• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 연구회 합동 학술발표회 논문집
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• pp.1-4
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• 1998

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of switched reluctance motor. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM. The results show that fuzzy-neural controller is suitable for wide speed range.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.563-566
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• 1991

This paper discusses the possibility of applying fuzzy logic controller in a microprocessor - based servomotor controller, such as servomotor position controller, which requires faster and more accurate response compared with other industrial processes. According to the fuzzy control rule made by tie analysis of error and error change, one Look-up table that contains various quantized step is made and appropriate initial error change is selected to the good responses.

• 전기학회논문지
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• 제58권8호
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• pp.1624-1632
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• 2009

In this study, we introduce the design methodology of an optimized Interval Type-2 fuzzy controller. The fixed MF design of type-1 based FLC leads to the difficulty of rule-based control design for representing the linguistically uncertain expression. In the Type-2 FLC as the expanded type of Type-1 FLC, we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. Type-2 FLC has a robust characteristic in the unknown system with unspecific noise when compared with Type-1 FLC. Through computer simulation as well as practical experiment, we compare their performance by applying both the optimized Type-1 and Type-2 fuzzy cascade controllers to ball and beam system. To evaluate each controller performance, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time and steady-state error.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.80-83
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• 2003

In this note, a separation principle of the Takagi-Sugeno (T-S) fuzzy-model-based controller/observer is investigated. The separation principle of T-S fuzzy-model-based controller/observer sharing the premise parts in the fuzzy rule with directly measurable premise variables is well established. In that case, the fact that the augmented observer-based control system has the eigenvalues of the sub-closed-loop control system by the state-feedback controller and the sub-closed-loop observer error system is used to prove the separation principle. This paper studies the separation principle of T-S fuzzy-model-based controller/observer in which the premise variables cannot be directly measurable.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• Transactions on Control, Automation and Systems Engineering
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• 제3권3호
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.320-325
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• 1998

Underwater robotic vehicles (URVs) have been an important tool for various underwater tasks because they have greater speed, endurance, depth capability, and safety than human divers. As the use of such vehicles increases, the vehicle control system becomes one of the most critical subsytems to increase autonomy of the vehicle. The vehicle dynamics are nonlinear and their hydrodynamic coefficients are often difficult to estimate accurately. In this paper a new type of fuzzy model-based controller based on Takagi-Sugeno-Kang fuzzy model is designed and applied to the control of of an underwater robotic vehicle. 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 the change of parameters have been shown and compared with the re ults of conventional linear controller by simulation.

• Journal of Electrical Engineering and Technology
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• 제6권5호
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• pp.595-604
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• 2011

A three-phase three-level shunt active filter controlled by fuzzy logic current controller which can compensate current harmonics generated by nonlinear loads is presented. Three-level inverters and fuzzy controllers have been successfully employed in several power electronic applications these past years. To improve the conventional pwm controller performance, a new control scheme based on fuzzy current controller is adopted for three-level (NPC) shunt active filter. The scheme is designed to improve compensation capability of APF by adjusting the current error using a fuzzy rule. The inverter current reference signals required to compensate harmonic currents use the synchronous reference detection method. This technique is easy to implement and achieves good results. To maintain the dc voltage across capacitor constant and reduce inverter losses, a proportional integral voltage controller is used. The simulation of global system control and power circuits is performed using Matlab-Simulink and SimPowerSystem toolbox. The results obtained in transient and steady states under various operating conditions show the effectiveness of the proposed shunt active filter based on fuzzy current controller compared to the conventional scheme.

• 전기학회논문지
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• 제60권10호
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• pp.1978-1983
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• 2011

BLDC motor is used in a wide variety of industrial and servo applications. Its features and advantages mainly consist in high value of torque/inertia ratio, high efficiency with speed range and high dynamic performance. This paper deals with the speed control of a trapezoidal type brushless DC motor using Fuzzy PI controller. The conventional PI controller has been widely used in industrial applications. If we select a optimal PI control gain, the PI controller shows very good control performance. But it is very difficult to find the optimal PI control gain. Fuzzy control does not need any model of plant and is basically adaptive and gives robust performance for plant parameter variation. Therefore the combinations of conventional PI controller and fuzzy controller seem to be very effective. This paper deals with PI controller with 4-rule based fuzzy controller. The proposed fuzzy PI controller increases the control performance of the conventional PI controller. Simulation and experimental results show that fuzzy PI controller has a good robustness regarding the improper tuned PI controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.238-242
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• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.