Nonlinear Models of Biological Systems (3)

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Mathematical Model for a Single-Link Flexible Joint Robot

Model description: 

The dynamical equations governing the behavior of a single-link flexible joint robot are traditionally obtained from Lagrangian dynamics considerations. Let $q$ denote the angular position of the link (see attached image) of half length $L$ and mass $m$ and let $q_m$ be the angular position of the motor. The differential equations governing the controlled motions are given by

$$\begin{align*} \tau &= D_m\ddot{q}_m + D_m\dot{q}_m + K_S(q_m-q) \\ 0 &= D\ddot{q} + B\dot{q} + mgL\sin{q}+K_S(q-q_m), \end{align*}$$

where $D$ denotes the inertia of the link, $D_m$ denotes the motor inertia; the flexible joint stiffness coefficient is $K_S$ and the motor viscous damping and the link viscous damping are $B_m$ and $B$, respectively. The gravitational acceleration is denoted by $g$.

Define $\rho^2 = 1 / K_S$, which is not to be taken as a small constant related to singular perturbation techniques. The state variables were defined as the motor's angular position $x_1 = q_m$, the corresponding angular velocity $x_2 = dq_m/dt$, the elastic force $x_3 = K_S(q - q_m)$ and $X_4 := (dq/dt- dq_m/dt)/\rho$. The state variable representation is then obtained as

$$\begin{align*} \dot{x}_1 &= x_2 \\ \dot{x}_2 &=-a_5x_2 + a_1x_3 + a_1u \\ \dot{x}_3 &= x_4/\rho \\ \dot{x}_4 &= [-a_2a_3\sin{\rho^2x_3 + x_1}-a_4x_3 - a_7x_2 - a_6\rho x_4 - a_1u]/\rho \end{align*}$$

with $a_1=1/D_m$, $a_2 = 1/D$, $a_3 = mgL$, $a_4=a_1+a_2$, $a_5 = B_m/D_m$, $a_6=B/D$, $a_7 = a_6-a_5$, $a=\tau.$

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TitleDynamical Feedback Control of Robotic Manipulators with Joint Flexibility
Publication TypeMagazine Article
Year of Publication1992
AuthorsSira-Ramirez, Hebertt, Ahmad Shaheen, and Zribi Mohamed
MagazineIEEE Transactions on Systems, Man and Cybernetics
Volume22
Issue Number4
Pagination736-747
Date Published06/1992
ISSN0018-9472
Accession Number4277471
Keywordsdifferential equations, dynamics, feedback, observability, position control, robots, stability, variable structure systems
AbstractDynamic feedback control strategies are proposed for the asymptotic stabilization and asymptotic output tracking problems, associated with the operation of flexible joint manipulators. Smooth dynamical linearizing feedback controllers, as well as dynamical sliding mode regulators, are derived within the context of M. Fliess's (1989) generalized observability canonical form (GOCF). The GOCF is obtained by means of a state elimination procedure, carried out on the system of differential equations describing the manipulator dynamics. The remarkable feature of this new approach lies in the fact that a truly effective smoothing of the sliding mode controlled responses is possible while substantially reducing the chattering in the control input torque. Simulation examples are given that illustrate the performance of the proposed controllers
DOI10.1109/21.156586

Three Heated Rooms

Model description: 

Consider three heated rooms depicted in the attached image. The first room can be heated directly by the input $u^1$, the heat transfer into the room. The other two rooms are heated via two boilers with the inputs $u^2$ and $u^3$ respectively. The temperature of each room is described by $x^1$, $x^2$, and $x^3$ respectively, the temperature of each boiler by $x^4$ and $x^5$. The heat emission of each room is considered as a nonlinear function of the room temperature. With $x = (x^1,\ldots, x^5)^{\mathrm T}$ the the nonlinear system has the form

$$\dot{x}=\begin{pmatrix} -c_0(x^2-T_0)-c_1(x^1-T_0)^2\\ -c_0(x^2-T_0)-c1(x^2-T_0)^2 + c_2(x^4-x^2)\\ -c_0(x^3-T_0)-c1(x^3-T_0)^2 + c_2(x^5-x^3)\\ -c_2(x^4-x^2)\\ -c_2(x^5-x^3) \end{pmatrix} + \begin{pmatrix} 1 & 0 & 0\\ 0 & 0 & 0\\ 0 & 0 & 0\\ 0 & 1 & 0\\ 0 & 0 & 1 \end{pmatrix} \begin{pmatrix} u^1\\ u^2\\ u^3 \end{pmatrix}, $$

where $c_0$, $c_1$, and $c_2$ are parameters for the heat transmission, and $T_0$ is the temperature outside of the rooms. We will choose our parameters so that the time is measured in hours and $x^i$ is measured in Kelvin. The state manifold is $\mathcal{M}= \mathbb{R}^5$, and the coupling conditions are

$y=h(x)=\begin{pmatrix}x^1-x^2\\x^1-x^3\end{pmatrix}=0,$

i.e. the temperature of the three rooms should be equal.

