; \dot{\theta}_y)^{\mathrm T}$

Parameters are presented in the table below.

Symbol Description Parameter value and unit
$m$ Mass of the ball $0.11$ $Kg$
$R$ Radius of the ball $0.02$ $m$
$S$ Dimension of the ball $1.0 \times 1.0$ $m^2$
$x$ Position of the ball in the $x$-axis $m$
$y$ Position of the ball in the $y$-axis $m$
$\dot{x}$ Velocity of the ball in the $x$-axis $m/s$
$\dot{y}$ Velocity of the ball in the $y$-axis $m/s$
$w$ Rolling angular velocity of the ball $Arc/s$
$\dot{r}$ Velocity of the ball, $\dot{r}^2 = x^2 + y^2$ $m/s$
$v_{max}$ Maximum velocity of the ball $4$ $mm/s$
$\tau_x$ Torque applied to the plate in the $x$-axis $Kg$ $m^2/s^2$
$\tau_y$ Torque applied to the plate in the $y$-axis $Kg$ $m^2/s^2$
$\theta_x$ Angle of the plate in the $x$-axis $Arc$
$\theta_y$ Angle of the plate in the $y$-axis $Arc$
$\dot{\theta}_x$ Angle velocity of the plate in the $x$-axis $Arc/s$
$\dot{\theta}_y$ Angle velocity of the plate in the $y$-axis $Arc/s$
$u_x$ Angle acceleration velocity of the plate from $x$-axis $Arc/s^2$
$u_y$ Angle acceleration velocity of the plate from $y$-axis $Arc/s^2$
$J_P$ Mass moment of inertia of the plate $0.5$ $Kg$ $m^2$
$J$ Mass moment of inertia of the ball $1.76e$ $-$ $5$ $Kg$ $m^2$
$g$ Acceleration due to gravity $9.8$ $m/s^2$

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8

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TitleTrajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme
Publication TypeJournal Article
Year of Publication2004
AuthorsFan, Xingzhe, Zhang Naiyao, and Teng Shujie
JournalFuzzy Sets and Systems
Volume144
Pagination297-312
Date PublishedJun
ISSN0165-0114
DOI10.1016/S0165-0114(03)00135-0

Isothermal Continuous Stirred Tank Reactor

Model description: 

$$\begin{align*} \dot x_1 &= -k_1x_1 - k_3x_1^2 + u(c - x_1) \\ \dot x_2 &= k_1x_1 - k_2x_2 - ux_2 \\ y &= x_2, \end{align*}$$

where $c$ and $x_1$ are the concentrations of the input and reactant substance, $x_2$ is the concentration of the desired output product, $u$ is the normalized input flow rate of the reactant substance. The output $y$ here reflects the grade of the final product. The parameters $k_1, k_2, k_3$ and $c$ are positive constants under the isothermal considered conditions.

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2

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Publication details: 

TitleFlatness-based optimal noncausal output transitions for constrained nonlinear systems: case study on an isothermal continuously stirred tank reactor
Publication TypeJournal Article
Year of Publication2005
AuthorsWang, G.L., and Allgower F.
JournalIEE Proceedings Control Theory and Applications
Volume152
Start Page105
Issue1
Pagination105–112
Date Published01/2005
ISSN1350-2379
Accession Number8307340
Keywordschemical reactors, control system synthesis, feedforward, nonlinear control systems, optimal control
Abstract

The issue of optimal output transition control for nonlinear differential flat systems with constraints is investigated. Of special interest is the generation of a state reference trajectory and a feedforward input, which are essential to the two-degree-of-freedom design. The proposed approach is to transfer the transition problem in the real output space into a trajectory planning issue in the flat output space. The distinguishing feature of our approach is the generation of a noncausal trajectory for the flat output. This approach is shown to be highly effective in creating performance improvements. It should also be noted that the proposed methodology guarantees that the planned trajectories are feasible for all nonlocal transitions. This allows the application of stable inversion in the planning of optimal output transitions. The proposed method is illustrated on a benchmark system, the isothermal continuous stirred tank reactor, although its applicability is much wider.

DOI10.1049/ip-cta:20041162

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