Model of Phytoplanktonic Cell Growth

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Induction Motor

Model description: 

Induction motor is represented by fifth order nonlinear differential equation as

$$\begin{align*} \dot{i}_{sa} &={MR_{r} \over \sigma L_{s}L_{r}^{2}}\phi_{ra}+{n_{p} M\over \sigma L_{s}L_{r}} \omega\phi_{rb}- \gamma i_{sa}+{1\over \sigma L_{s}}u_{sa} \\ \dot{i}_{sb} &={MR_{r} \over \sigma L_{s}L_{r}^{2}}\phi_{rb}- {n_{p} M \over \sigma L_{s}L_{r}}\omega\phi_{ra}-\gamma i_{sb}+{1\over \sigma L_{s}}u_{sb} \\ \dot{\phi}_{ra} &=-{R_{r}\over L_{r}}\phi_{ra}-n_{p}\omega \phi_{rb}+{MR_{r} \over L_{r}}i_{sa} \\ \dot{\phi}_{rb} &=n_{p}\omega\phi_{ra}-{R_{r}\over L_{r}}\phi_{rb}+{MR_{r}\over L_{r}} i_{sb}\cr \dot{\omega} &={n_{p} M \over JL_{r}}(\phi_{ra}i_{sb}-\phi_{rb}i_{sa})-{fv\over J}\omega-{1\over J}T_{l}, \end{align*}$$

where $i_{sa}, i_{sb}, \phi_{ra},\phi_{rb}$ and $\omega$ denote stator currents, rotor fluxes, and angular velocity, respectively, and $u_{sa}$ and $u_{sb}$ denote stator voltage inputs. The parameters $\sigma$ and $\gamma$ are defined as $\sigma = 1-M^2/L_sL_r, \gamma = (L_r^2r_s+M^2R_r)/\sigma L_s L_r^2 \cdot M, L_s, L_r, R_s$ and $R_r$ denote the mutual inductance, the self-inductances, the resistances, respectively. The subscript $a$ and $b$ denote the components of a vector with respect to a fixed stator reference frame and $s, r$ stand for stator and rotor of motor. $n_p, f_v, J, T_l$ are the number of pole-pair, the co-efficient of viscous damping, the inertia of rotor, and the load torque. We assume that the state variables $i_{sa}, i_{sb}, \omega$ are available for measurement and $T_l$ has a unknown constant value, that is, $\dot{T}_l=0$ . As a result, the model of induction motor can be rewritten into the form

$\eqalignno{ \dot{x}_{i} & =A_{i} (u, y_{i+1}, \cdots,y_{p})x_{i}\cr & +g_{i}(x_{1}, \cdots, x_{i},; u; y_{i+1}, \cdots, y_{p}) \cr & y_{i}=C_{i}x_{i}, 1\leq i \leq p}$

as follows:

$\eqalignno{ & \dot{x}_{1}=\left(\matrix{ 0 & A_{11}(y_{2})\cr 0 & 0}\right)x_{1}+g_{1}(x_{1}, u, y_{2})\cr & \dot{x}_{2}=\left(\matrix{ 0 & A_{21}\cr 0 & 0}\right) x_{2}+g_{2}(x_{1}, x_{2}, u)\cr & y_{1}=C_{1}x_{1}\cr & y_{2}=C_{2}x_{2},}$

where $x_1=[i_{sa},i_{sb},\phi_{ra},\phi_{rb}]^T$, $x_2=[\omega,T_l]^T$, $y_1=[i_{sa},i_{sb}]^T$, $y_2=\omega$, $u=[u_{sa},u_{sb}]^T$ and

$\eqalignno{ & A_{11}= \left(\matrix{ MR_{r}/ \sigma L_{s}L_{r}^{2} & (n_{p}M/\sigma L_{s}L_{r})y_{2}\cr -(n_{p}M/ \sigma L_{s}L_{r})y_{2} & M R_{r}/\sigma L_{s}L_{r}^{2}}\right)\cr & A_{21}=\left(\matrix{ -{1\over J}}\right)\cr & g_{1}=\left(\matrix{ -\gamma i_{sa} +(1/ \sigma L_{s})u_{sa}\cr -\gamma i_{sb} + (1/\sigma L_{s})u_{sb}\cr -(R_{r}/L_{r})\phi_{ra}-n_{p}y_{2}\phi_{rb}+(MR_{r}/L_{r})i_{sa}\cr n_{p}y_{2}\phi_{ra} -(R_{r}/L_{r})\phi_{rb}+(MR_{r}/L_{r})i_{sb}}\right)\cr & g_{2}=\left(\matrix{(n_{p}M/JL_{r})(\phi_{ra}i_{sb})-(\phi_{rb} i_{sa}) - (f_{v}/J)\omega)\cr 0}\right)}$

