T-S fuzzy model

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Block-triangular MIMO system 1

Model description: 

$$\Sigma _{S_{1}}: \cases{ \begin{align*} \dot{x}_{1,1} &=f_{1,1}(\bar {x}_{1,1},\bar {x}_{2,1})+g_{1,1}(\bar {x}_{1,1},\bar{x}_{2,1})x_{1,2} \\ \dot{x}_{1,2} &=f_{1,2}(X)+g_{1,2}(\bar{x}_{1,1},\bar{x}_{2,1})u_{1} \\ \dot{x}_{2,1} &=f_{2,1}(\bar {x}_{1,1},\bar {x}_{2,1})+g_{2,1}(\bar{x}_{1,1},\bar{x}_{2,1})x_{2,2} \\ \dot{x}_{2,2} &=f_{2,2}(X,u_{1})+g_{2,2}(\bar{x}_{1,1},\bar {x} _{2,1})u_{2} \\ y_{j} &=x_{j,1}, \quad j=1,2, \end{align*}}$$

where $X = [\bar{x}_{1,2}^{\mathrm T}, \bar{x}_{2,2}^{\mathrm T}]^{\mathrm T}$ with $\bar{x}_{j,2}=[x_{j,1},x_{j,2}]^{\mathrm T},j=1,2$.

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TitleAdaptive neural control of uncertain MIMO nonlinear systems
Publication TypeJournal Article
Year of Publication2004
AuthorsGe, Shuzhi Sam, and Wang Cong
JournalIEEE Transactions on Neural Networks
Volume15
Issue3
Start Page674
Pagination674-692
Date Published05/2004
ISSN1045-9227
Accession Number8012935
Keywordsadaptive control, closed loop systems, control system synthesis, MIMO systems, neurocontrollers, nonlinear control systems
AbstractIn this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach.
DOI10.1109/TNN.2004.826130

VTOL system

Model description: 

Consider the VTOL example. The following shortcuts ${\rm c}(\cdot)=\cos(\cdot),\quad {\rm s}(\cdot)=\sin(\cdot)$ are used. The $\Delta_0$ is given as

$$\begin{align*} \omega_{0}^{1} &: {\mathrm d}x-v_{x}{\mathrm d}t \\ \omega_{0}^{2} &: {\mathrm d}v_{x}-(u^{2}\epsilon {\mathrm c}(\theta)-u^{1}{\mathrm s}(\theta)){\mathrm d}t \\ \omega_{0}^{3} &: {\mathrm d}z-v_{z}{\mathrm d}t \\ \omega_{0}^{4} &: {\mathrm d}v_{z}-(u^{1}{\mathrm c}(\theta)+u^{2}\epsilon {\mathrm s}(\theta)-1){\mathrm d}t \\ \omega_{0}^{5} &: {\mathrm d}\theta-\omega {\mathrm d}t \\ \omega_{0}^{6} &: {\mathrm d}\omega-u^{2}{\mathrm d}t, \end{align*}$$

where $\epsilon$ is a constant parameter. It can be shown that $\Delta_{0,\mathrm{d}t}^{\perp} = \mathrm{span}\{\delta_{u^1},\delta_{u^2}\}$. Construct $\Delta_1 \in \Delta_0$ such that $v_0(\Delta_1)\in \Delta_1$ holds with $v_0=\Delta_{0,{\mathrm d}t}^{\perp}$, i.e. $\Delta_1$ is given as:

$\begin{align*} \omega_{1}^{1} &: {\mathrm d}x-v_{x}{\mathrm d}t \\ \omega_{1}^{2} &: {\mathrm c}(\theta){\mathrm d}v_{x}+{\mathrm s}(\theta){\mathrm d}v_{z}-\epsilon {\mathrm d}\omega+{\mathrm s}(\theta){\mathrm d}t \\ \omega_{1}^{3} &: {\mathrm d}z-v_{z}{\mathrm d}t \\ \omega_{1}^{4} &: {\mathrm d}\theta-\omega {\mathrm d}t \end{align*}$

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TitleOn calculating flat outputs for Pfaffian systems by a reduction procedure - Demonstrated by means of the VTOL example
Publication TypeConference Paper
Year of Publication2011
AuthorsSchoberl, M., and Schlacher K.
Conference Name9th IEEE International Conference on Control and Automation (ICCA), 2011
Date Published12/2011
PublisherIEEE
Conference LocationSantiago
ISBN Number978-1-4577-1475-7
Accession Number12496118
Keywordsaircraft control, machinery, partial differential equations, sequences
AbstractThis paper addresses the problem of generating a flat system parametrization for Pfaffian systems in a constructive manner. The main idea behind the procedure is a subsequent application of transformations that decompose a given Pfaffian system into a sequence of systems. This splitting of a Pfaffian system possesses the property that a parametrization of the bottom part can be elementarily obtained provided a rank criterion is met and the parametrization for the upper part is known. Then (if possible) this procedure will be repeated with the upper system to generate a sequence of systems by gradual reduction of the complexity of the problem. The application of the whole machinery to the VTOL example will demonstrate the effectiveness of the procedure. In fact the well known flat output for the VTOL and an alternative one are derived using this constructive machinery in a systematic fashion.
DOI10.1109/ICCA.2011.6137922

