• Title, Summary, Keyword: fuzzy closed-loop systems

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A Note to the Stability of Fuzzy Closed-Loop Control Systems

  • Hong, Dug-Hun
    • Journal of the Korean Data and Information Science Society
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    • v.12 no.1
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    • pp.89-97
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    • 2001
  • Chen and Chen(FSS, 1993, 159-168) presented a reasonable analytical model of fuzzy closed-loop systems and proposed a method to analyze the stability of fuzzy control by the relational matrix of fuzzy system. Chen, Lu and Chen(IEEE Trans. Syst. Man Cybern., 1995, 881-888) formulated the sufficient and necessary conditions on stability of fuzzy closed-loop control systems. Gang and Chen(FSS, 1996, 27-34) deduced a linguistic relation model of a fuzzy closed loop control system from the linguistic models of the fuzzy controller and the controlled process and discussed the linguistic stability of fuzzy closed loop system by a linguistic relation matrix. In this paper, we study more on their models. Indeed, we prove the existence and uniqueness of equilibrium state $X_e$ in which fuzzy system is stable and give closed form of $X_e$. The same examples in Chen and Chen and Gang and Chen are treated to analyze the stability of fuzzy control systems.

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Sampled-Data Observer-Based Decentralized Fuzzy Control for Nonlinear Large-Scale Systems

  • Koo, Geun Bum;Park, Jin Bae;Joo, Young Hoon
    • Journal of Electrical Engineering and Technology
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    • v.11 no.3
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    • pp.724-732
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    • 2016
  • In this paper, a sampled-data observer-based decentralized fuzzy control technique is proposed for a class of nonlinear large-scale systems, which can be represented to a Takagi-Sugeno fuzzy system. The premise variable is assumed to be measurable for the design of the observer-based fuzzy controller, and the closed-loop system is obtained. Based on an exact discretized model of the closed-loop system, the stability condition is derived for the closed-loop system. Also, the stability condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed techniques.

PDC Intelligent control-based theory for structure system dynamics

  • Chen, Tim;Lohnash, Megan;Owens, Emmanuel;Chen, C.Y.J.
    • Smart Structures and Systems
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    • v.25 no.4
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    • pp.401-408
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    • 2020
  • This paper deals with the problem of global stabilization for a class of nonlinear control systems. An effective approach is proposed for controlling the system interaction of structures through a combination of parallel distributed compensation (PDC) intelligent controllers and fuzzy observers. An efficient approximate inference algorithm using expectation propagation and a Bayesian additive model is developed which allows us to predict the total number of control systems, thereby contributing to a more adaptive trajectory for the closed-loop system and that of its corresponding model. The closed-loop fuzzy system can be made as close as desired, so that the behavior of the closed-loop system can be rigorously predicted by establishing that of the closed-loop fuzzy system.

Development of Neuro-Fuzzy-Based Fault Diagnostic System for Closed-Loop Control system (페푸프 제어 시스템을 위한 퍼지-신경망 기방 고장 진단 시스템의 개발)

  • Kim, Seong-Ho;Lee, Seong-Ryong;Gang, Jeong-Gyu
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.6
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    • pp.494-501
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    • 2001
  • In this paper an ANFIS(Adativo Neuro-Fuzzy Inference System)- based fault detection and diagnosis for a closed loop control system is proposed. The proposed diagnostic system contains two ANFIS. One is run as a parallel model within the model in closed loop control(MCL) and the other is run as a series-parallel model within the process in closed loop(PCL) for the generation of relevant symptoms for fault diagnosis. These symptoms are further processed by another classification logic with simple rules and neural network for process and controller fault diagnosis. Experimental results for a DC shunt motor control system illustrate the effectiveness of the proposed diagnostic scheme.

