• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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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.

• 한국산학기술학회논문지
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• 제17권2호
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• pp.325-333
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• 2016

본 연구는 솔더링 시스템에서 세라믹 인두기의 온도 제어를 위한 제어기 설계 알고리즘을 제안하고, 제어 실험으로 그 유효성을 보였다. 솔더링 시스템의 세라믹 인두기 온도 응답 특성이 매우 느리며 제어 입력에 비선형인 특성을 갖고 있어 정밀한 모델링과 제어기 설계에 어렵다. 본 연구에서는 전제부 변수가 제어 입력이고 결론부가 전달함수인 퍼지 규칙들로 구성된 TSK 퍼지 모델로 세라믹 인두기 온도 특성을 표현하였다. 퍼지 모델 결론부의 전달함수는 계단 입력 응답으로부터 구한다. 세라믹 인두기 온도 응답 특성이 매우 느리므로 완전한 계단 입력 응답을 구하기가 어렵다. 불완전한 계단 입력 응답으로 전달함수를 구하는 방법으로 유전적 알고리즘(GA)을 사용하는 것을 제안하며, 예제들로 제안한 그 유효성을 보였다. 또한 TSK 퍼지 모델로부터 퍼지 제어기를 설계하는 방법도 제안하며 그 유효성을 예제 시뮬레이션으로 확인하였다. 제안한 방법들을 세라믹 인두기 온도 제어에 적용하여 실험 하였다. 퍼지 모델의 규칙은 7개로 구성되었으며 퍼지 제어기의 결론부는 PI 제어기로 하였다. 제안한 퍼지 제어기의 실험 결과는 선형 제어기보다 우수하였으며 퍼지 PID 제어기를 사용한 기존 연구 결과에 못지않았다.

• 한국산학기술학회논문지
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• 제15권10호
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• pp.6289-6295
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• 2014

BGA 또는 SMD 형태를 갖는 반도체 칩을 인쇄회로 기판에 장착/제거 등의 수리작업에 사용되는 리워크 시스템은 작업 대상물의 손상을 줄이기 위해 열풍 토출구의 온도를 정밀하게 제어할 필요가 있다. 본 논문에서는 비선형 시스템인 리워크 시스템의 열풍 온도 제어를 위해 TSK 퍼지 규칙으로 구성되는 퍼지 PID 제어기 설계 방법을 제시한다. 먼저 제안하는 제어기의 설계 알고리즘을 제시하고, 리워크 시스템에 적용하여 제어기를 설계하는 과정을 보인다. 제안한 제어기의 성능을 확인하기 위하여 온도 제어를 실험한 결과, 제안 방법의 최소자승오차는 9.44로서 일반적으로 사용하는 PID 제어기를 사용한 경우의 오차인 15.88보다 설정온도에 잘 수렴함을 보였다.

• 제어로봇시스템학회논문지
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• 제17권2호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.52-57
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• 2006

In this paper, we have proposed a new adaptive fuzzy control algorithm based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter approximation error is proved. Unlike the existing feedback linearization based methods, the proposed algorithm can guarantee the global stability in the presence of the singularity in the inverse dynamics of the plant. The performance of the proposed algorithm is demonstrated through the problem of balancing and swing-up of an inverted pendulum on a cart.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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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.

• 동력기계공학회지
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• 제2권2호
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• pp.47-54
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• 1998

Underwater robotic vehicles(URVs) have been an important tool for various underwater tasks such as pipe-lining, data collection, hydrography mapping, construction, maintenance and repairing of undersea equipment, etc because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system is one of the most critical subsystems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. It is desirable to have an intelligent vehicle control system because the fixed-parameter linear controller such as PID may not be able to handle these changes promptly and result in poor performance. In this paper we described and analyzed a new type of fuzzy model-based controller which is designed for underwater robotic vehicles and based on Takagi-Sugeno-Kang(TSK) fuzzy model. The proposed fuzzy controller: 1) is a nonlinear controller, but a linear state feedback controller in the consequent of each local fuzzy control rule; 2) can guarantee the stability of the closed-loop fuzzy system; 3) is relatively easy to implement. Its good performance as well as its robustness to parameter changes will be shown and compared with those of the PID controller by simulation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.553-558
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• 1994

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 model that can express a highly nonlinear functional relation with small number of rules. Burr features such as height, width, area, cutting area are extracted from image processing by use of the vision system. Cutting depth, repeative number and normal cutting force are chosen as control signals representing actions of the human expert. It is verified that our processed fuzzy model can accurately express the skills of human experts for the deburring process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.425-427
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• 1997

In this paper, a new approach to modeling and control of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure. The mountain clustering technique is used in partitioning of system, and TSK rule structure is adopted to form the fuzzy rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators.