• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.59-64
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• 2017

In this paper, a controller for discrete Takagi-Sugeno(T-S) fuzzy model under imperfect premise matching is proposed. Most of previous papers have obtained the stabilization condition using common quadratic Lyapunov function. However, the stabilization condition may be conservative due to the typical disadvantage of the common quadratic Lyapunov function. Hence, in order to solve this problem, we propose the stabilization condition of discrete T-S fuzzy model using fuzzy Lyapunov function. Finally, the proposed approach is verified by the simulation experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.780-785
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• 2007

This paper proposes the decentralized dynamic output feedback controller for discrete-time nonlinear interconnected systems using Takagi-Sugeno (T-S) fuzzy model. Through T-S fuzzy model of each subsystem, the decentralized dynamic output feedback controller is designed. By the closed-loop subsystems with controller, it represents the linear matrix inequality (LMI) for stability of whole interconnected system. The value of control gain are obtained by LMI. An example is given to show the experimentally verification discussed throughout the paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.42.2-42
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• 2001

This paper deals with a fuzzy sliding mode observer for nonlinear systems. A nonlinear system is approximated by a multiple model Takagi Sugeno fuzzy system and then transformed into a canonical form for which a nonlinear observer is constructed. This study presents a type of fuzzy sliding mode observer that deals with matched and unmatched uncertainties in the plant dynamics very effectively. The proposed method was validated by the example of a inverted pendulum.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.523-527
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator, The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.188-190
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• 2000

This paper proposed design of robust fuzzy controller for nonlinear systems in the presence of parametric uncertainties. In the design procedure, we represent the nonlinear system using Takagi-Sugeno fuzzy model. A sufficient condition of the robust stability is presented in the sense of Lyapunov for the TSK fuzzy model with uncertainties. Finally, the effectiveness of proposed controller has been through a result of numerical simulation.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.648-650
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• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• Journal of Navigation and Port Research
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• v.26 no.2
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• pp.193-197
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• 2002

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship's maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.15-20
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• 2010

In this paper, we discuss a decentralized observer design problem for the fault detection in the large-scale continuous-time T-S (Takagi-Sugeno) fuzzy system. Since the fault detection residual is desired to be as sensitive as possible, on the fault, we use $\mathfrak{H}_-$ index performance criterion. Sufficient conditions for the existence of such a observer is presented in terms of linear matrix inequalities. Simulation results show the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• 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).