Type: 

Form: 

Model order: 

4

Time domain: 

Linearity: 

Publication details: 

TitleA state observer for a special class of MIMO nonlinear systems and its application to induction motor
Publication TypeConference Paper
Year of Publication2002
AuthorsLee, Sungryul
Conference NameProceedings of the 41st IEEE Conference on Decision and Control, 2002.
Date Published12/2002
PublisherIEEE
Conference LocationLas Vegas, NV, USA
ISBN Number0-7803-7516-5
Accession Number7670389
Keywordsinduction motors, machine control, MIMO systems, nonlinear control systems, observers
AbstractPresents an observer design methodology for a special class of MIMO nonlinear systems. First, we characterize the class of MIMO nonlinear systems that consists of the linear observable part and the nonlinear one with a block triangular structure. Also, the similarity transformation that plays an important role in proving the convergence of the proposed observer is generalized to MIMO systems. From this, we propose the state observer that can be seen as an interconnection of the existing observer for SISO triangular nonlinear systems. Since the gain of the proposed observer minimizes a nonlinear part of the system to suppress the stability of the error dynamics, it improves the transient performance of the high gain observer. Finally, the simulation results for an induction motor are included to illustrate the validity of our design scheme.
DOI10.1109/CDC.2002.1184484

Dynamics of Hydrostatic Transmission

Model description: 

The hydrostatic transmission dynamics is represented by a nonlinear fourth order state-space model

$$\begin{align*} \dot{q}_{1}(t) &= -a_{11}q_{1}(t)+b_{11}u_{1}(t) \\ \dot{q}_{2}(t) &= -a_{22}q_{2}(t)+b_{22}u_{2}(t) \\ \dot{q}_{3}(t) &= a_{31}q_{1}(t)p(t)-a_{33}q_{3}(t)-a_{34}q_{2}(t)q_{4}(t) \\ \dot{q}_{4}(t) &=a_{43}q_{2}(t)q_{3}(t)-a_{44}q_{4}(t), \end{align*}$$

where $q_1(t)$ is the normalized hydraulic pump angle, $q_2(t)$ is the normalized hydraulic motor angle, $q_3(t)$ is the pressure difference [bar], $q_4(t)$ is the hydraulic motor speed [rad/s], $p(t)$ is the speed of hydraulic pump [rad/s], $u_1(t)$ is the normalized control signal of the hydraulic pump, and $u_2(t)$ is the normalized control signal of the hydraulic motor. It is supposed that the external variable $p(t)$ , as well as the second state variable $q_2(t)$ are measurable. In given working point the model parameters are

$\eqalignno{& a_{11}=7.6923 \qquad a_{22}=4.5455 \quad a_{33}=7.6054.10^{-4} \cr &a_{31}=0.7877 \qquad a_{34}=0.9235\quad\ b_{11}=1.8590.10^{3} \cr &a_{43}=12.1967 \quad\ \ a_{44}=0.4143\quad b_{22}= 1.2879.10{}^{{3}}}$

Type: 

Form: 

Model order: 

4

Time domain: 

Linearity: 

Publication details: 

TitleDesign of Stable Fuzzy-Observer-Based Residual Generators for a Class of Nonlinear Systems
Publication TypeConference Paper
Year of Publication2011
AuthorsKrokavec, D., Filasova A., and Hladky V.
Conference Name15th IEEE International Conference on Intelligent Engineering Systems (INES), 2011
Date Published06/2011
PublisherIEEE
Conference LocationPoprad
ISBN Number978-1-4244-8954-1
Accession Number12118815
Keywordscontinuous time systems, fault diagnosis, fuzzy systems, linear matrix inequalities, MIMO systems, nonlinear control systems, observers, stability
AbstractOne principle for designing fuzzy-observer-based fault residual generators for one class of continuous-time nonlinear MIMO system is treated in this paper. The problem addressed can be indicated as an approach given sufficient conditions for residual generator design based on fuzzy system state observers. The conditions are outlined in the terms of linear matrix inequalities to possess a stable structure closest to optimal asymptotic properties. Simulation results illustrate the design procedures and demonstrate the performance of the proposed residual generator.
DOI10.1109/INES.2011.5954768

