A Truck-Trailer System

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Bilinear system

Model description: 

The time-invariant bilinear system is given by

$$Y(t) = 1.5X(t) + 1.2X(t-1) - 0.2X(t-2) + 0.7X(t-1)Y(t-1) - 0.1X(t-2)Y(t-2) + \epsilon(t),$$

where $A=0, \alpha=0, B=\begin{bmatrix}1.5 &1.2 &-0.2\end{bmatrix}, C = \begin{bmatrix}0.7 &0 &-0.1\end{bmatrix}$. Note that $\Theta = \begin{bmatrix}B & C\end{bmatrix}^{\mathrm T}.$

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Model order: 

2

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Publication details: 

TitleIdentification of bilinear systems using Bayesian inference
Publication TypeConference Paper
Year of Publication1998
AuthorsMeddeb, S., Tourneret J.Y., and Castanie F.
Conference NameProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, 1998.
Date Published05/1998
PublisherIEEE
Conference LocationSeattle, WA
ISBN Number0-7803-4428-6
Accession Number6053933
KeywordsBayes methods, bilinear systems, discrete time systems, inference mechanisms, Markov processes, Monte Carlo methods, parameter estimation, signal sampling
AbstractA large class of nonlinear phenomena can be described using bilinear systems. Such systems are very attractive since they usually require few parameters, to approximate most nonlinearities (compared to other systems). This paper addresses the problems of bilinear system identicalness using Bayesian inference. The Gibbs sampler is used to estimate the bilinear system parameters, from measurements of the system input and output signals
DOI10.1109/ICASSP.1998.681761

Bilinear descriptor system

Model description: 

Consider the following bilinear descriptor system:

$$\begin{pmatrix} 1 & -1\\ 0 & 0 \end{pmatrix}x_{k+1}=\begin{pmatrix} -0.5 & 1\\ -1 & 0 \end{pmatrix}x_{k}+\begin{pmatrix} 0.5 & 0.25\\ -1 & 0.5 \end{pmatrix}x_{k}u_{k}.$$

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Publication details: 

TitleStabilization of Discrete-time Bilinear Descriptor Systems
Publication TypeConference Paper
Year of Publication2006
AuthorsLu, Guoping, Zhang Xiaomei, Tang Hongji, and Zhou Lei
Conference NameThe Sixth World Congress on Intelligent Control and Automation, 2006.
Date Published06/2006
PublisherIEEE
Conference LocationDalian, China
ISBN Number1-4244-0332-4
Accession Number9187947
Keywordsasymptotic stability, bilinear systems, closed loop systems, discrete time systems, state feedback
AbstractThis paper discusses global asymptotic stabilization of a class of discrete-time bilinear descriptor systems. By means of LaSalle invariant principle and the implicit function theorem, a sufficient condition is presented to guarantee the uniqueness and existence of solution and the global asymptotic stability of the resulting closed-loop systems simultaneously. Finally, the effectiveness of the proposed approach is illustrated by a numerical example
DOI10.1109/WCICA.2006.1712293

MIMO nonlinear system

Model description: 

$$\begin{align*} y_{1}(k+1) &=0.9y_{1}(k)-0.3y_{1}(k-1)/[1+y_{2}^{2}(k-1)]+0.7u_{1}(k) \\ &+0.1y_{1}^{2}(k-1)y_{2}^{2}(k)+0.3\sin(u_{1}(k-1))-0.7u_{2}(k)+0.6u_{2}(k-1) \\ y_{2}(k+1) &=-0.1y_{2}(k-1)+0.3y_{1}(k-1)y_{2}(k)+0.8\sin(u_{1}(k)) \\ &+0.1u_{1}(k-1)+0.9u_{2}(k)+0.2u_{2}(k-1)+0.1u_{2}^{2}(k-1) \end{align*}$$

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TitleStable adaptive neural network control of MIMO nonaffine nonlinear discrete-time systems
Publication TypeConference Paper
Year of Publication2008
AuthorsZhai, Lianfei, Chai Tianyou, Yang Chenguang, Ge S.S, and Lee Tong Heng
Conference Name47th IEEE Conference on Decision and Control, 2008.
Date Published12/2008
PublisherIEEE
Conference LocationCancun
ISBN Number978-1-4244-3123-6
Accession Number10442029
Keywordsadaptive control, closed loop systems, control system synthesis, discrete time systems, MIMO systems, neurocontrollers, nonlinear control systems, stability
AbstractIn this paper, stable adaptive neural network (NN) control, a combination of weighted one-step-ahead control and adaptive NN is developed for a class of multi-input-multi-output (MIMO) nonaffine nonlinear discrete-time systems. The weighted one-step-ahead control is designed to stabilize the nominal linear system, while the adaptive NN compensator is introduced to deal with the nonlinearities. Under the assumption that the inverse control gain matrix has an either positive definite or negative definite symmetric part, the obstacle in NN weights tuning for the MIMO systems is transformed to unknown control direction problem for single-input-single-output (SISO) system. Discrete Nussbaum gain is introduced into the NN weights adaptation law to overcome the unknown control direction problem. It is proved that all signals of the closed-loop system are bounded, while the tracking error converges to a compact set. Simulation result illustrates the effectiveness of the proposed control.
DOI10.1109/CDC.2008.4738830

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

A Truck-Trailer System

Model description: 

Consider a truck-trailer system depicted in the attached image. Its dynamics is described by

$$\begin{align*} x_{1}(t+1) &=\left(1-\frac{vT}{L}\right)x_{1}(t)+\frac{vT}{l}u(t) \\ x_{2}(t+1) &=\frac{vT}{L}x_1(t)+x_{2}(t) \\ x_{3}(t+1) &=x_{3}(t)+vT\sin\left(\frac{vT}{2L}x_{1}(t)+x_{2}(t)\right)x_{1}(t), \end{align*}$$

where $x_1(t)$ : angle difference between truck and trailer. $x_2(t)$ : angle of trailer. $x_3(t)$ : vertical position of rear of trailer, $u(t)$ : steering angle, $T$ : sampling time. In this example, the parameters are $T=2.0s$, $l=2.8m$, $L=5.5m$, $v=-1.0m/s$.

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Form: 

Model order: 

3

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Attachment: 

Publication details: 

TitleStabilization of discrete-time nonlinear control systems - Multiple fuzzy Lyapunov function approach
Publication TypeConference Paper
Year of Publication2009
AuthorsKau, Shih-Wei, Huang Xin-Yuan, Shiu Sheng-Yu, and Fang Chun-Hsiung
Conference NameInternational Conference on Information and Automation, 2009. ICIA '09.
Date Published06/2009
PublisherIEEE
Conference LocationZhuhai, Macau
ISBN Number978-1-4244-3607-1
Accession Number10837484
Keywordsdiscrete time systems, fuzzy control, linear matrix inequalities, Lyapunov methods, nonlinear control systems, stability
AbstractThis paper deals with the stabilization problem for discrete-time nonlinear systems that are represented by the Takagi - Sugeno fuzzy model. By the multiple fuzzy Lyapunov function and the three-index algebraic combination technique, a new stabilization condition is developed. The condition is expressed in the form of linear matrix inequalities (LMIs) and proved to be less conservative than existing results in the literature. Finally, a truck-trailer system is given to illustrate the novelty of the proposed approach.
DOI10.1109/ICINFA.2009.5204890

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