Three Compartment Model Describing the Dynamics of a Drug in a Tissue

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Coupled electric drives

Model description: 

This particular laboratory-scale process simulates the actual industrial problems in tension and speed controls as they occur in magnetic tape drives, textile machines, paper mills, strip metal production plants, etc. To simulate these problems. the coupled electric drives consists of two similar servo-motors which drive a jockey pulley via a continuous flexible belt (see attached image).The jockey pulley assembly constitutes a simulated work station. The basic control problem is to regulate the belt speed and tension by varying the two servo-motor torque.

The structure of the transfer function matrix model (numerator and denominator orders of each transfer function) have been determined from the theoretical modelling of the coupled electric drive system which has given

$$\begin{bmatrix} Y_1(s)\\ Y_2(s) \end{bmatrix} =G(s) \begin{bmatrix} U_1(s)\\ U_2(s) \end{bmatrix}$$

with

$ G(s) = \begin{bmatrix} \dfrac{b_{1,1,0}}{s^2+a_{11}s+a_{12}} & \dfrac{b_{1,2,0}}{s^2+a_{11}s+a_{12}}\\ \dfrac{b_{2,1,0}s+b_{2,1,1}}{s^3+a_{21}s^2+a_{22}s+a_{23}} & \dfrac{b_{2,2,0}}{s^3+a_{21}s^2+a_{22}s+a_{23}} \end{bmatrix} $

The inputs $U(s)$ to the system are the drive voltages to the servo-motor power amplifiers. The outputs $Y_1(s)$ and $Y_2(s)$ are the jockey pulley velocity and the belt tension respectively.

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TitleA new bias-compensating least-squares method for continuous-time MIMO system identification applied to a laboratory-scale process
Publication TypeConference Paper
Year of Publication1994
AuthorsGarnier, H., Sibille P., and Nguyen H.L.
Conference NameProceedings of the Third IEEE Conference on Control Applications, 1994.
Date Published08/1994
PublisherIEEE
Conference LocationGlasgow
ISBN Number0-7803-1872-2
Accession Number4880903
Keywordscontinuous time systems, identification, least squares approximations, MIMO systems, process control, stochastic processes, transfer function matrices
AbstractThis paper presents a new bias-compensating least-squares method for the identification of continuous-time transfer function matrix model of multi-input multi-output (MIMO) systems. The proposed method uses the generalised Poisson moment functional approach for handling time derivatives and is applied to the identification of a laboratory-scale process which simulates industrial material transport control problems. Model validation results show the potentiality of the proposed method in practical applications
DOI10.1109/CCA.1994.381459

A fourth-order heat exchanger process

Model description: 

The system under study consists of two sets of single shell heat exchangers filled with water, placed in parallel and cooled by a liquid saturated refrigerant flowing through a coil system, as it is illustrated in the attached image. The saturated vapour generated in the coil system is separated from the liquid phase in the stages $S1$ and $S2$, both of neglected volumes. This vapour, withdrawn in $S1$ and $S2$, reduces the refrigerant mass flow rate along the cooling system, and only the saturated liquid portion is used for cooling purposes. Table below provides the fluid properties and equipment dimensions. The temperature of the refrigerant remains constant at $T_C$ as the liquid is saturated, and the energy exchanged with water is used to vapourise a small portion of the refrigerant fluid. The idividual heat exchanger energy balances can be expressed in terms of deviation variables to define the following LTI system:

$$\begin{align*} A &= \begin{bmatrix} -\dfrac{1+\nu_A}{\tau_1} & \dfrac{1}{\tau_1} & 0 & 0\\ 0 & -\dfrac{1+\nu_A}{\tau_2} & 0 & 0 \\ 0 & 0 & -\dfrac{1+\nu_B}{\tau_3} & \dfrac{1}{\tau_3} \\ 0 & 0 & 0 & -\dfrac{1+\nu_B}{\tau_4} \\ \end{bmatrix} \\ B &= \begin{bmatrix} 0 & 0 & 0 & 0\\ \dfrac{k_1}{\tau_1} & 0 & \dfrac{k_3}{\tau_2} & 0\\ 0 & 0 & 0 & 0\\ 0 & \dfrac{k_2}{\tau_t} & 0 & \dfrac{k_4}{\tau_4}\\ \end{bmatrix}, C = \begin{bmatrix} \mu & 0 & \mu & 0\\ 0 & \mu & 0 & \mu\\ \mu & 0 & \mu & 0\\ 0 & \mu & \mu & 0\\ \end{bmatrix}, D = \begin{bmatrix} 0 \end{bmatrix}, \end{align*}$$