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Intelligent Digital Redesign for Nonlinear Interconnected Systems using Decentralized Fuzzy Control

  • Koo, Geun-Bum;Park, Jin-Bae;Joo, Young-Hoon
    • Journal of Electrical Engineering and Technology
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    • v.7 no.3
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    • pp.420-428
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    • 2012
  • In this paper, a novel intelligent digital redesign (IDR) technique is proposed for the nonlinear interconnected systems which can be represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique is to convert a pre-designed analog controller into an equivalent digital one. To develop this method, the discretized models of the analog and digital closed-loop system with the decentralized controller are presented, respectively. Using these discretized models, the digital decentralized control gain is obtained to minimize the norm between the state variables of the analog and digital closed-loop systems and stabilize the digital closed-loop system. Its sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

Intelligent Controller for Networked Control Systems with Time-delay (시간지연을 갖는 네트워크 제어 시스템의 지능형 제어기 설계)

  • Bae, Gi-Sun;Joo, Young-Hoon
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.2
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    • pp.139-144
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    • 2011
  • We consider the stabilization problem for a class of networked control systems with random delays in the discrete-time domain. The controller-to-actuator and sensor-to-controller time-delays are modeled as two Markov chains, and the resulting closed-loop systems are Markovian jump nonlinear systems with two modes. The T-S (Takagi-Sugeno) fuzzy model is employed to represent a nonlinear system with Markovian jump parameters. The aim is to design a fuzzy controller such that the closed-loop Markovian jump fuzzy system is stochastically stable. The necessary and sufficient conditions on the existence of stabilizing fuzzy controllers are established in terms of LMIs (Linear Matrix Inequalities). It is shown that fuzzy controller gains are mode-dependent. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.

Robust Stability Analysis of Fuzzy Feedback Linearization Control Systems

  • Park, Chang-Woo;Lee, Chang-Hoon;Park, Mignon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.2 no.1
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    • pp.78-82
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    • 2002
  • In this paper, we have studied a numerical stability analysis method for the robust fuzzy feedback linearization regulator using Takagi-Sugeno fuzzy model. To analyze the robust stability, we assume that uncertainty is included in the model structure with known bounds. For these structured uncertainty, the robust stability of the closed system is analyzed by applying Linear Matrix Inequalities theory following a transformation of the closed loop systems into Lur'e systems.

Design of TSK Fuzzy Controller Based on TSK Fuzzy Model (TSK퍼지모델로부터 TSK퍼지제어기의 설계)

  • Kang, Geun-Taek;Lee, Won-Chang
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.11
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    • pp.53-67
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    • 1998
  • This paper suggests a method designing the TSK fuzzy controller based on the TSK fuzzy model, which guarantees the stability of the closed loop system and makes the response of the closed loop system to be a desired one. This paper deals with the general type of TSK fuzzy model of which consequents are affine equations having a constant term. The TSK fuzzy controller suggested in this paper is designed by using the pole placement which developed for the linear systems and makes the closed loop system have the same behavior as a desired linear system. A reference input can be introduced to the suggested TSK fuzzy controller and an integral action also can be introduced. Simulation results reveal that the suggested methods are practically feasible. This paper deals with both the continuous systems and the discrete systems.

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A design of neuro-fuzzy adaptive controller using a reference model following function (기준 모델 추종 기능을 이용한 뉴로-퍼지 적응 제어기 설계)

  • Lee, Young-Seog;Ryoo, Dong-Wan;Seo, Bo-Hyeok
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.2
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    • pp.203-208
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    • 1998
  • This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

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H∞ Control of T-S Fuzzy Systems Using a Fuzzy Basis- Function-Dependent Lyapunov Function (퍼지 기저함수에 종속적인 Lyapunov 함수를 이용한 T-S 퍼지 시스템의 H∞ 제어)

  • Choi, Hyoun-Chul;Chwa, Dong-Kyoung;Hong, Suk-Kyo
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.7
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    • pp.615-623
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    • 2008
  • This paper proposes an $H_{\infty}$ controller design method for Takagi-Sugeno (T-S) fuzzy systems using a fuzzy basis-function-dependent Lyapunov function. Sufficient conditions for the guaranteed $H_{\infty}$ performance of the T-S fuzzy control system are given in terms of linear matrix inequalities (LMIs). These LMI conditions are further used for a convex optimization problem in which the $H_{\infty}-norm$ of the closed-loop system is to be minimized. To facilitate the basis-function-dependent Lyapunov function approach and thus improve the closed-loop system performance, additional decision variables are introduced in the optimization problem, which provide an additional degree-of-freedom and thus can enlarge the solution space of the problem. Numerical examples show the effectiveness of the proposed method.