MIMO discrete-time system with triangular form inputs

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Nonlinear Time Series

Model description: 

The following time series is modeled using RBF networks

$$y(t)=\left(0.8-0.5e^{-y^{2}(t-1)}\right)y(t-1)-\left(0.3+0.9e^{-y^{2}(t-1)}\right)y(t-2)+0.1\sin(\pi y(t-1))+\xi(t),$$

where $\xi(t)$ is a zero-mean Gaussian white noise sequence with variance 0.01.

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Publication details: 

TitleTwo-Stage Mixed Discrete–Continuous Identification of Radial Basis Function (RBF) Neural Models for Nonlinear Systems
Publication TypeJournal Article
Year of Publication2008
AuthorsLi, Kang, Peng Jian-Xun, and Bai E.-W.
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume56
Start Page630
Issue3
Pagination630-643
Date Published08/2008
ISSN1549-8328
Accession Number10543358
Keywordscomputational complexity, integer programming, Nonlinear dynamical systems, radial basis function networks
AbstractThe identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.
DOI10.1109/TCSI.2008.2002545

Droop model

Model description: 

The behavior of phytoplankton cells in a continuous reactor is usually described by the Droop model. Cell growth is limited by a nutrient with concentration $S$. The biomass has a concentration $N$ and $Q$ represents the cell quota of assimilated nutrient, expressed as the amount of intracellular nutrient per biomass unit. The dilution rate $D$ corresponds to the flow rate of renewal medium over the volume of the reactor, and $D$ is the input of the system.

We denote $D = D_0 + u$, and the system fits

$$\sum_D \begin{cases} \dot{x}_i = f(x) + ug(x)\\ y=h(x_1) \end{cases}$$

with

$f(x)=\begin{pmatrix} a_2\left(1-\dfrac{1}{x_2}\right)x_1 - D_0x_1\\ a_3\dfrac{x_3}{a_1+x_3} - a_2(x_2 - 1)\\ D_0(1-x_3)-\dfrac{x_1x_3}{a_1+x_3} \end{pmatrix}$

$g(x)=\begin{pmatrix} -x_1\\ 0\\ 1-x_3 \end{pmatrix}$, and $h(x_1)=x_1$, where

$ x_1 = (\rho_m N/S_i);\\ x_2 = (Q/K_Q);\\ x_3 = (S/S_i);\\ a_1 = (K_{\rho}/S_i);\\ a_2 = \mu_m;\\ a_3 = (\rho_m/K_Q). $

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3

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Publication details: 

TitleNonlinear observers for a class of biological systems: application to validation of a phytoplanktonic growth model
Publication TypeJournal Article
Year of Publication1998
AuthorsBernard, O., Sallet G., and Sciandra A.
JournalIEEE Transactions on Automatic Control
Volume43
Start Page1056
Issue8
Pagination1056-1065
Date Published08/1998
ISSN0018-9286
Accession Number6002262
Keywordsbiocybernetics, living systems, nonlinear systems, observability, observers, physiological models
AbstractThe authors construct nonlinear observers in order to discuss the validity of biological models. They consider a class of systems including many classical models used in biological modeling. They formulate the nonlinear observers corresponding to these systems and prove the conditions necessary for their exponential convergence. They apply these observers on the well-known Droop model which describes the growth of a population of phytoplanktonic cells. The validity of this model is discussed based on the performance of the observers working on experimental data
DOI10.1109/9.704977

A Three-Mass System

Model description: 

The transfer function of the three-mass-system is much more complex than it is for one dominant elasticity (two-mass-system).

$${G_{\rm mech}}(s) = \underbrace{{\dfrac{1} {T_{ \Sigma} \cdot s}}}_{G_{\rm rs}(s)} \cdot \underbrace{ \dfrac{ a_{7} \cdot s^{4} + a_{6} \cdot s^{3}+a_{5} \cdot s^{2} + a_{4} \cdot s + 1}{a_{3} \cdot s^{4} + a_{2} \cdot s^{3}+ a_{1} \cdot s^{2} + a_{4} \cdot s + 1}} _{G_{\rm nrs}(s)}$$

with

$T_{\Sigma} = T_{\rm M} + T_{{\rm L}1} + T_{{\rm L}2}$

and

$\begin{align*} a_{1}&=d_{1}d_{2}T_{{\rm C}1}T_{{\rm C}2}+T_{{\rm L}2}\left(T_{\rm M}+T_{{ \rm L}1}\right) \cdot \frac{T_{C2}}{T_{\Sigma }}+T_{\rm M}\left(T_{{\rm L}1}+T_{{\rm L}2}\right) \cdot \frac{T_{{\rm C}1}}{T_{\Sigma}} \\ a_{2}&=\frac{T_{{\rm C}1}T_{{\rm C}2}}{T_{\Sigma}}\cdot\left(d_{1}T_{{\rm L}2}\left(T_{\rm M}+T_{{\rm L}1}\right)+d_{2}T_{\rm M} \left(T_{{\rm L}1}+T_{{\rm L}2}\right)\right) \\ a_{3}&=\frac{T_{\rm M}T_{{\rm L}1}T_{{\rm L}2}T_{{\rm C}1}T_{{\rm C}2}}{T_{\Sigma}} \\ a_{4}&=d_{1}T_{{\rm C}1}+d_{2}T_{{\rm C}2} \\ a_{5}&=d_{1}d_{2}T_{{\rm C}1}T_{{\rm C}2}+T_{{\rm L}2}T_{{\rm C}2}+\left(T_{{\rm L}1}+T_{{\rm L}2}\right)\cdot T_{{\rm C}1} \\ a_{6}&=\left(\left(d_{1}+d_{2}\right)T_{{\rm L}2}+d_{2}T_{{\rm L}1}\right)\cdot T_{{\rm C}1}T_{{\rm C}2} \\ a_{7}&=T_{{\rm L}1}T_{{\rm L}2}T_{{\rm C}1}T_{{\rm C}2}. \end{align*}$

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Publication details: 

TitleApplication of the Welch-Method for the Identification of Two- and Three-Mass-Systems
Publication TypeJournal Article
Year of Publication2008
AuthorsVillwock, S., and Pacas M.
JournalIEEE Transactions on Industrial Electronics
Volume55
Start Page457
Issue1
Pagination457-466
Date Published01/2008
ISSN0278-0046
Accession Number9756566
Keywordselectric drives, frequency response, identification, machine control, spectral analysis
AbstractThis paper deals with the measurement of the frequency response of the mechanical part of a drive for the parameter identification of a plant. The system is stimulated by pseudorandom binary signals. The measurement of the frequency response is part of a system identification procedure being carried out during an automatic commissioning of the drive. For the calculation of the frequency response of the mechanics, the Welch-method is applied for spectral analysis. The Welch-method is known from the fields of communications and measurement engineering. This paper addresses the application of this powerful method for the identification of electrical drives. Investigations have pointed out that the pure utilization of conventional identification strategies does not yield satisfying experimental results. Experimental results presented in this paper point out clearly the efficiency and flexibility of the proposed Welch-method. This paper contains many practical aspects and realization details that are important for their implementation on industrial systems. Although in principle, commercial software tools can be utilized for identifying the parameters of the plant, this paper addresses the implementation of the necessary identification algorithms on the embedded control electronics of the drives. The utilization of the Levenberg-Marquardt-algorithm yields excellent results for the identified parameters on the basis of the measured frequency response data.
DOI10.1109/TIE.2007.909753

A Two-Mass System

Model description: 

The transfer function of a nonrigid mechanical system with two concentrated masses is given by

$$G_{\rm mech}(s) = \underbrace{ \dfrac{1}{s \cdot \left(T_{\rm M} + T_{\rm L}\right)} }_{G_{\rm rs}(s)} \cdot \underbrace{ \dfrac{T_{ \rm L} \cdot T_{\rm C} \cdot s^{2} + d \cdot T_{\rm C} s + 1} {\dfrac{ T_{\rm L} \cdot T_{\rm C} \cdot T_{\rm M}}{T_{\rm M} + T_{\rm L}} \cdot s^{2} + d \cdot T_{\rm C} \cdot s + 1}}_{G_{\rm nrs}(s)}.$$

$T_M$ and $T_L$ are the run-up times of the motor and the load. The nonrigid shaft of the two-mass-configuration is modeled as a damper-spring-system. $T_C$ is the normalized spring-constant and $d$ is the related damping of the spring.