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5

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TitleAttractive Invariant Submanifold-based Coupling Controller Design
Publication TypeConference Paper
Year of Publication2011
AuthorsLabisch, Daniel, and Konigorski Ulrich
Conference Name2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) Orlando, FL, USA
Date Published12/2011
PublisherIEEE
Conference LocationOrlando, FL
ISBN Number978-1-61284-800-6
Accession Number12590357
Keywordscontrol system synthesis, multivariable control systems, nonlinear control systems
AbstractIn this paper, we provide an algorithm for the design of a coupling controller for a nonlinear input-affine system. The resulting controller renders the maximal locally controlled invariant output-nulling submanifold locally attractive for the controlled system. The connections to the constrained dynamics algorithm and the triangular decoupling problem are presented, and necessary and sufficient conditions for the success of the new algorithm are derived.
DOI10.1109/CDC.2011.6160421

Nonlinear benchmark system

Model description: 

$$\begin{align*} x_1(t+1) &=\left(\dfrac{x_1(t)}{1+x_1^2(t)}+1\right)\sin{x_2(t)} \\ x_2(t+1) &=x_2(t)\cos{x_2(t)}+x_1(t)e^{-((x_1^2(t)+x_2^2(t))/8} + \dfrac{u^3(t)}{1+u^2(t)+0.5\cos{x_1(t)+x_2(t)}} \\ y(t) &=\dfrac{x_1(t)}{1+0.5\sin{x_2(t)}}+\dfrac{x_2(t)}{1+0.5\sin{x_1(t)}}+e(t), \end{align*}$$

where $e(t)$ is the noise term, has a variance of 0.1.

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2

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TitleNonlinear system identification via direct weight optimization
Publication TypeJournal Article
Year of Publication2005
AuthorsRoll, Jacob, Nazin Alexander, and Ljung Lennart
JournalAutomatica
Volume41
Pagination475 - 490
Date Published01/2005
ISSN0005-1098
URLhttp://dx.doi.org/10.1016/j.automatica.2004.11.010

Nonlinear Models of Biological Systems (4)

Model description: 

The model we discuss here has been proposed to study glucose metabolism in the brain from positron emission tomography (PET) [$^{18}$F]-Fluoro-Deoxy-Glucose($^{18}$F-FDG) data K. Schmidt, G. Mies, and L. Sokoloff, “Model of kinetic behavior of deoxyglucose in heterogeneous tissues in brain: A reinterpretation of the significance of parameters fitted to homogeneous tissue models,” J. Cereb. Blood Flow Metab., vol. 11, pp. 10–24, 1991. The model is shown in Fig. 4. It is a two compartment model with two time-varying parameters which account for brain tissue heterogeneity.

The system-experiment model is

$$\begin{align*} \dot{x}_2(t) &= k_{21}u(t) - (k_{12} + k_{32}(t))x_2(t) \\ \dot{x}_3(t) &= k_{32}(t)x_2(t)\\ y_1(t) &= x_2(t) + x_3(t)\\ \end{align*}$$

System parameters are presented in the table below.

$x_1$ [$^{18}$F]FDG plasma concentration which acts as known input of the model;
$u(t) \equiv x_1(t),x_2 and x_3$ [$^{18}$F]FDG and [$^{18}$F]-Fluoro-Deoxy-Glucose-6-Phosphate concentrations in the brain tissue;
$y$ measured output;
$k_{21},k_{12}(t),k_{32}(t)$ unknown parameters with: \begin{align} k_{12}(t) = k_{12}(1 + \alpha\epsilon^{-\beta t}) \\ k_{32}(t) = k_{32}(1 + \alpha\epsilon^{-\beta t}). \end{align}

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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 (3)

Model description: 

This example deals with a model described in E. Walter, Identifiability of State Space Models. Berlin, Germany:Springer-Verlag, 1982. The model is shown in Fig. 3. All fluxes are assumed linear except some leaving compartment 3. In particular, parameters $k_{13}$, $k_{23}$, and $k_{43}$ are assumed to be parametrically linearly controlled by the arrival compartment, i.e. $k_{13}(x_1) = k_{13}x_1$, $k_{23}(x_2) = k_{23}x_2$ and $k_{43}(x_4) = k_{43}x_4$.

The system-experiment model is

$$\begin{align*} \dot{x}_1(t) &= - (k_{01} + k_{21})x_1(t) + k_{12}x_2(t) + k_{13}x_1(t)x_3(t)\\ \dot{x}_2(t) &= k_{21}x_1(t) - (k_{02} + k_{12} + k_{32} +k_{42})x_2(t) - k_{23}x_3(t)x_2(t) + k_{24}x_4(t)\\ \dot{x}_3(t) &= k_{32}x_2(t) - [k_{03} + k_{13}x_1(t) + k_{23}x_2(t) - k_{43}x_4(t)]x_3(t) + k_{34}x_4(t) + u(t)\\ \dot{x}_4(t) &= k_{43}x_2(t) + k_{43}x_3(t)x_4(t) - (k_{24} + k_{34})x_4(t)\\ y_1(t) &= x_1(t) \\ y_2(t) &= x_2(t) \end{align*}$$

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4

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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

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