SISO NLTI plant

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Generic nonlinear system

Model description: 

$$\begin{align*} \dot{x}_1 &= x_1 = x_1x_4 + \theta_1x_1^2 \theta_2x_3 + x_2u_1 + u_2 + u_3 \\ \dot{x}_2 &= x_2\cos{x_3} + \theta_3x_4 + u_1 + u_4 \\ \dot{x}_3 &= \sin{x_2} - x_3 \\ \dot{x}_4 &= x_3 - x_4 + x_1x_4 + \theta_1x_1^2 + (1 + x_2)u_1 + u_2 + u_3 \\ y_1 &= x_1 \\ y_2 &= x_2, \end{align*}$$

where $\theta_1 = 0.5$, $\theta_2 = 2$, and $\theta_3 = 1$ are unknown constant parameters.

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Publication details: 

TitleVirtual Grouping based adaptive actuator failure compensation for MIMO nonlinear systems
Publication TypeJournal Article
Year of Publication2005
AuthorsTang, Xidong
JournalIEEE Transactions on Automatic Control
Volume50
Issue11
Start Page1775
Pagination1780
Date Published11/2005
ISSN0018-9286
Accession Number8646599
Keywordsactuators, adaptive control, closed loop systems, control system synthesis, failure analysis, MIMO systems, nonlinear control systems, redundancy
AbstractA new control design technique called virtual grouping is presented to handle actuator redundancy and failures for multiple-input-mutliple-output (MIMO) systems, enlarging the set of compensable actuator failures. An adaptive compensation scheme is thus developed for a class of nonlinear MIMO systems to ensure closed-loop signal boundedness and asymptotic output tracking despite unknown actuator failures. Simulation results are given to show the effectiveness of the adaptive design.
DOI10.1109/TAC.2005.858633

Electric drive with a four-pole squirrel-cage induction motor

Model description: 

The motor dynamics are mapped by aset of five highly coupled nonlinear differential equations as given by

$$\begin{align*} \dfrac{{\mathrm d}i_{qs}^r}{{\mathrm d}t} &= \dfrac{1}{L_{\Sigma}} \left[-\dfrac{L_{RM}^2r_s+M^2r_r}{L_{RM}}i_{qs}^r + \dfrac{r_rM}{L_{RM}}\Psi_{qr}^r - (L_\Sigma i_{ds}^r + M \Psi_{dr}^r) \omega_r + L_{RM}u_{qs}^r\right] \\ \dfrac{{\mathrm d}i_{ds}^r}{{\mathrm d}t} &= \dfrac{1}{L_{\Sigma}} \left[-\dfrac{L_{RM}^2r_s+M^2r_r}{L_{RM}}i_{ds}^r + \dfrac{r_rM}{L_{RM}}\Psi_{dr}^r + (L_\Sigma i_{qs}^r + M \Psi_{qr}^r) \omega_r + L_{RM}u_{ds}^r\right] \\ \dfrac{{\mathrm d}\Psi_{qr}^r}{{\mathrm d}t} &= \dfrac{r_r M}{L_{RM}}i_{qs}^r - \dfrac{r_r}{L_{RM}}\Psi_{qr}^r \\ \dfrac{{\mathrm d}\Psi_{dr}^r}{{\mathrm d}t} &= \dfrac{r_r M}{L_{RM}}i_{ds}^r - \dfrac{r_r}{L_{RM}}\Psi_{dr}^r \\ \dfrac{{\mathrm d}\omega_r}{{\mathrm d}t} &= -\dfrac{B_m}{J}\omega_r + \dfrac{P}{2J}\left[\dfrac{P}{2}\dfrac{M}{L_{RM}}(i_{qs}^r\Psi_{dr}^r - i_{ds}^r-\Psi_{qr})-T_L\right], \end{align*}$$

where $L_{\Sigma} = L_{SM}L_{RM}-M^2$. For the load torque we assume the expression $T_L=c_2\omega_r^2 + c_3\omega_r^3$.

