• Title/Summary/Keyword: Affine T-S fuzzy model

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Controller Design for Affine T-S Fuzzy System with Parametric Uncertainties (파라미터 불확실성을 갖는 어핀 T-S 퍼지 시스템의 제어기 설계)

  • Lee, Sang-In;Park, Jin-Bae;Joo, Young-Hoon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.133-136
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    • 2004
  • This paper proposes a stability condition in affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties and then, introduces the design method of a fuzzy-model-based controller which guarantees the stability. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of linear matrix inequalities (LMIs).

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Design of Controller for Affine Takagi-Sugeno Fuzzy System with Parametric Uncertainties via BMI

  • Lee, Sang-In;Joo, Young-Hoon;Park, Jin-Bae
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.658-662
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    • 2004
  • This paper develops a stability analysis and controller synthesis methodology for a continuous-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties. Affine T-S fuzzy system can be an advantage because it may be able to approximate nonlinear functions to high accuracy with fewer rules than the homogeneous T-S fuzzy systems with linear consequents only. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of bilinear matrix inequalities (BMIs). A simulation example is given to illustrate the application of the proposed method.

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Controller Design for Discrete-Time Affine T-S Fuzzy System with Parametric Uncertainties (파라미터 불확실성을 갖는 이산시간 어핀 T-S 퍼지 시스템의 제어기 설계)

  • Lee, Sang-In;Park, Jin-Bae;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • 2004.07d
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    • pp.2516-2518
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    • 2004
  • This paper proposes a stability condition in discrete-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties and then, introduces the design method of a fuzzy-model-based controller which guarantees the stability. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of linear matrix inequalities (LMIs).

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Periodic Sampled-Data Control for Fuzzy Systems;Intelligent Digital Redesign Approach

  • Kim, D.W.;Joo, Y.H.;Park, J.B.
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1492-1495
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    • 2005
  • This paper presents a new linear-matrix-inequality-based intelligent digital redesign (LMI-based IDR) technique to match the states of the analog and the digital T-S fuzzy control systems at the intersampling instants as well as the sampling ones. The main features of the proposed technique are: 1) the affine control scheme is employed to increase the degree of freedom; 2) the fuzzy-model-based periodic control is employed; and the control input is changed n times during one sampling period; 3) The proposed IDR technique is based on the approximately discretized version of the T-S fuzzy system; but its discretization error vanishes as n approaches the infinity. 4) some sufficient conditions involved in the state matching and the stability of the closed-loop discrete-time system can be formulated in the LMIs format.

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LMI-Based Intelligent Digital Redesign for Multirate Sampled-Data Fuzzy Systems

  • Kim, Do-Wan;Joo, Young-Hoon;Park, Jin-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.1
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    • pp.113-118
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    • 2006
  • This paper presents a new linear-matrix-inequality-based intelligent digital redesign (LMI-based IDR) technique to match the states of the analog and the digital T-S fuzzy control systems at the intersampling instants as well as the sampling ones. The main features of the proposed technique are: 1) the affine control scheme is employed to increase the degree of freedom; 2) the fuzzy-model-based periodic control is employed, and the control input is changed n times during one sampling period; 3) The proposed IDR technique is based on the approximately discretized version of the T-S fuzzy system, but its discretization error vanishes as n approaches the infinity. 4) some sufficient conditions involved in the state matching and the stability of the closed-loop discrete-time system can be formulated in the LMIs format.