• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.4-7
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• 2001

A stable TSK-type FLC can be designed by the method of Parallel Distributed Compensation (PDC), but in this case, solving the LMI problem is not a trivial task. To overcome such a difficulty, a Time-Delay based FLC (TDFLC) is proposed. TSK-type TDFLC consists of Time-Delay Control (TDC) and Sliding Mode Control (SMC) schemes, which result in a robust controller basaed upon an integral sliding surface.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.357-367
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• 2000

A new robust load-frequency control methodology is proposed for nonlinear power systems with valve position limits of the governor in the presence of parametric uncertaines. The TSK fuzzy model is adopted and formulated for fuzzy modeling of the nonlinear power system. A sufficient condition of the robust stabilitry is presented in the sense of lyapunov for the TSK model with parametric uncertainties. The intekkigent digital redesign technique for the uncertain power systems is also studied. The effectiveness of the robust digital fuzzy controller disign mothod is demonstrated through a numerical simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6289-6295
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• 2014

Rework systems are the equipment used to install or remove semiconductor chips with BGA or SMD forms in printed circuit boards. The rework systems have hot air outlets. At the outlets, precise temperature control is needed to avoid heat shock. The aim of this paper was to suggest a new controller for temperature control at the hot air outlets. The suggested controller was a fuzzy PID controller. The fuzzy PID controllers were composed of TSK fuzzy rules and had outstanding ability for nonlinear systems control. This paper reports the design algorithm of fuzzy PID controllers, and the design process of the fuzzy PID controller for the temperature control of the outlets. Temperature control experiments were performed to verify the ability of the suggested controller. As a result, the RMS of the proposed method is 9.44 and the general method is 15.88. The experiments showed that the temperatures at the outlet using the suggested fuzzy PID controller followed the desired ones better than the commonly used PID controller.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2002.03a
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• pp.141-144
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• 2002

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.84-87
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• 2002

This paper presents an adaptive PID control scheme with fuzzy model for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model was used to estimate the error of control input, and the parameter of PID controller was adapted from the error The parameter of TSK fuzzy model was also adapted to plant by comparing the activity output of plant and model output. PID controller which was adapted the uncertainty of nonlinear plant and the change of parameter can be designed by using the presented method. The usefullness of algorithm which was proposed by the simulation of several nonlinear system was also certificated.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.298-309
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• 2004

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

• Smart Structures and Systems
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• v.18 no.5
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• pp.1005-1028
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• 2016

Two novel semi-active control methods for a seismically excited nonlinear benchmark building equipped with magnetorheological dampers are presented and evaluated in this paper. While a primary controller is designed to estimate the optimal control force of a magnetorheological (MR) damper, the required voltage input for the damper to produce such desired control force is achieved using two different methods. The first technique uses an optimal compact Takagi-Sugeno-Kang (TSK) fuzzy inverse model of MR damper to predict the required voltage to actuate the MR dampers (TSKFInv). The other voltage regulator introduced here works based on the maximum and minimum capacities of MR damper at each time-step (MaxMin). Both semi-active algorithms developed here, use acceleration feedback only. The results demonstrate that both TSKFInv and MaxMin algorithms are quite effective in seismic response reduction for wide range of motions from moderate to severe seismic events, compared with the passive systems and performs better than original and Modified clipped optimal controller systems, known as COC and MCOC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.342-345
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameter of PID controller are adapted using the error. The parameters of TSK fuzzy model are also adapted to plant. The proposed algorithm allows designing adaptive PID controller which is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.453-458
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• 1998

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. So as to handle a variety of nonlinearity and reflect the degree of confidence in the informations about system, we combine multiple model method with hierarchical prioritized structure. The mountain clustering technique is used in partition of system, and TSK rule structure is adopted to form the fuzzy rules. Back propagation algorithm is used for learning parameters in the rules. Computer simulations are performed to verify the effectiveness of the proposed method. It is useful for the treatment fo the nonlinear system of which the quantitative math-approach is difficult.