Relative Degree Two MIMO Nonlinear System:

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Recurrent Trainable Neural Network

Model description: 

The RTNN model is described bythe following equations:

$$\begin{align*} X(k+1) &= JX(k) + BU(k)\\ Z(k) &= S[X(k)]\\ Y(k) &= S[CZ(k)]\\ J &\doteq \mathrm{blockdiag}(J_i); |J_i| <1, \end{align*}$$

here $X(\cdot)$ is a $n$-state vector of the RTTN; $U(\cdot)$ is a $m$-input vector; $Y(\cdot)$ is a $l$-output vector; $Z(\cdot)$ is an auxiliary vector variable with $l$ dimension; $S(\cdot)$ is a vector-valued smooth activation function (sigmoid, $tanh$, saturation) with appropriate dimensions; $J$ is a weigh-state block-diagonal matrix with $(1 \times 1)$ and $(2 \times 2)$ blocks; $J_i$ is an $i-th$ block of $J$ and $|J_i|<1$ is a stability condition.

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Publication details: 

TitleAdaptive Neural Control of Nonlinear Systems
Publication TypeConference Paper
Year of Publication2001
AuthorsGarrido, Ruben
EditorBaruch, Ieroham, Flores Jose Martin, and Thomas Federico
Conference NameInternational Conference on Artificial Neural Networks - ICANN 2001
Date Published08/2001
PublisherSpringer
Conference LocationVienna, Austria
ISBN Number3-540-42486-5
URLhttp://dblp.uni-trier.de/rec/bib/conf/icann/2001

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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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
Issue3
Start Page630
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

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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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
Issue4
Start Page1630
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

Multi-Output Nonlinear System

Model description: 

Consider a multi-output nonlinear system in the form of

$$\begin{align*} \dot{x} &= f(x, u)\\ y &= h(x), \end{align*}$$

where $x \in \mathbb{R}^n$ is the state, $u \in \mathbb{R}^m$ is the control input, $y \in \mathbb{R}^p$ is the output, $f$ and $h$ are smooth vector fields. The control input $u: \mathbb{R} \rightarrow \mathbb{R}^m$ is assumed to be an analytic time function. In particular, we will restrict our interest to the class of systems of the following form:

$\eqalignno{ \dot{x} &= A_ix_i+g_i(x_1,\ldots,x_i;u;y_{i+1},\ldots,y_p)\\ y &= C_ix_i & 1 \leq i \leq p, }$

where $x=[x_1^{\mathrm T}, x_2^{\mathrm T}, \ldots, x_p^{\mathrm T}]^{\mathrm T} \in \mathbb{R}^n$, $x_i=[x_{i1}, x_{i2}, \ldots, x_{in}]^T \in \mathbb{R}^{n_i}$, $y=[y_1, \ldots, y_p]^{\mathrm T} \in \mathbb{R}^p$

$A_i = \begin{bmatrix} 0 & 1 & \cdots & 0 \\ \vdots & \vdots & \ddots &\vdots\\ 0 & 0 & \cdots & 1\\ 0 & 0 & \cdots & 0 \end{bmatrix} \in \mathbb{R}^{n_i \times n_i}, C_i = [1, 0, \ldots, 0] \in R^{1 \times n_i}$

$g_i = \begin{bmatrix} g_{i1} (x_{[1,i-1]}x_{i1},u,y_{[i+1, p]} )\\ g_{i1} (x_{[1,i-1]}x_{i1},x_{i2},u,y_{[i+1, p]} )\\ \vdots\\ g_{i1} (x_{[1,i-1]}x_{[i1,in_i]},u,y_{[i+1, p]} ) \end{bmatrix}$

with $x_{[1,i-1]}=[x_1^{\mathrm T}, \ldots, x_{i-1}^{\mathrm T}]^{\mathrm T}$ and $y_{[i+1,p]}=[y_{i+1},\ldots,y_p]^{\mathrm T}$, and $g$ is a smooth vector field.

The system has the form

$$\begin{align*} \dot{x}_1 &= x_2 + 0.01x_1u \\ \dot{x}_2 &= -x-1 + (1-x_1^2)+x_3u \\ \dot{x}_3 &= x_4 + 0.01x_2x_3 \exp(u) \\ \dot{x}_4 &= -x_3 + (1 - x_3^2)x_4 + u \\ y_1 &= x_1 \\ y_2 &= x_3 \end{align*}$$

with $u= 2 \sin {3t}$.

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Publication details: 

TitleState observer for MIMO nonlinear systems
Publication TypeJournal Article
Year of Publication2003
AuthorsLee, S., and Park M.
JournalIEE Proceedings on Control Theory and Applications
Volume150
Issue4
Start Page421
Pagination421-426
Date Published07/2003
ISSN1350-2379
Accession Number7732346
KeywordsMIMO systems, nonlinear control systems, observers
AbstractA state observer design for a special class of MIMO nonlinear systems which has a block triangular structure is presented. For this purpose an extension of the existing design for SISO triangular systems to MIMO cases is performed. Since the gain of the proposed observer depends on both the nonlinear and linear parts of the system, it improves the transient performance of the high gain observer. Also, by using a generalised similarity transformation for the error dynamics, it is shown that under a boundedness condition, the proposed observer guarantees the global exponential convergence of the estimation error. Finally, an illustrative example is included to show the validity of the design approach.
DOI10.1049/ip-cta:20030513

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

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

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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

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