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Publication details: 

TitleApplication of the Welch-Method for the Identification of Two- and Three-Mass-Systems
Publication TypeJournal Article
Year of Publication2008
AuthorsVillwock, S., and Pacas M.
JournalIEEE Transactions on Industrial Electronics
Volume55
Start Page457
Issue1
Pagination457-466
Date Published01/2008
ISSN0278-0046
Accession Number9756566
Keywordselectric drives, frequency response, identification, machine control, spectral analysis
AbstractThis paper deals with the measurement of the frequency response of the mechanical part of a drive for the parameter identification of a plant. The system is stimulated by pseudorandom binary signals. The measurement of the frequency response is part of a system identification procedure being carried out during an automatic commissioning of the drive. For the calculation of the frequency response of the mechanics, the Welch-method is applied for spectral analysis. The Welch-method is known from the fields of communications and measurement engineering. This paper addresses the application of this powerful method for the identification of electrical drives. Investigations have pointed out that the pure utilization of conventional identification strategies does not yield satisfying experimental results. Experimental results presented in this paper point out clearly the efficiency and flexibility of the proposed Welch-method. This paper contains many practical aspects and realization details that are important for their implementation on industrial systems. Although in principle, commercial software tools can be utilized for identifying the parameters of the plant, this paper addresses the implementation of the necessary identification algorithms on the embedded control electronics of the drives. The utilization of the Levenberg-Marquardt-algorithm yields excellent results for the identified parameters on the basis of the measured frequency response data.
DOI10.1109/TIE.2007.909753

MIMO discrete-time system with triangular form inputs

Model description: 

Simulation studies are carried out for the following MIMO discrete-time system with triangular form inputs

$$\begin{align*} x_{1,1}(k+1) &= f_{1,1}({\bar x}_{1,1}(k))+g_{1,1}({\bar x}_{1,1}(k))x_{1,2}(k) \\ x_{1,2}(k+1) &= f_{1,2}({\bar x}_{1,2}(k))+g_{1,2}({\bar x}_{1,2}(k))u_{1} (k)+d_{1}(k) \\ x_{2,1}(k+1) &= f_{2,1}({\bar x}_{2,1}(k)) +g_{2,1}({\bar x}_{2,1}(k))x_{2,2}(k) \\ x_{2,2}(k+1) &= f_{2,2}({\bar x}_{2,2}(k),u_{1}(k)) +g_{2,2}({\bar x}_{2,2}(k))u_{2}(k)+d_{2}(k) \\ y_{1}(k) &= x_{1,1}(k) \\ y_{2}(k) &= x_{2,1}(k), \end{align*}$$

where

$\begin{cases} f_{1,1}({\bar x}_{1,1}(k))={{x^{2}_{1,1}(k)}\over {1+x^{2}_{1,1}(k)}}\\ g_{1,1}({\bar x}_{1,1}(k))=0.3 \\ f_{1,2}({\bar x}_{1,2}(k))= {{x^{2}_{1,1}(k)}\over{1+x^{2}_{1,2}(k)+x^{2}_{2,1}(k)+x^{2}_{2,2}(k)}}\\ g_{1,2}({\bar x}_{1,2}(k))=1\\ d_{1}(k)=0.1 \cos{0.05k}\cos{x_{1,1}(k)}\\ \end{cases}$

$\begin{cases} f_{2,1}({\bar x}_{2,1}(k))= {{x^{2}_{2,1}(k)}\over {1+x^{2}_{2,1}(k)}}\\ g_{2,1}({\bar x}_{2,1}(k))=0.2\\ f_{2,2}({\bar x}_{2,2}(k),u_{1}(k))={{x^{2}_{2,1}(k)}\over{1+x^{2}_{1,1}+x^{2}_{1,2}(k)+x^{2}_{2,2}(k)}}u^{2}_{1}(k)\\ g_{2,2}({\bar x}_{2,2}(k))=1\\ d_{2}(k)=0.1\cos{0.05k}\cos{x_{2,1}(k)}\\ \end{cases}$

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Publication details: 

TitleAdaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time
Publication TypeJournal Article
Year of Publication2004
AuthorsGe, S.S., Zhang Jin, and Lee Tong Heng
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume34
Start Page1630
Issue4
Pagination1630-1645
Date Published08/2004
ISSN1083-4419
Accession Number8111571
Keywordscascade systems, closed loop systems, discrete time systems, Lyapunov methods, MIMO systems, neural nets, nonlinear systems, stability
AbstractIn this paper, adaptive neural network (NN) control is investigated for a class of multiinput and multioutput (MIMO) nonlinear systems with unknown bounded disturbances in discrete-time domain. The MIMO system under study consists of several subsystems with each subsystem in strict feedback form. The inputs of the MIMO system are in triangular form. First, through a coordinate transformation, the MIMO system is transformed into a sequential decrease cascade form (SDCF). Then, by using high-order neural networks (HONN) as emulators of the desired controls, an effective neural network control scheme with adaptation laws is developed. Through embedded backstepping, stability of the closed-loop system is proved based on Lyapunov synthesis. The output tracking errors are guaranteed to converge to a residue whose size is adjustable. Simulation results show the effectiveness of the proposed control scheme.
DOI10.1109/TSMCB.2004.826827

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