• 제어로봇시스템학회논문지
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• 제16권8호
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• pp.735-743
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• 2010

A new Fuzzy sliding mode controller is proposed to improve the cornering performance of the four wheel hybrid vehicles. The Fuzzy sliding mode control is applied for the control of rear motor and EHB (Electro-Hydraulic Brake) to improve the cornering performance. The modeling of the automobile is simplified that each of the two wheels is modeled as two degrees of freedom object and the friction coefficient between the wheel and the ground is assumed to be constant. The output of the Fuzzy sliding mode algorithm is the direct yaw moment for the rear wheels, which compensates for the slip angle. Through the simulations using ADAMS and MATLAB Simulink, the cornering performance of the proposed algorithm is compared to the conventional PID to show the superiority of the proposed algorithm. In the simulation experiments, the J-Turn and single lane change are used for each of the Fuzzy sliding mode algorithm and PID controller with the optimal gains which are tuned empirically.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권10호
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.244-249
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• 1998

In this paper, the hybrid state space self-tuning control technique Is studied within the framework of fuzzy systems and dual-rate sampling control theory. We show that fuzzy modeling techniques can be used to formulate chaotic dynamical systems. Then, we develop the hybrid state space self-tuning fuzzy control techniques with dual-rate sampling for digital control of chaotic systems. An equivalent fast-rate discrete-time state-space model of the continuous-time system is constructed by using fuzzy inference systems. To obtain the continuous-time optimal state feedback gains, the constructed discrete-time fuzzy system is converted into a continuous-time system. The developed optimal continuous-time control law is then convened into an equivalent slow-rate digital control law using the proposed digital redesign method. The proposed technique enables us to systematically and effective]y carry out framework for modeling and control of chaotic systems. The proposed method has been successfully applied for controlling the chaotic trajectories of Chua's circuit.

• 전자공학회논문지B
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• 제29B권10호
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• pp.59-68
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• 1992

In this paper, we proposed coordinator description and ambiguity on the hybrid controller for position/force control of robot manipulator. When the hybrid controller is desiged based on the PID control conception, the parameter sharing problem must be considered. However, selection problem of coordinate system on n-DOF robot manipulator control is unsolved. Moreover, contact force on object and change of shape make another problems. And it is very difficult to figure out the accurate mathematical model of manipulator on account of ambiguity and nonlinearity of actuator. Therfore, we design a new hybrid controller, FPID(Fuzzy PID). For verifying the validity of the controller, we tried computer simulation of this system. As a result, we can get remarkable improvement of overdamping and overshooting. Also we can solve compicance problem effectively. Furthermore, ambiguity problem is solved by adding control knowledge based compensator. So robust controller can be acheived, too.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.359-362
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• 1996

Our unicycle robot has simple mechanical structure. But unicycle's dynamical system is a very sensitive unstable system. Equation of motion of this simple unicycle robot was derived using Lagrange's method. In this paper a human inference control mechanism was established throughout an inquiry into hyman riding a unicycle, and we developed a hybrid controller to control our unicycle robot. Our controller is consisted with the PD and fuzzy controller containing fuzzy gain scheduling technique. Computer simulation shows good results.

• 전력전자학회논문지
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• 제5권4호
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• pp.352-357
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• 2000

벡터제어기법은 유도전동기의 고성능 운전을 위해 널리 사용되고 있다. 벡터제어기법을 사용해 전동기의 속도제어를 행할 정우 전동기의 속도나 전류를 제어하기 위해 주로 PI제어기가 사용되고 있다. 이 경우 유도전동기의 동 특성은 PI제어기의 이득과 밀접한 관계를 갖고 있으며 유도전동기의 고성능제어를 위해서는 PI제어기의 이득을 최적화 시킬 필요가 있다. 그러나 PI제어기의 이득을 최적화 시키기 위해서는 전동기제이 시스템의 등가모델을 정확히 알아야 하기 때문에 변동 부하조건하에서 일관성 있는 최적 이득값을 얻기란 대단히 힘들다. 본 논문에서는 이러한 PI제어기의 단점을 보완하기 위해 과도상태만을 제어하기 위한 간략화된 퍼지제어기와 정상상태 제어를 위한 기존의 PI제어기를 병렬로 구성한 하이브리드 제어기를 제안하고 이를 실제 유도전동기의 벡터제어에 적용하여 알고리즘의 타당성을 검증하였다.

• 조명전기설비학회논문지
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• 제21권5호
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• pp.1-9
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• 2007

본 논문은 SynRM의 동손 및 철손을 최소화 하는 효율 최적화 제어를 제시한다. 퍼지와 신경회로망으로 구성된 적응 퍼지-신경회로망 제어기를 바탕으로 한 속도 제어기를 설계한다. 특정 전동기 토크를 발생하는 d-q축 전류 조합은 무수히 많이 존재한다. 효율 최적화 제어기의 목적은 정상상태에 확실한 동작점에서 d-q축 전류 조합을 찾는 것이다. 제시된 알고리즘은 양호한 동적 토크 제어를 유지하는 동안 속도 및 토크 변화를 감소시키기 위하여 전자기적 손실은 허용한다. HAI 제어기의 제어 성능은 다양한 동작 상태에서 평가된다. 결과 분석은 제시된 알고리즘의 타당성을 보여준다.

• 산업기술연구
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• 제20권B호
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• pp.155-162
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance, FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and to increase efficiency. a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy PI and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and the resultant rule base is Macvicar-Whelan type. And the membership function is a Gaussian function. The frequency response information is used in tuning of the membership functions. Also a tuning strategy for the scaling factors is proposed based on the relationship between PID gain and the scaling factors. Simulation results show better performance and the effectiveness of the proposed method.

• International Journal of Automotive Technology
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• 제6권5호
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• pp.529-536
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• 2005

This paper presents a novel design of a fuzzy control strategy (FCS) based on torque distribution for parallel hybrid electric vehicles (HEVs). An empirical load-regulating vehicle operation strategy is developed on the basis of analysis of the components efficiency map data and the overall energy conversion efficiency. The aim of the strategy is to optimize the fuel economy and balance the battery state-of-charge (SOC), while satisfying the vehicle performance and drivability requirements. In order to accomplish this strategy, a fuzzy inference engine with a rule-base extracted from the empirical strategy is designed, which works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the driver torque request between the engine and the motor. Simulation results reveal that compared with the conventional strategy which uses precise threshold parameters the proposed FCS improves fuel economy as well as maintains better battery SOC within its operation range.