where: $\nu_A \triangleq (hA/C_p(\dot{m}_1 + \dot{m}_3)), \\ \nu_B \triangleq (hA/C_p(\dot{m}_2 + \dot{m}_4)),\\ \tau_1 \triangleq (M_1/(\dot{m}_1 + \dot{m}_3)),\\ \tau_2 \triangleq (M_2/(\dot{m}_1 + \dot{m}_3)),\\ \tau_3 \triangleq (M_3/(\dot{m}_2 + \dot{m}_4)),\\ \tau_4 \triangleq (M_4/(\dot{m}_4 + \dot{m}_3)),\\ k_1 \triangleq(\dot{m}_1/(\dot{m}_1 + \dot{m}_3)),\\ k_2 \triangleq(\dot{m}_2/(\dot{m}_2 + \dot{m}_4)),\\ k_3 \triangleq(\dot{m}_3/(\dot{m}_1 + \dot{m}_3)),\\ k_4 \triangleq(\dot{m}_4/(\dot{m}_1 + \dot{m}_3)),\\ \mu \triangleq(hA/h_{lv}).\\$

This system was discretised with a sampling time of $T = 1 s$ and was discretised while rounding the input and output delays to the closest integer-multiples of $T$.

$C_p$ 4.217 kJ/kg K water specific heat
$h_{lv}$ 850 kJ/kg refrigerator heat vapourisation
$T_S(0)$ 40$^{\circ}$C initial temperature in $E_s$
$T_{jin}(0)$ 40$^{\circ}$C initial water inlet temperature $j$
$T_c$ 40$^{\circ}$C refrigerant temperature
$\dot{m}_j$ 1 kg/s water mass flow $j$
$M_s$ 50 kg mass of water in $E_s$
$hA$ 8 kJ/kg overall surface heat transfer
$V_1$ 0.5 $\times$ 10$^{-3}$m$^4$ inlet water pipe volume 1
$V_2$ 2 $\times$ 10$^{-3}$m$^4$ inlet water pipe volume 2
$V_4$ 1.5 $\times$ 10$^{-3}$m$^4$ inlet water pipe volume 4
$\rho$ 1000 kg/m$^3$ water density

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4

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TitleDiscretisation of continuous-time dynamic multi-input multi-output systems with non-uniform delays
Publication TypeJournal Article
Year of Publication2011
AuthorsKassas, Z.M.
JournalControl Theory & Applications, IET
Volume5
Issue14
Start Page1637
Pagination1637-1647
Date Published09/2011
ISSN1751-8644
Accession Number12228423
Keywordscontinuous time systems, delays, discrete systems, MIMO systems
AbstractInput and output time delays in continuous-time (CT) dynamic systems impact such systems differently as their effects are encountered before and after the state dynamics. Given a fixed sampling time, input and output signals in multiple-input multiple-output (MIMO) systems may exhibit any combination of the following four cases: no delays, integer-multiple delays, fractional delays and integer-multiple plus fractional delays. A common pitfall in the digital control of delayed systems literature is to only consider the system timing diagram to derive the discrete-time (DT) equivalent model; hence, effectively `lump` the delays across the system as one total delay. DT equivalent models for systems with input delays are radically different than those with output delays. Existing discretisation techniques for delayed systems usually consider the delays to be integer-multiples of the sampling time. This study is intended to serve as a reference for systematically deriving DT equivalent models of MIMO systems exhibiting any combination of the four delay cases. This algorithm is applied towards discretising an MIMO heat exchanger process with non-uniform input and output delays. A significant improvement towards the CT response was noted when applying this algorithm as opposed to rounding the delays to the closest integer-multiple of the sampling time.
DOI10.1049/iet-cta.2010.0467

T-S fuzzy model

Model description: 

Consider a T-S fuzzy model

Plant Rule $i$: If $x_1(t)$ is $F_1^1(x_1(t))$

Then $x(t+1) = A_ix(t)+B_iu(t),$

where

$\begin{align*} A_1 &=\left[\matrix{-a & 2\cr -0.1 & b}\right], A_2=\left[\matrix{-a & 2\cr-0.1 & b }\right], A_3=\left[\matrix{-0.9 & 0.5\cr -0.1 & -1.7}\right] \\ B_1 &=\left[\matrix{b\cr 4}\right], B_2=\left[\matrix{b\cr 4.8}\right], B_3=\left[\matrix{3\cr 0.1}\right]. \end{align*}$

The parameters $a$ and $b$ are adjusted to compare the relaxation of stabilization conditions.