Two Continuously Stirred-Tank Reactor Process

Model description: 

The process dynamic model consists of six nonlinear ordinary differential equations:

$$\begin{align*} \dot x_{11} &= b_{11} x_{12} \\ \dot x_{12} &= b_{12} u_1 \\ \dot x_{21} &= b_{21} x_{22} + \phi _{21} \left({x_{11},x_{21} } \right) + \Phi x_{31} \\ \dot x_{22} &= b_{22} u_2 + \phi _{22} \left({x_{21},x_{22} } \right) \\ \dot x_{31} &= b_{31} x_{32} + \phi _{31} \left({x_{11},x_{12},x_{21},x_{31} } \right) + \Psi w \\ \dot x_{32} &= b_{32} u_3 + \phi _{32} \left({x_{31},x_{32} } \right) \\ y &= \left[{y_1,y_2,y_3 } \right] = \left[{x_{11},x_{21},x_{31} } \right], \end{align*}$$

where

$\eqalignno{b_{11} &= 1,b_{12} = 1,b_{21} = {{UA} \over {\rho c_p V}},b_{22} = {{F_{j2} } \over {V_j}},b_{31} = {{UA} \over {\rho c_p V}}\cr b_{32} &= {{F_{j1} } \over {V_j}},\Psi = {{F_0 } \over V},\Phi = {{F + F_R } \over V} \cr \phi _{21} &= {{F + F_R } \over V}T_1^d - {{F + F_R } \over V}\left({x_{21} + T_2^d } \right)\cr &\quad - {{\alpha \lambda } \over {\rho c_p}}\left({x_{11} + C_{A2}^d } \right)e^{- \left({{E \over {R\left({x_{21} + T_2^d } \right)}}} \right)}\cr &\quad - {{UA} \over {\rho c_p V}}\left({x_{21} + T_2^d - T_{j2}^d } \right) \cr \phi _{22} &= {{F_{j2} } \over {V_j}}\left({T_{j20}^d - x_{22} - T_{j2}^d } \right)\cr &\quad + {{UA} \over {\rho _j c_j V_j}}\left({x_{21} + T_2^d - x_{22} - T_{j2}^d } \right) \cr \phi _{31} &= {{F_0 } \over V}T_0^d - {{F + F_R } \over V}\left({x_{31} + T_1^d } \right) + {{F_R } \over V}\left({x_{21} + T_2^d } \right)\cr &\quad - {{\alpha \lambda } \over {\rho c_p}}C_A e^{- \left({{E \over {R\left({x_{31} + T_1^d } \right)}}} \right)} - {{UA} \over {\rho c_p V}}\left({x_{31} + T_1^d - T_{j1}^d } \right) \cr \phi _{32} &= {{F_{j1} } \over {V_j}}\left({T_{j10}^d - x_{32} - T_{j1}^d } \right)\cr &\quad + {{UA} \over {\rho _j c_j V_j}}\left({x_{31} + T_1^d - x_{32} - T_{j1}^d } \right) \cr C_A &= {V \over {F + F_R}}\Bigg(x_{12} + {{F + F_R } \over V}({x_{11} + C_{A2}^d })\cr &\quad + \alpha ({x_{11} + C_{A2}^d })e^{- \Big({{E \over {R({x_{21} + T_2^d })}}} \Big)} \Bigg). }$