Two-link Rigid Robot Manipulator

Model description: 

Consider a two-link rigid robot manipulator moving a horizontal plane. The dynamic equations of this MIMO system are

$$\left[\matrix{ \ddot{q}_{1}\cr \ddot{q}_{2} }\right]=\left[\matrix{ M_{11} & M_{12}\cr M_{21} & M_{22} }\right]^{-1} \left\{\left[\matrix{ u_{1}\cr u_{2} }\right]-\left[\matrix{ -h\dot{q}_{2} & -h(\dot{q}_{1}+\dot{q}_{2})\cr h\dot{q}_{1} & 0 }\right]\left[\matrix{ \dot{q}_{1}\cr \dot{q}_{2} }\right]\right\},$$

where

$\begin{align*} M_{11}&=a_{1}+2a_{3}\cos(q_{2})+2a_{4} \sin (q_{2}),\ M_{22}=a_{2} \\ M_{12}&=M_{21}=a_{2}+\alpha_{3}\cos(q_{2})+a_{4}\sin(q_{2}) \\ h&=a_{3}\sin(q_{2})-a_{4}\cos(q_{2}) \end{align*}$

with

$\begin{align*} a_{1}&=I_{1}+m_{1}l_{c1}^{2}+I_{e}+m_{e}l_{ce}^{2}+m_{e}l_{1}^{2} \\ a_{2}&=I_{e}+m_{e}l_{ce}^{2} \\ a_{3}&=m_{e}l_{1}l_{ce}\cos(\delta_{e}) \\ a_{4}&=m_{e}l_{1}l_{ce}\sin(\delta_{e}). \end{align*}$

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TitleIndirect adaptive fuzzy control for a class of MIMO nonlinear systems with unknown control direction
Publication TypeConference Paper
Year of Publication2010
AuthorsWuxi, Shi
Conference Name29th Chinese Control Conference (CCC), 2010
Date Published06/2010
PublisherIEEE
Conference LocationBeijing
ISBN Number978-1-4244-6263-6
Accession Number11612096
Keywordsadaptive control, approximation theory, closed loop systems, fuzzy control, matrix algebra, MIMO systems, nonlinear control systems, uncertain systems
AbstractIn this paper, an indirect adaptive fuzzy controller is developed for a class of uncertain MIMO nonlinear systems with unknown sign of the control gain matrix. Within this scheme,the fuzzy logic systems are used to approximate the plant's unknown nonlinear functions. The estimated gain matrix is decomposed into the product of one diagonal matrix and two orthogonal matrixes. In order to compensate the lumped errors,all parameter adaptive laws are adjusted by the time-varying dead-zone of the filtered tracking errors,which its size is adjusted adaptively with the estimated bounds on the approximation errors. The proposed scheme guarantees that all the signals in the resulting closed-loop system are bounded, and the tracking error converges to a small neighborhood of the origin. A simulation example is used to demonstrate the effectiveness of the proposed scheme.
URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5572851&queryText%3DIndirect+Adaptive+Fuzzy+Control+for+a+Class+of+MIMO+Nonlinear+Systems+with+Unknown+Control+Direction

A nonlinear system

Model description: 

Consider a nonlinear system

$$\begin{align*} x_{1}(t+1) &=x_{1}(t)-x_{1}(t)x_{2}(t)+(5+x_{1}(t))u(t) \\ x_{2}(t+1) &=-x_{1}(t)-0.5x_{2}(t)+2x_{1}(t)u(t) \end{align*}$$

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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

T-S fuzzy model

Model description: 

Consider a T-S fuzzy model

Plant Rule $i$: If $x_1(t)$ is $F_1^1(x_1(t))$

Then $x(t+1) = A_ix(t)+B_iu(t),$

where

$\begin{align*} A_1 &=\left[\matrix{-a & 2\cr -0.1 & b}\right], A_2=\left[\matrix{-a & 2\cr-0.1 & b }\right], A_3=\left[\matrix{-0.9 & 0.5\cr -0.1 & -1.7}\right] \\ B_1 &=\left[\matrix{b\cr 4}\right], B_2=\left[\matrix{b\cr 4.8}\right], B_3=\left[\matrix{3\cr 0.1}\right]. \end{align*}$

The parameters $a$ and $b$ are adjusted to compare the relaxation of stabilization conditions.

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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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