• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.861-864
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• 1993

This research provides the results of a trial to generate the chaos by using nonlinear function constructed by fuzzy inference rules. The chaos generation function or chaotic behavior can be obtained by using Takagi-Sugeno fuzzy model with some constraint of the relationship of its parameters. Two examples are shown in this research. The first is simple example that construct of logistic image by fuzzy model. The second is more complicated one that provide the chaotic time series by non-linear autoregression based on fuzzy model. Simulated results are shown in these examples.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1649-1656
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• 2017

This paper addresses the control problem of a laboratory-level 2 degree-of-freedom helicopter. The exact fuzzy model in a Takagi - Sugeno form is constructed by the sector nonlinearity technique, and is then represented as a set of uncertain linear systems. Output-tracking controller is designed in terms of linear matrix inequalities and the closed-loop stability is rigorously analyzed. Experimental evaluation shows that the proposed method is of benefit to many real industrial plants.

• 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 the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.94-99
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• 2012

This paper discusses a decentralized sampled-data control problem for large-scale nonlinear systems. The system is represented in Takagi-Sugeno's form. Next, we design a decentralized analog controller based on the overlapping decomposition technique. The final step is to apply the intelligent digital redesign scheme for converting the analog controller into the sampled-data one. Design condition is represented in terms of linear matrix inequalities. A simulation result is provided for the effectiveness of the proposed design method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.1-6
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• 2008

In this paper, we present the robust fuzzy algorithm for variable speed control of wind turbines. Generally, the plants of wind turbines are consisted of complex nonlinearities, and the parameters of variable speed of wind turbines are represented as uncertain terms. For solving these complexity, we propose the robust fuzzy algorithm. At first, the exact fuzzy modeling are performed for variable speed of wind turbines. Next, we design the fuzzy controller for reanalyzed T-S fuzzy model of the wind turbines, then, we prove the stability of the plant through the Lyapunov stability theorem. At last, an example is included for visualizing the efficiency of the proposed technique.

• 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 Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.133-136
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• 2003

This paper presents a fuzzy control scheme for backing up control of Truck-Trailer, which is nonlinear and unstable by using TSK(Takagi-Sugeno-kang) fuzzy system. The nonlinear system of Truck-Trailer was expressed by using TSK fuzzy model, and the TSK fuzzy controller was designed from TSK fuzzy model. The usefulness of the proposed algorithm for backing up truck-trailer is certificated by the computer simulations.

• 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).

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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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 structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.2
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• pp.152-160
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• 2000

This paper addresses the analysis and design of fuzzy-model-based controller for nonlinear systems using extended PDC and optimal pole-placement schemes. In the design procedure, we represent the nonlinear system using a Takagi-Sugeno fkzy model and formulate the controller rules by using the extended parallel distributed compensator (EPDC) and construct an overall fuzzy logic controller by blending all local state feedback controllers with an optimal pole-placement scheme. Unlike the commonly used parallel distributed compensation technique, by blending a newly extended parallel distributed compensator and the optimal poleplacement schemes, we can design not only a local stable k z y controller but also an overall stable fuzzy controller to perform the tacking control objective. Furthermore, a stability analysis is carried out not only for the fuzzy model but also for a real nonlinear system. Finally. the effectiveness and feasibility of the proposed fizzy model-based controller design method has been shown through a simulation example.