• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권12호
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 한국통신학회논문지
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• 제19권11호
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• pp.2189-2198
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• 1994

본 논문에서는 퍼지 제어 시스템에 사용되는 퍼지 논리 제어기의 성능을 향상시키기 위한 여러가지 알고리즘들 중에서 학습기법에 속하는 퍼지 메타 규칙에 기초한 자기 동조 기법을 사용하여 직류 서보 전동기 제어를 위한 자기 동조 퍼지 논리 제어기를 구현해서, 자기 동조 퍼지 논리 제어기의 설계와 시뮬레이션 및 실험 결과를 고찰하고, 그 결과를 일반적인 퍼지 논리 제어기의 결과와 비교하여 자기 동조 퍼지 논리 제어기의 성능을 평가한다.

• 한국정밀공학회지
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• 제12권8호
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• pp.92-98
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• 1995

The position control of flexible joint manipulator is investigated by applying the self-organizing fuzzy logic controller (SOC) proposed by Procyk and Mamdani. The SOC is a heuristic rule-based controller and a further extension of an ordinary fuzzy controller, which has a hierachy structrue which consists of an algorithm being identical to a fuzzy controller at the lower ollp and a learning algorithm accomodating the performance evalution and rule modification function at the upper ollp. This form of control can be used in those complex systems which have been too difficult to control or which in the past have had to rely on the experience of a human operator. Even though the significant dynamic coupling of the motors and links on the flexible joint manipulator, the performance of command-following is good by applying the proposed SOC.

• 대한기계학회논문집A
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• 제22권3호
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• pp.663-670
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• 1998

Most of feedback shift controllers developed in the past have fixed control parameters tuned by experts using a trial and error method. Therefore, those controllers cannot satisfy the best control performance under various driving conditions. To improve the shift quality under various driving conditions, a new self-organizing controller(SOC) that has an optimal control performance through self-learning of driving conditions and driver's pattern is designed in this study. The proposed SOC algorithm for the shift controller uses simple descent method and has less calculation time than complex fuzzy relation, thus makes real-time control passible. PCSV (Pressure Control Solenoid Valve) control current is used as a control input, and turbine speed of the torque converter is used indirectly to monitor the transient torque as a feedback signal, which is more convenient to use and economic than the torque signal measured directoly by a torque sensor. The results of computer simulations show that an apparent reduction of shift-transient torque is obtained through the process of each run without initial fuzzy rules and a good control performance in the shift-transient torque is also obtained.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.439-443
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• 1996

A fuzzy teaming controller is experimentally designed to control the ball k beam system in this paper. Although most fuzzy controllers have been built just to emulate human decision-making behavior, it is necessary to construct the rule bases by using a learning method with self-improvement when it is difficult or impossible to get them only by expert's experience. The algorithm introduces a reference model to generate a desired output and minimizes a performance index function based on the error and error-rate using the gradient-decent method. In our balancing experiment of the ball ＆ beam system, this paper shows that the fuzzy control rules by learning are superior to the expert's experience.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.408-411
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• 1993

In this paper, we propose the Fuzzy Neural Controller with a Self-Organizing Map based on the fuzzy relation neuron. The fuzzy ndes expressing the input-output relation of the system are obtained by using the fuzzy relation neuron and updated automatically by means of the generalized delta rule. Also, the proposed method has a capability to express the knowledge acquired from the input-output data in form of fuzzy inferences rules. The learning algorithm of this fuzzy relation neuron is described. The effectiveness of the proposed fuzzy neural controller is illustrated by applying it to a number of test data sets.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1100-1106
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• 2004

In this report, a self-organizing fuzzy neural network is proposed to control chemical feeding, which is one of the most important problems in water treatment process. In the case of the learning according to raw water quality, the self-organizing fuzzy network, which can be driven by plant operator, is very effective, Simulation results of the proposed method using the data of water treatment plant show good performance. This algorithm is included to chemical feeder, which is composed of PLC, magnetic flow-meter and control valve, so the intelligent control of chemical feeding is realized.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.127-130
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• 1990

The fuzzy logic controller which can be applied to various industrial processes is quite often dependent on the heuristics of the experienced operator. The operator's knowledge is often uncertain. Therefore an incorrect control rule on the basis of the operator's information is a cause of bad performance of the system. This paper proposes a new self-learning fuzzy control method by the fuzzy system identification using the data pairs of input and output and arbitrary initial relation matrix. The position control of a DC servo motor model is simulated to verify the effectiveness of the proposed algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 B
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.