New relaxed stabilization conditions for discrete‐time Takagi–Sugeno fuzzy control systems

Kong, Lei; Yuan, Jingqi

doi:10.1002/asjc.2047

Cited by 8 publications

(9 citation statements)

References 31 publications

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“…Another future research direction will be dedicated to the development of hybrid control techniques with focus on TP‐based model transformation applied to control by exploiting the advantageous features of fuzzy control. Several intelligent control features [45–50] will be integrated including fuzzy model features as, for example, those given in other studies [51–57] and other representative applications [58–62].…”

Section: Discussionmentioning

confidence: 99%

Tensor product‐based model transformation approach to tower crane systems modeling

Hedrea

Precup

Roman

et al. 2021

Asian Journal of Control

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This paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonlinear model of TCR systems as representative multi‐input–multi‐output controlled processes. A linear parameter‐varying model is next derived, and the modeling steps specific to TP–based model transformation are proceeded to obtain the TP model. The TP model is tested on TCR laboratory equipment in two open‐loop scenarios considering chirp signals and pseudorandom binary step signals applied to the three model inputs (control inputs). The nonlinear and TP model outputs in the two scenarios are the payload position, the cart position, and the arm angular position. The nonlinear and TP model outputs are collected, measured, and compared. The simulation results prove that the derived TP model approximately mimics the behavior of the nonlinear model; both system responses and numerical approximation errors are illustrated.

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Section: Discussionmentioning

confidence: 99%

Tensor product‐based model transformation approach to tower crane systems modeling

Hedrea

Precup

Roman

et al. 2021

Asian Journal of Control

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“…The value of a scheduling variable z j belonging to the fuzzy set

Z_{j}^{i}

is represented by the truth value of MFs w ij : R → [0, 1]. TS models are considered as a state space model of the micro‐grid 32‐34 ;

\overset{x}{true} = f ((), x, u)

y = h ((), x)

where f and h are differential functions representing the state and output models,

x \in R^{n_{x}}

is the state vector,

u \in R^{n_{u}}

is the input vector and

y \in R^{n_{y}}

is the output vector.…”

Section: The Detailed Procedures Of Proposed Algorithmmentioning

confidence: 99%

“…The stability condition of a TS model can be easily formulated by linear matrix inequality (LMI). This makes it possible to use for the stability studies 32‐35 …”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy and neural methods have their disadvantages, including the black‐box nature of the neural network and challenges associated with finding MF and the optimal weighting coefficients for the fuzzy system. In the neuro‐fuzzy system, however, the neural section is used to provide a full‐featured tool for uncertain system identification, while the fuzzy section is used to provide a framework of previous knowledge for neural network training 32‐35 . Therefore, a TS‐based neuro‐fuzzy system is used in this study.…”

Section: Introductionmentioning

confidence: 99%

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The islanded micro‐grid large signal stability analysis based on neuro‐fuzzy model

Ahmadi

Kazemi

2020

Int Trans Electr Energ Syst

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Summary The study and identification of stability condition in an islanded micro‐grid are critical due to the lack of generation resources and load changes. On the other hand, the output power of the renewable units and the load level in a micro‐grid are uncertain and the system does not have a dominant operating point. Therefore, small signal analysis methods results have small validity range and cannot be generalized to the whole system. Therefore, a Lyapunov function (LF) based large signal stability method is proposed in this article to address the problem of small signal methods. Also, a Neuro‐Fuzzy system is proposed to consider uncertainties and network modeling. In this study, the Takagi‐Sugeno (T‐S) fuzzy system is used. Artificial neural network is also applied to provide an optimal tool for identifying system uncertainties, while T‐S rules are used to provide a framework of previous knowledge of the system. The main purpose is to propose an identification and modeling process to study the stability condition and boundaries in an islanded micro‐grid. After the uncertain system is modeled, the system LF is calculated using Linear Matrix Inequality and the system domain of attraction is determined.

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“…Fuzzy logic control, which is known as a powerful scheme for the control of complicated nonlinear systems, has been extensively applied in industrial processes [1][2][3][4][5][6][7][8]. The method relying on T-S fuzzy models has been widely adopted in steering of complex systems with nonlinearities in recent years [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A new design of output feedback sliding mode controller for T‐S fuzzy‐affine systems

Qiu

Lam

2020

Asian Journal of Control

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This work focuses on output feedback sliding mode control (SMC) for nonlinear systems via T-S fuzzy affine models. Associated with the sliding surface, a singular system is firstly proposed to describe the dynamical features of the sliding motion. Adopting some convexification techniques and based on piecewise quadratic Lyapunov functions (PQLFs) and a common quadratic Lyapunov function (CQLF), sufficient criteria for the asymptotic stability analysis of the sliding mode dynamical system are attained. Two output feedback SMC synthesis schemes are developed to drive the closed-loop system states to the sliding surface in finite time. A simulation example is presented to show the validity of the proposed methods.

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New relaxed stabilization conditions for discrete‐time Takagi–Sugeno fuzzy control systems

Cited by 8 publications

References 31 publications

Tensor product‐based model transformation approach to tower crane systems modeling

Tensor product‐based model transformation approach to tower crane systems modeling

The islanded micro‐grid large signal stability analysis based on neuro‐fuzzy model

A new design of output feedback sliding mode controller for T‐S fuzzy‐affine systems

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