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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

A linear system

Model description: 

Consider a linear system represented by the transfer function

$$G(s)=\dfrac{c}{s(s+a)}$$

where $a$ and $c>0$ are unknowns constants, and the reference model

$$G_m(s)=\dfrac{\omega^2}{s^2 + 2\zeta\omega s + \omega^2}.$$

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2

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TitleAdaptive output feedback control of nonlinear systems represented by input-output models
Publication TypeJournal Article
Year of Publication1996
AuthorsKhalil, H.K.
JournalIEEE Transactions on Automatic Control
Volume41
Issue2
Start Page177
Pagination177-188
Date Published02/1996
ISSN0018-9286
Accession Number5202146
Keywordsadaptive control, linearisation techniques, nonlinear control systems, state feedback
AbstractWe consider a single-input-single-output nonlinear system which can be represented globally by an input-output model. The system is input-output linearizable by feedback and is required to satisfy a minimum phase condition. The nonlinearities are not required to satisfy any global growth condition. The model depends linearly on unknown parameters which belong to a known compact convex set. We design a semiglobal adaptive output feedback controller which ensures that the output of the system tracks any given reference signal which is bounded and has bounded derivatives up to the nth order, where n is the order of the system. The reference signal and its derivatives are assumed to belong to a known compact set. It is also assumed to be sufficiently rich to satisfy a persistence of excitation condition. The design process is simple. First we assume that the output and its derivatives are available for feedback and design the adaptive controller as a state feedback controller in appropriate coordinates. Then we saturate the controller outside a domain of interest and use a high-gain observer to estimate the derivatives of the output. We prove, via asymptotic analysis, that when the speed of the high-gain observer is sufficiently high, the adaptive output feedback controller recovers the performance achieved under the state feedback one
DOI10.1109/9.481517

Three Compartment Model Describing the Dynamics of a Drug in a Tissue

Model description: 

Consider the system

$$\begin{align*} \dot{x}_1 &= p_{13}x_3 + p_{12}x_2 - p_{21}x_1+u \\ \dot{x}_2 &= -p_{12}x_2 + p_{21}x_1 \\ \dot{x}_3 &= -p_{13}x_3 \\ y &= x_2, \end{align*}$$

where $x=[x_1, x_2, x_3]$ is the state vector, e.g. $x_1,x_2,x_3$ are drug masses in compartment 1, 2 and 3, respectively; initial conditions are $x_1(0)=0$, $x_2(0)=0$, $x_3(0)=0$; $u$ is the drug input; $y$ is the measured drug output; $p=[p_{12},p_{21},p_{13}]$ is the rate parameter vector (assumed constant).

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3

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TitleA new differential algebra algorithm to test identifiability of nonlinear systems with given initial conditions
Publication TypeConference Paper
Year of Publication2001
AuthorsSaccomani, M.P., Audoly S., Bellu G., and D'Angio L.
Conference NameProceedings of the 40th IEEE Conference on Decision and Control, 2001.
Date Published12/2001
PublisherIEEE
Conference LocationOrlando, FL
ISBN Number0-7803-7061-9
Accession Number7212178
Keywordsdifferential equations, identification, nonlinear systems, polynomials
AbstractA priori global identifiability is a fundamental prerequisite for model identification. It concerns uniqueness of the parametric structure of a dynamic model describing given input and output functions measured during an experiment. 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. The introduction of concepts of differential algebra and in particular the concept of characteristic set of a differential ideal introduced by Ritt (1950) have proven very useful tools in identifiability analysis. Yet the construction of an efficient algorithm still remains a difficult task. An improvement on existing algorithms has been published by some of the present the authors (Saccomani et al., 2000). Unfortunately this algorithm, like all other algorithms based on differential algebra, may run into difficulties for systems which are started at certain specific initial conditions. We propose a new version of the algorithm which gives the correct answer even if the system is started at special states from which the accessibility property is not guaranteed
DOI10.1109/.2001.980295

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