• 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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• 2000.11a
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• pp.372-375
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• 2000

퍼지 시스템은 비선형 시스템을 해석하고 제어기 설계 등에 많이 이용되고 있으나 대부분의 그 구현은 PC나 웍스테이션의 프로그램에 의존하고 있다. 고속의 동작을 요구하는 시스템이나 소형 시스템에는 전용 프로세서의 사용이 필요하다. 본 논문에서는 여러 퍼지 시스템 중에서 적은 규칙수로도 효과적인 성능을 나타내고 결론부가 선형식으로 표현되어 ASIC을 이용한 하드웨어화가 용이한 형태를 가진 TSK퍼지 추론 프로세서를 FPGA로 구현한다. ASIC의 설계는 Top-down 방식을 이용하여 전체구성은 Schematic을 이용하고 기능블록은 VHDL로 기술한다. TSK퍼지 추론의 연산은 전제부와 결론부를 병렬연산함으로써 고속처리를 구현하고 이에 필요한 제어부를 설계하였다. 또한 하드웨어 구현을 위해 실수연산을 이산화된 연산으로 바꾸고 이에 따른 나누기 연산자를 구현하였다.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.102-109
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• 1996

In this paper, we present a new approach for the automation of deburring process. An algorithm for teaching skills of a human expert to a robot manipulator is developed. This approach makes use of TSK fuzzy mode that can wxpress a highly nonlinear functional relation with small number of rules. Burr features such as height, width, area, grinding area are extracted from image processing by use of the vision system. Grinding depth, repetitive number and normal grinding force are chosen as control signals representing actions of the human expert. It is verified that our proposed fuzzy model can accurately express the skills of human experts for the deburring process.

• 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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• 2001.05a
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• pp.144-147
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• 2001

This paper presents adaptive fuzzy controller which is uncertainty or unknown variation in different parameters with nonlinear system of helicopter. The proposed adaptive fuzzy controller applied TSK(Takagi-Sugeno-Kang) fuzzy system which is not only low number of fuzzy rule, and a linear input-output equation with a constant term, but also can represent a large class of nonlinear system with good accuracy. The adaptive law was designed by using Lyapunov stability theory. The adaptive fuzzy controller is a model reference adaptive controller which can adjust the parameter $\theta$ so that the plant output tracks the reference model output. First of all, system of helicopter was considered as stopping state, and design of controller was simulated from dynamics equation with stopping state. Results show that it is controlled more successfully with a model reference adaptive controller than with a non-adaptive fuzzy controller when there is a modelling error between system and model or a continuous added noise in such unstable system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.536-543
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• 2004

In this paper, we proposed a new method of modeling a neuro-fuzzy system using a hybrid clustering algorithm. The initial parameters and the number of clusters of the proposed system are optimally chosen simultaneously with respect to the process of regression, which is a unique characteristics of the proposed system. The proposed algorithm presented in this work improves the overall performance of the proposed a neuro-fuzzy system by choosing a proper number of clusters adaptively according the characteristics of given data. The process of clustering is performed by deciding on the number of classes, which yields the property of convergence of the system. In experiments, the superiority of the proposed neuro-fuzzy system is demonstrated, especially the process of optimizing parameters and clustering of learning speed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• 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.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.4
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• pp.286-291
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• 1993

This paper presents a design method of fuzzy controller based on TSK fuzzy model. By using the proposed method, we can design fuzzy controller mathematically, which guarantees the stability of fuzzy system. We derived a theorem related to the stability of fuzzy system. In that theorem, we show that the fuzzy system has the same stable state transition matrix as we desire. The validity of the proposed method is shown through an experiment of DC motor velocity control.