• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.697-699
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• 2000

In this paper, an extension of model predictive controller for nonlinear process using Takagi-Sugeno(TS) fuzzy model is proposed Since the consequent parts of TS fuzzy model comprise linear equations of input and output variables. it is locally linear, and the Generalized Predictive Control(GPC) technique which has been developed to control Linear Time Invariant(LTI) plants, can be extended as a parallel distributed controller. Also fuzzy soft constraints are introduced to handle both equality and inequality constraints in a unified form. So the traditional constrained GPC can be transferred to a standard fuzzy optimization problem. The proposed method conciliates the advantages of the fuzzy modeling with the advantages of the constrained predictive control, and the degree of freedom is increased in specifying the desired process behavior.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.134-138
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• 1996

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.272-279
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• 1997

In this paper, a fuzzy generalized predictive control (FGPC) for non-linear plants is proposed. In the proposed method, the receding horizon control is applied to the control part, while fuzzy systems are used for the predictor part. It is suggested that the fuzzy predictor is time-varying affine with respect to input variables for easy computation of control inputs. Since the receding horizon control can be obtained only with a predictor instead of a plant model, the fuzzy predictor is obtained directly from input-output data without identifying a plant model. A parameter estimation algorithm is used for identifying the fuzzy predictor. The control inputs of the FGPC are computed by minimizing a receding horizon cost function with predicted plant outputs. The proposed controller has a similar architecture to the generalized predictive control (GPC) except for the predictor synthesis method, and thus may possess inherent good properties of the GPC. Computer simulations show that the performance of the FGPC is satisfactory.

• Journal of Power Electronics
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• 제5권1호
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• pp.20-28
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• 2005

This paper presents a fuzzy predictive sliding mode control for high performance induction motor position drives. A new simplified inner-loop sliding-mode current control scheme based on a nonlinear mathematical model of an induction motor is introduced. Novel predictive fuzzy logic PI and PID controllers are used in speed and position loops, respectively. Sliding-mode current controllers and fuzzy predictive logic controllers are designed based on indirect vector control. The overall system performance is examined under different dynamic operating conditions. The performance of the drive system is robust and stable, and insensitive to parameters and operating condition variations even though non-exact system parameters are used in the implementation of the proposed controllers.

• 한국지능시스템학회논문지
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• 제6권2호
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• pp.97-105
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• 1996

본 논문에서는 퍼지모델을 이용한 비선형 공정의 적응모델예측제어가 제안된다. 모델예측제어의 저긍구조는 순환 퍼지모델링을 통해 구현된다. 사용된 퍼지모델의 후건부가 입, 출력 변수의 선형식이기 때문에, 전체 공정의 모델을 구하고 이를 이용하여 미래 공정출력을 구한 후 비용함수를 최로로하는 제어법칙은 일반형 예측제어(GPC)와 같은 형태가 된다. 제안된 적응 퍼지모델 예측제어는 퍼지모델이 가지는 본래적인 비선형성으로 인해 비선형공정을 우수한 성능으로 제어한다. 공정제어입력의 변화량을 출력값으로 하는 적응 퍼지모델 예측제어(AFMPC)인 경우, 상수의 기준입력에 대해 정상상태가 없고 매우 우수한 성능을 보인다. 제안된 제어구조의 특성 및 성은 비선형 공정의 모의 실험에 의해 검증한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.1068-1071
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• 1993

An algorithm of fuzzy predictive sliding control is proposed to design a jet engine control system. Sliding control using predictive scheme is adopted to compensate the time delay of fuel injector. Fuzzy rule-base is also introduced to adjust the command input for suppressing the surge. The potential of the proposed algorithm is shown through simulations utilizing a typical engine-only model.

• Smart Structures and Systems
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• 제31권1호
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• 전기학회논문지
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• 제66권9호
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• pp.1364-1372
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• 2017

In this paper, a problem of event-triggered model predictive control is investigated for continuous-time Takagi-Sugeno (T-S) fuzzy systems with input quantization. To efficiently utilize network resources, event-trigger is employed, which transmits limited signals satisfying the condition that the measurement of errors is over the ratio of a certain level. Considering sampling and quantization, continuous Takagi-Sugeno (T-S) fuzzy systems are regarded as a sector bounded continuous-time T-S fuzzy systems with input delay. Then, a model predictive controller (MPC) based on parallel distributed compensation (PDC) is designed to optimally stabilize the closed loop systems. The proposed MPC optimize the objective function over infinite horizon, which can be easily calculated and implemented solving linear matrix inequalities (LMIs) for every event-triggered time. The validity and effectiveness are shown that the event triggered MPC can stabilize well the systems with even smaller average sampling rate and limited actuator signal guaranteeing optimal performances through the numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.20-23
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• 1997

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult to apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.