• International Journal of Fuzzy Logic and Intelligent Systems
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• 제10권1호
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• pp.43-48
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• 2010

This article presents the experimental studies of controlling angle and position of the inverted pendulum system using neural network to compensate for errors caused due to fuzzy controller. Although fuzzy control method can deal with nonlinearities of the system, fixed fuzzy rules may not work and result in tracking errors in some cases. First, a nominal Takagi-Sugeno (TS) type fuzzy controller with fixed weights is used for controlling the inverted pendulum system. Then the neural network is added at the reference input to form the reference compensation technique (RCT)control structure. Neural network modifies the input trajectories to improve system performances by updating internal weights in on-line fashion. The back-propagation learning algorithm for neural network is derived and used to update weights. Control hardware of a DSP 6713 board to have real time control is implemented. Experimental results of controlling inverted pendulum system are conducted and performances are compared.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.589-596
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• 2001

In this paper, a new design methodology for the optimal control of nonlinear systems described by the TS(Takagi-Sugeno) fuzzy model is proposed. First, a new theorem concerning the optimal stabilizing control of a general nonlinear dynamic system is proposed. Next, based on the proposed theorem and the inverse optimal approach, an optimal controller synthesis procedure for a TS fuzzy system is given, Also, it is shown that the optimal controller can be found by solving a linear matrix inequality problem. Finally, the proposed method is applied to the attitude control of a rigid spacecraft to demonstrate its validity.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.77-84
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• 2007

In this paper, Takagi-Sugeno (TS) fuzzy integral control is investigated to regulate the output voltage of an asymmetric half-bridge (AHB) DC/DC converter; First, we model the dynamic characteristics of the AHB DC/DC converter with state-space averaging method and small perturbation at an operating point. After introducing an additional integral state of the output regulation error, we obtain the $5^{th}$-order TS fuzzy model of the AHB DC/DC converter. Second, the concept of the parallel distributed compensation is applied to design the fuzzy integral controller, in which the state feedback gains are obtained by solving the linear matrix inequalities (LMIs). Finally, simulation results are presented to show the performance of the considered design method as the output voltage regulator and compared to the results for which the conventional loop gain method is used.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.227-232
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• 2006

In this paper, we propose a systematic method for intelligent digital redesign of a fuzzy-model-based controller for continuous-time nonlinear system which may also contain system uncertainties. The continuous-time uncertain TS fuzzy model is first contructed to represent the uncertain nonlinear system. A parallel distributed compensation(PDC) technique is then used to design a fuzzy-model-based controller for both stabilization. The designed continuous-time controller is then converted to an equivalent discrete-time controller by using a globally intelligent digital redesign method. This new technique is designed by a global matching of state variables between analog control system and digital control system. This new design technique provides a systematic and effective framework for integration of the fuzzy-model-based control theory and the advanced digital redesign technique for nonlinear systems with uncertainties. Finally, Chaotic Lorenz system is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.

• 한국지능시스템학회논문지
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• 제15권7호
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• pp.881-886
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• 2005

본 논문은 복합 상태 공간에서의 피지 기반 제어기를 이용한 지능형 디지털 재설계의 전 역적 접근 방안에 대해 제안하고자 한다. 이산화를 통한 제어기 설계에 있어서 불확실성이 포함된 실시간 비선형 시스템에 대해 보다 효율적이고 안정적인 접근을 위해 TS 퍼지 모델이 사용되었다. 그리고 전 역적 접근을 위한 방안으로서 문제를 볼록 최적화 관점으로 변환 후, 에러가 가질 수 있는 놈의 영역을 최소화하여 상태 접합을 이루고자 하였다. 또한 power series를 사용함으로써 불확실성이 조합된 비선형 시스템을 보다 더 정확하게 분석하였다. 샘플링 기간이 충분히 작다면, 불확실 비선형 시스템의 실시간 시스템으로의 전환이 충분한 이유를 가지게 된다. 전 역적 접근을 통한 디지털로 제어된 시스템은 선형 행렬 부등식 형태로 바꾸어 시스템의 안정성을 보장하고자 하였다. 마지막으로 TS 퍼지 모델로 분석된 혼돈 Lorenz System에 적용함으로써 제안된 방법의 안정성과 효율성을 보장받게 된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.524-526
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• 1999

In the construction of successful fuzzy models and/or controllers for nonlinear systems, the identification of a good fuzzy inference system is an important yet difficult problem, which is traditionally accomplished by a time-consuming trial-and-error process. In this paper, we propose a systematic identification procedure for complex multi-input single-output nonlinear systems with DNA coding method. A DNA coding method is optimization algorithm based on biological DNA as conventional genetic algorithms(GAs) are. The strings in the DNA coding method are variable-length strings, while standard GAs work with a fixed-length coding scheme. the DNA coding method is well suited to learning because it allows a flexible representation of a fuzzy inference system. We also propose a new coding method fur applying the DNA coding method to the identification of fuzzy models. This coding scheme can effectively represent the zero-order Takagi-Sugeno(TS) fuzzy model. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a Duffing-forced oscillation system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2117-2119
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• 2001

This paper proposes a novel intelligent digital redesign technique for a class of nonlinear systems represented by input-delayed Takagi-Sugeno (TS) fuzzy systems. The digitally redesigned controller can show good performance provided that the analog controller is well-designed. The developed digital redesign technique is based on the 'state-matching', so the control performance is guaranteed as well as the stability of the system. An simulation example is included to ensure the effectiveness of the proposed method.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.762-767
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• 2008

본 논문에서는 TS 퍼지 모델로 표현되는 시간 지연 비선형 시스템에 대한 $L_{\infty}$ 이득 상태 제한 퍼지 제어기를 제안한다. 이 위해 먼저 TS 퍼지 모델을 이용하여 시간 지연 비선형 시스템을 모델링한다. 다음 이 퍼지 모델을 기본으로 $L_{\infty}$ 이득을 얻기위해 퍼지 상태 궤환 제어기를 설계한다. 마지막으로 $L_{\infty}$ 이득을 얻기 위한 충분조건을 유도한다. 충분조건은 선형 행렬 부등식의 형태로 공식화 한다. 마지막으로 몇 가지 예제를 통하여 제안된 제어기의 효율성을 증명한다.

• 한국지능시스템학회논문지
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• 제15권3호
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• pp.318-323
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• 2005

본 논문에서는 불확실성을 포함한 연속시간 비선형 동적 시스템에 대한 퍼지 모델 기반 제어기의 지능형 디지털 재설계를 위한 조직적인 방법을 제안한다. 불확실 비선형 시스템을 표현하기 위해 연속시간 불확실 TS 퍼지 모델이 구성된다. 그리고, 안정화와 추적을 위한 퍼지 모델 기반 제어기 설계를 위해 PDC 기법이 이용된다. 마지막으로, 설계된 연속시간 제어기는 지능형 디지털 재설계 기법을 이용함으로써 이산시간 제어기로 전환됨을 증명하는 방법을 제안한다. 이 새로운 설계 기법은 퍼지 모델 기반 제어 이론과 불확실성을 가진 비선형 동적 시스템에 대한 진보된 디지털 재설계 기법의 통합을 위한 조직적이고, 효과적인 틀을 제공한다 마지막으로, 단일 링크 유연 로봇 시스템의 모의 실험을 이용해 개발된 설계 방법의 효용성과 실행 가능성을 입증한다.