The values of the process parameters are

$\eqalignno{& \alpha = {\rm 7}{\rm .08} \times {\rm 10}^{{\rm 10}} {\rm h}^{- 1},\quad \rho = 800.9189\,{\rm kg/m}^{\rm 3}\cr & \rho _j = 997.9450\,{\rm kg/m}^3,\quad \lambda = - 3.1644 \times {\rm 10}^{\rm 7} {\rm J/mol}\cr & R = 1679.2\,{\rm J/(mol} {\cdot} {}^{\circ} {\rm C)},\quad E = 3.1644 \times 10^7 {\rm J/mol}\cr & c_\rho = 1395.3\,{\rm J/(kg} {\cdot} {}^{\circ} {\rm C)},\quad c_j = 1860.3\,{\rm J/(kg} {\cdot} {}^{\circ} {\rm C)}\cr & U = 1.3625 \times 10^6{\kern1pt} {\rm J/(h} {\cdot} {\rm m}^{\rm 2} {\cdot} {}^{\circ} {\rm C)},\quad F_0 = F_2 = F = 2.8317\,{\rm m}^{\rm 3}\!{\rm /h}\cr & F_R = 1.4158\,{\rm m}^{\rm 3}\!{\rm /h},\quad F_{j1} = 1.4130\,{\rm m}^{\rm 3}\!{\rm /h}\cr & F_{j2} = 1.4130\,{\rm m}^{\rm 3}\!{\rm /h},\quad T_0^d = 703.7\,{}^{\circ} {\rm C},\quad T_1^d = 750\,{}^{\circ} {\rm C}\cr & T_2^d = 737.5\,{}^{\circ} {\rm C},\quad T_{j1}^d = 740.8\,{}^{\circ} {\rm C},\quad T_{j2}^d = 727.6\,{}^{\circ} {\rm C}\cr & T_{j10}^d \! = \! 629.2\,{}^{\circ} {\rm C},\quad T_{j20}^d \!=\! 608.2\,{}^{\circ} {\rm C},\quad C_{A0}^d \!=\! 18.3728\,{\rm mol/m}^{\rm 3}\cr & C_{A1}^d = 12.3061\,{\rm mol/m}^{\rm 3},\quad C_{A2}^d = 10.4178\,{\rm mol/m}^{\rm 3}\cr & V_1 = V_2 = V = 1.3592\,{\rm m}^{\rm 3},\quad V_{j1} = V_{j2} = V_j = 0.1090\,{\rm m}^{\rm 3}\cr & A = 23.2\,{\rm m}^{\rm 3} . }$

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

TitleRobust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems
Publication TypeJournal Article
Year of Publication2010
AuthorsLee, Hyeongcheol
JournalIEEE Transactions on Fuzzy Systems
Volume19
Issue2
Pagination265 - 275
Date Published11/2010
ISSN1063-6706
Accession Number11903670
Keywordsadaptive control, feedback, fuzzy control, MIMO systems, nonlinear control systems, robust control
AbstractThis paper presents a robust adaptive control method for a class of multi-input-multi-output (MIMO) nonlinear systems that are transformable to a parametric-strict-feedback form which has couplings among input channels and the appearance of parametric uncertainties in the input matrices. The proposed approach effectively combines the design techniques of robust adaptive control by backstepping and adaptive fuzzy-logic control in order to remove the matching-condition requirement and to provide boundedness of tracking errors, even under dominant model uncertainties and poor parameter adaptation. Unlike previous robust adaptive fuzzy controls of MIMO nonlinear systems, this research introduces the robustness terms explicitly in the controller structure to counteract the effects of model uncertainties and parameter-adaptation errors. Uniform boundedness of the MIMO nonlinear control system is proved, and simulation results further validate the effectiveness and performance of the proposed control method.
DOI10.1109/TFUZZ.2010.2095859

Relative Degree Two MIMO Nonlinear System:

Model description: 

Consider the following relative degree two MIMO nonlinear systems:

$$\begin{align*} \dot x_1 &= x_2 + \vartheta _1 x_1 \sin \left(t \right) + \Delta _1 \left({x_1 } \right) \\ \dot x_2 &= u + \vartheta _2 \left[{\matrix{{\left({x_1 + x_{2,1} } \right)\sin ^3 \left(t \right)} \cr {x_{2,1} + 2x_{2,2} } \cr}} \right] + \left[{\matrix{1 \cr {x_1 + x_{2,2} } \cr}} \right]\Delta _2 \left({x_{2,1} } \right) \\ y &= \left[{x_1,x_{2,2} } \right]^{\mathrm T}, \end{align*}$$

where $\Delta_1(x_1)=d_1\sin{(r_1x_1)}$ and $\Delta_2(x_{2,1})=d_2\tan{(r_2,x_{2,1})}.$ $\vartheta_1, \vartheta_2,\Delta_1,\Delta_2$ satisfy