The state vector is given by

$x(t) = [i_{qs}^r, i_{ds}^r, \Psi_{qr}^r, \Psi_{dr}^r, \omega_r]^{\mathrm T} = [x_1, x_2, x_3, x_4, x_5]^{\mathrm T}.$

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Model order: 

5

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Publication details: 

TitleNonlinear identification of induction motor parameters
Publication TypeConference Paper
Year of Publication1999
AuthorsPappano, V., Lyshevski S.E., and Friedland B.
Conference NameProceedings of the 1999 American Control Conference, 1999.
Date Published01/1999
PublisherIEEE
Conference LocationSan Diego, CA
ISBN Number0-7803-4990-3
Accession Number6402981
Keywordsdynamics, identification, multivariable systems, Nonlinear dynamical systems, squirrel cage motors, state-space methods, transients
AbstractIn this paper, a nonlinear mapping identification concept is applied to identify the unknown parameters of induction motors using transient dynamics. The developed identification algorithm has significant advantages due to computational efficiency, robustness and convergence, reliability and feasibility. The reported model-based state-space identification can be applied to a wide class of nonlinear multivariable continuous-time dynamic systems. To illustrate the analytical results and to demonstrate the practical capabilities, the unknown motor parameters are found for a squirrel-cage induction motor, under the assumption that all the state vector is available for measurement
DOI10.1109/ACC.1999.782431

Continuously stirred tank reactor system

Model description: 

A schematic of the CSTR plant is shown in the attached image. The process dynamics are described by

$$\begin{align*} \dot{C}_{a} &=\frac{q}{V}(C_{a0}-c_{a})-a_{0}C_{a}e^{-\frac{E}{RT_{a}}} \\ \dot{T}_{a} &=\frac{q}{V}(T_{f}-T_{a})+a_{1}C_{a}e^{-\frac{E}{RT_{a}}}+a_{3}q_{c}\left(1-e^{\frac{a_{2}}{q_{c}}}\right)(T_{cf}-T_{a}), \end{align*}$$

where the variables $C_a$ and $T_a$ are the concentration and temperature of the tank, respectively; the coolant flow rate $q_c$ is the control input and the parameters of the plant are defined in the attached table. Within the tank reactor, two chemicals are mixed and react to produce a product compound $A$ at a concentration $C_a(t)$ with the temperature of the mixture being $T(t)$. The reaction is both irreversible and exothermic.

In the paper, authors assumed that plant parameters $q, C_{a0}, T_f$ and $V$ are at the nominal values given in the attached table. The activation energy $E/R = 1 \times 10^4K$ is assumed to be known. The state variables the input and the output are defined as $x=[x_1,x_2]^{\mathrm T}=[C_a,T_a]^{\mathrm T},u=q_c,y=C_a$. Using this notation, the CSTR plant can be re-expressed as

$$\begin{align*} \dot{x}_{1} &=1-x_{1}-a_{0}x_{1}e^{-\frac{10^4}{{\rm a}_2}} \\ \dot{x}_{2} &=T_{f}-x_{2}+a_{1}x_{1}e^{-\frac{10^4}{{\rm a}_2}}+a_{3}u\left(1-e^{-\frac{a_2}{u}}\right)(T_{cf}-x_{2}) \\ y &= x_{1}, \end{align*}$$

where the unknown constant parameters are $a_0, a_1, a_2$ and $a_3$.

