Pendulum system with Coulomb friction

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Third-order nonlinear discrete-time system #2

Model description: 

Image below shows the block diagram of a discrete-time system.

$$\begin{align*} H_1(z) &=\dfrac{0.2z^{-1}}{z^{-1}-0.21z^{-2}} \\ H_2(z) &=\dfrac{0.1z^{-1}}{1-1.1z^{-1}+0.3z^{-2}} \\ H_3(z) &=\dfrac{0.3z^{-1}}{1-0.4z^{-1}} \end{align*}$$

Type: 

Form: 

Model order: 

3

Time domain: 

Linearity: 

Publication details: 

TitleNonlinear system identification using genetic algorithms with application to feedforward control design
Publication TypeConference Paper
Year of Publication1998
AuthorsLuh, Guan-Chun, and Rizzoni G.
Conference NameProceedings of the 1998 American Control Conference, 1998.
Date Published06/1998
PublisherIEEE
Conference LocationPhiladelphia, PA
ISBN Number0-7803-4530-4
Accession Number6076036
Keywordsautoregressive processes, continuous time systems, discrete time systems, feedforward, genetic algorithms, identification, inverse problems, nonlinear systems
AbstractA GAMAS-based system identification scheme is developed to construct NARX model of nonlinear systems. Several simulated examples demonstrate that it can be applied to identify both nonlinear continuous-time systems and discrete-time systems with acceptable accuracy. Inverting the identified NARX model, a feedforward controller may be derived to track desired time varying signal of nonlinear systems. Sufficient conditions of the invertibility of NARX model are proposed to investigate the existence of the inverse model. Simulation results depict the effectiveness of the feedforward controller with the aid of simple feedback controller designed for regulation purpose
DOI10.1109/ACC.1998.703056

Third-order nonlinear discrete-time system #1

Model description: 

The block diagram of a third-order nonlinear discrete time system adopted by Fakhouri for identification evaluation is shown below.

$$\begin{align*} H_1(z) &=\dfrac{0.1z^{-1}}{1-0.5z^{-1}} \\ H_2(z) &=\dfrac{0.1z^{-1}}{1-1.3z^{-1}+0.42z^{-2}} \\ H_3(z) &=\dfrac{1.0z^{-1}}{1-0.7z^{-1}} \end{align*}$$

Type: 

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Model order: 

3

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Publication details: 

TitleNonlinear system identification using genetic algorithms with application to feedforward control design
Publication TypeConference Paper
Year of Publication1998
AuthorsLuh, Guan-Chun, and Rizzoni G.
Conference NameProceedings of the 1998 American Control Conference, 1998.
Date Published06/1998
PublisherIEEE
Conference LocationPhiladelphia, PA
ISBN Number0-7803-4530-4
Accession Number6076036
Keywordsautoregressive processes, continuous time systems, discrete time systems, feedforward, genetic algorithms, identification, inverse problems, nonlinear systems
AbstractA GAMAS-based system identification scheme is developed to construct NARX model of nonlinear systems. Several simulated examples demonstrate that it can be applied to identify both nonlinear continuous-time systems and discrete-time systems with acceptable accuracy. Inverting the identified NARX model, a feedforward controller may be derived to track desired time varying signal of nonlinear systems. Sufficient conditions of the invertibility of NARX model are proposed to investigate the existence of the inverse model. Simulation results depict the effectiveness of the feedforward controller with the aid of simple feedback controller designed for regulation purpose
DOI10.1109/ACC.1998.703056

Continuous stirred-tank reactor system

Model description: 

The following CSTR system developed by Liu(1967). The reaction is exothermic first-order, $A \rightarrow B$, and is given by the following mass and energy balances. One should notice that the energy balance includes cooling water jacket dynamics. The following model was identified using regression techniques on the energy balance equations:

$$\begin{align*} y(k) &= 1.3187y(k-1) - 0.2214y(k-2) - 0.1474y(k-3) \\ &- 8.6337u(k-1) + 2.9234u(k-2) + 1.2493u(k-3) \\ &- 0.0858y(k-1)u(k-1) + 0.0050y(k-2)u(k-1) \\ &+ 0.0602y(k-2)u(k-2) + 0.0035y(k-3)u(k-1) \\ &- 0.0281y(k-3)u(k-2) + 0.0107y(k-3)u(k-3). \end{align*}$$

Type: 

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Model order: 

3

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Publication details: 

TitleIdentification and Control of Bilinear Systems
Publication TypeConference Paper
Year of Publication1992
AuthorsBartee, James F., and Georgakis Christos
Conference NameAmerican Control Conference, 1992
Date Published06/1992
PublisherIEEE
Conference LocationChicago, Illinois
ISBN Number0-7803-0210-9
KeywordsAlgorithm design and analysis, Chemical processes, Continuous-stirred tank reactor, control system synthesis, Control systems, Control theory, linear systems, nonlinear control systems, nonlinear systems, process control
AbstractThe research presented in this paper combines the problem of identifiction and control of nonlinear processes. This is done by approximating the process with a bilinear model and designing model-based control structures (Reference System Controllers) based on the bilinear approximation. The identification of the bilinear model and the construction of the controller are described below. An example of the identification and control of an exothermic CSTR is also presented.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=4792606&queryText%3DIdentification+and+Control+of+Bilinear+Systems

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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Publication details: 

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

Pendulum system with Coulomb friction

Model description: 

Consider a pendulum system with Coulomb friction and external perturbation

$$ \ddot {\theta} = \frac{1}{J}u - \frac{g}{L}\sin \theta - \frac{V_s}{J}\dot{\theta } - \frac{P_s}{J}\mathrm{sgn}(\dot{\theta}) + \upsilon, $$

where parameters have the following values $M=1.1$, $L=0.9$, $J=ML^2=0.891$, $V_s=0.18$, $P_s=0.18$, $P_s=0.45$, $g=9.815$, and $v$ is an uncertain external perturbation $|\upsilon| \leq 1$.

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Model order: 

2

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Publication details: 

TitleA Simple Nonlinear Observer for a Class of Uncertain Mechanical Systems
Publication TypeJournal Article
Year of Publication2007
AuthorsSu, Yuxin, Müller P.C., and Zheng Chunhong
JournalIEEE Transactions on Automatic Control
Volume52
Issue7
Start Page1340
Pagination1340-1345
Date Published07/2007
ISSN0018-9286
Accession Number9606706
Keywordsasymptotic stability, MIMO systems, nonlinear control systems, observers, uncertain systems
AbstractA simple nonlinear observer is proposed for a class of uncertain nonlinear multiple-input-multiple-output (MIMO) mechanical systems whose dynamics are first-order differentiable. The global asymptotic observation of the proposed observer is proved. Thus, the observer can be designed independently of the controller. Furthermore, the proposed observer is formulated without any detailed model knowledge of the system. These advantages make it easy to implement. Numerical simulations are included to illustrate the effectiveness of the proposed observer.
DOI10.1109/TAC.2007.900851

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