$\displaylines{2 \le \vartheta _1 \le 4, - 4 \le \vartheta _2 \le - 1, \left\vert {\Delta _1 \left({x_1 } \right)} \right\vert \le \delta _1 = 40\cr \left\vert {\Delta _2 \left({x_{2,1} } \right)} \right\vert \le \delta _2 = 20. }$

The initial conditions are assumed to be $x_1(0)=0.5,x_{2,1}(0)=0$ and $x_{2,2}(0)=0.2$.

The actual plant parameters are

$\theta _1 = 3$, $\theta _2 = - 3$, $d_1 = - 30$, $d_2 = - 15$, $r_1 = 2$, $r_2 = 0.05.$

Type: 

Form: 

Time domain: 

Linearity: 

Publication details: 

TitleRobust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems
Publication TypeJournal Article
Year of Publication2010
AuthorsLee, Hyeongcheol
JournalIEEE Transactions on Fuzzy Systems
Volume19
Issue2
Pagination265 - 275
Date Published11/2010
ISSN1063-6706
Accession Number11903670
Keywordsadaptive control, feedback, fuzzy control, MIMO systems, nonlinear control systems, robust control
AbstractThis paper presents a robust adaptive control method for a class of multi-input-multi-output (MIMO) nonlinear systems that are transformable to a parametric-strict-feedback form which has couplings among input channels and the appearance of parametric uncertainties in the input matrices. The proposed approach effectively combines the design techniques of robust adaptive control by backstepping and adaptive fuzzy-logic control in order to remove the matching-condition requirement and to provide boundedness of tracking errors, even under dominant model uncertainties and poor parameter adaptation. Unlike previous robust adaptive fuzzy controls of MIMO nonlinear systems, this research introduces the robustness terms explicitly in the controller structure to counteract the effects of model uncertainties and parameter-adaptation errors. Uniform boundedness of the MIMO nonlinear control system is proved, and simulation results further validate the effectiveness and performance of the proposed control method.
DOI10.1109/TFUZZ.2010.2095859

Model of Phytoplanktonic Cell Growth

Model description: 

The models used to describe the growth of phytoplanktonic cells (biomass $x_2$) on a substrate (of concentration $x_1$) assume usually that the growth is a function of a variable ($x_3$) called internal quota, representing the nutrient stored in the cells:

$$\begin{align*} \dot{x}_1 &= u(t)(1-x_1)-\rho(x_1)x_2\\ \dot{x}_2 &= (\mu(x_3)-u(t))x_2\\ \dot{x}_3 &= \rho(x_1)-\mu(x_3)x_3. \end{align*}$$

The input $u(t)$ is the dilution rate of the continuously stirred bioreactor (we suppose $u(t) \geq u \geq 0$). The functions $\rho$ and $\mu$ represent the absorption rate and the growth rate:

$\rho(x_1)=a_1\dfrac{x_1}{a_2+x_1};$ $\mu(x_3)=a_3\left(1-\dfrac{a_4}{x_3}\right)$.

Type: 

Form: 

Model order: 

3

Time domain: 

Linearity: 

Publication details: 

TitleNon-linear qualitative signal processing for biological systems: application to the algal growth in bioreactors
Publication TypeJournal Article
Year of Publication1999
AuthorsBernard, Olivier, and Gouzé Jean-Luc
JournalMathematical Biosciences
Volume157
Start Page357
Issue1-2
Pagination357-372
Date Published03/1999
ISSN0025-5564
KeywordsAlgal growth, Bioreactor, Moving average, Non-linear systems, Qualitative behavior, Validation
AbstractWe present in this paper a qualitative method to validate and monitor the structure of a non-linear model with respect to experimental data, under some hypotheses. This method is broadly independent of the analytical formulation of the model, and depends only on the qualitative structure (the signs of the Jacobian matrix). The temporal sequences of the extrema of a filtered experimental signal are compared with the transitions allowed by a graph. In particular, we show that the usual moving average of the outputs follows this transition graph. We apply this method to compare models of algal growth in a bioreactor with experimental data.
DOI10.1016/S0025-5564(98)10091-3

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