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Attachment: 

Publication details: 

TitleAdaptive nonlinear control of continuously stirred tank reactor systems
Publication TypeConference Paper
Year of Publication2001
AuthorsZhang, T., and Guay M.
Conference NameProceedings of the 2001 American Control Conference, 2001.
Date Published06/2001
PublisherIEEE
Conference LocationArlington, VA
ISBN Number0-7803-6495-3
Accession Number7106659
Keywordsadaptive control, asymptotic stability, chemical technology, closed loop systems, feedback, Lyapunov methods, neurocontrollers, nonlinear control systems, process control
AbstractAdaptive nonlinear control is investigated for a class of continuously stirred tank reactor (CSTR) system. The CSTR plant under study belongs to a class of general nonlinear systems, and contains an unknown parameter that enters the model nonlinearly. Using adaptive backstepping and neural network (NN) approximation techniques, an alternative adaptive NN controller is developed that achieves asymptotic output tracking control. Both stability and control performance analysis of the closed-loop adaptive system are based on Lyapunov's stability techniques
DOI10.1109/ACC.2001.945898

Discrete bilinear plant

Model description: 

The plant is

$$y(k)=1.2y(k-1)-0.8y(k-2)+0.2y(k-1)u(k-1)+u(k-1)+0.6u(k-2) + d(k),$$

where $d(k)$ is a disturbance.

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Model order: 

2

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Publication details: 

TitleAdaptive Bilinear Model Predictive Control
Publication TypeConference Paper
Year of Publication1986
AuthorsYeo, Y.K., and Williams D.C.
Conference NameAmerican Control Conference, 1986
Date Published06/1986
PublisherIEEE
Conference LocationSeattle, WA
Keywordsadaptive control, control system synthesis, Delay, Error correction, Least squares approximation, Mathematical model, parameter estimation, predictive control, Predictive models, Programmable control
AbstractAn adaptive controller for bilinear plants without delay and with stable inverses is defined based upon a bilinear model predictive control law and a classical recursive identification algorithm. For the case with no disturbance both the control error and the identification error converge to zero. For the case with a bounded disturbance, the control error is bounded and the identification converges. For the case with a constant disturbance, the control error often converges to zero and the identification converges.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4789155&queryText%3DADAPTIVE+BILINEAR+MODEL+PREDICTIVE+CONTROL

SISO NLTI plant

Model description: 

Consider the following unknown discrete nonlinear dynamic system:

$$\begin{align*} y(k+1)&=p[{\bf q}(k), u(k)]=0.2\cos[0.8(y(k)+y(k-1))] \\ & +0.4\sin[0.8(y(k)+y(k-1))+2u(k)+u(k-1)] \\ &+0.1[9+y(k)+y(k-1)]+\left[{2(u(k)+u(k-1))\over 1+\cos(y(k))}\right] \end{align*}$$

for $k=0,1,2,\ldots$ with $y(k)=0,u(k)=0$, for $k=0,-1,-2,\ldots$, $\Delta t := t(k+1)-t(k)=0.02sec$, for $k=0,1,2,\ldots$.

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Model order: 

2

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Publication details: 

TitleRobust nonlinear adaptive control using neural networks
Publication TypeConference Paper
Year of Publication2001
AuthorsAdetona, O., Sathananthan S., and Keel L.H.
Conference NameProceedings of the 2001 American Control Conference, 2001
Date Published06/2001
PublisherIEEE
Conference LocationArlington, VA
ISBN Number0-7803-6495-3
Accession Number7092721
Keywordsadaptive control, asymptotic stability, neurocontrollers, nonlinear control systems, radial basis function networks, robust control
AbstractThis paper provides a robust indirect adaptive control method for non-affine plants. Subject to some mild assumptions, the method can be applied to both minimum and non-minimum phase plants with operating regions of any finite size while avoiding a set of restrictions, at least one of which is imposed by all existing methods. The benefits are achieved under the following assumptions: 1) the operating region is limited to the basin of attraction of an asymptotically stable equilibrium point of the plant; 2) the desired output of the plant is sufficiently slowly varying; and 3) the output of the plant must be sufficiently sensitive to the input signal. It is shown that the adaptive control system will be stable in the presence of unknown bounded modeling errors
DOI10.1109/ACC.2001.946247

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