• 디지털콘텐츠학회 논문지
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• 제2권1호
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• pp.51-61
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• 2001

퍼지 제어기(FLC)는 고전적인 제어기 보다 외란에 강하고 초기치에 대한 과도응답도 우수할 뿐만 아니라 시스템의 수학적 모델과 파라메터 값을 알지 못하더라도 적절한 제어가 가능하다. 그러나 퍼지 제어기의 제어 규칙 생성은 전문가의 경험과 일단 결정된 제어 규칙은 고정됨으로 인해 제어 시스템의 환경변화에 적응할 수 없는 한계성이 있다. 또한 제어기의 출력값은 미세한 오차를 가지곤 있어 정확한 목표 값에 수렴할 수 없다. 이러한 미세한 오차를 없애기 위하여 여러 가지 방법이 연구되고 있는데, 본 논문에서는 FLC에 NN(Neural Network)과 GA(Genetic Algorithm)를 결합한 GA-FNNIC(유전알고리즘-퍼지 신경망 지능 제어기 : Genetic Algorithm - Fuzzy Neural network Intelligence Controller)를 제안한다. 제안된 GA-FNNIC와 FLC 제어기 간의 출력 특성, 수렴속도, 과도특성과 상승시간에 대해 비교 분석하고, 최종적으로 본 GA-FNNIC가 오차없이 목표치에 정확하게 수렴하는 것을 보인다.

• 한국지능시스템학회논문지
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• 제5권4호
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• pp.77-86
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• 1995

퍼지 제어기에 신경회로망을 병렬로 연결시키므로 제어성능 향상을 위해 필요했던 소속함수의 미세조정 과정이 학습으로 대신되게 하는 제어기 구조를 제안하였다. 신경회로망의 학습은 오차 역전파 알고리듬에 의해 수행되고 퍼지 제어기의 출력이 학습에 사용되는 오차량으로 사용된다. 따라서 본 제어기는 전문가의 경험과 지식을 제어기 설계에 이용할 수 있고, 별도의 학습과정 없이 제어과정 중에서 신경회로망 제어기가 학습되어 초기의 제어특성이 개선되어지는 특성이 있다. 그리고 본 구성에서 퍼지 제어기는 사용된 규칙에 의해 형성되는 위상평면상의 슬라이딩 면으로 필요한 제어특성과 신경회로망의 학습기준을 제시하는 한편 신경회로망이 학습되기전 제어 시스템의 제어특성이 안정되도록 하며, 신경회로망은 시스템의 상태궤적이 퍼지제어기에 의해 형성된 슬라이딩 면을 가능한한 근사하게 추종하도록 학습되어져 위상평면상 임의의 위치에 있는 시스템의 상태가 슬라이딩 면을 따라 안정점에 도달하도록 하게한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 B
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• pp.1408-1410
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• 2000

The regulation performances needs high control gain in novel output feedback controller but high control gain is decreased relative stability of the total system. Thus, this paper proposed neural network controller(NNC) for output feedback controller. In this scheme, output feedback controller are guarantee global stability and NNC are controller steady-state error and defined optimal control law. And we demonstrated this scheme by simulations.

• 제어로봇시스템학회논문지
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• 제10권12호
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• pp.1137-1147
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• 2004

In this paper, we proposed two types of adaptive control mechanism which is named HIA(Humoral Immune Algorithm) PID and CMIA(Cell-Mediated Immune Algorithm) controller based on biological immune system under engineering point of view. The HIA PID which has real time control scheme is focused on the humoral immunity and the latter which has the self-tuning mechanism is focused on the T-cell regulated immune response. To verify the performance of the proposed controller, some experiments for the control of AGV which is used for the port automation to carry container without human are performed. The experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. Moreover, in that results, proposed controllers have better performance than other conventional PID controller and intelligent control method which is the NN(neural network) PID controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1021-1024
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• 1997

This paper has presented a method for self-tuning tile PID controller using a BP method of multilayered NNs. The proposed controller employ input signal as a learning signal of PID control. The proposed controller is applied to load-frequency control of power system and it is investigated a dynamic characteristic. The simulation results shows that proposed NN STPID controller has the good dynamics responses against load disturbances.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.143-153
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• 1997

At the on-line control method FLC(Fuzzy Logic Controller) is stronger to the disturbance than a classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But to make the control rule of the fuzzy controller through an expert's experiance has a changes of the control system, the control rule is fixed, it can't adjust to the environment changes of the control system, the controller output value has a minute error and it can't convergence correctly to the desired value[1][2]. There are many ways to eliminate the minute error[3][4][5], but in this paper suggests EP-FNNIC(Fuzzy Neurla Network Intelligence Controller) intelligence controller which combines FLC with NN(Neural Network) and EP(Evolution Programming). The output characteristics of EP-FNNIC controller will be compared and analyzed with FLC. It will be showed that this EP-FN IC controller converge correctly to the desirable value without any error. The convergence speed, overshoot, rising time, error of steady state of controller of these two kinds also will be compared.

• Journal of Power Electronics
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• 제6권1호
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• pp.38-44
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• 2006

In this paper, a speed sensorless control for an ultrasonic motor (USM) using a neural network (NN) is presented. In the proposed method, rotor speed is estimated by a three-layer NN which adapts nonlinearities associated with load torque and motor temperature into control. The intrinsic properties of a USM, such as high torque for low speeds, high static torque, compact size, etc., offer great advantages for industrial applications. However, the speed property of a USM has strong nonlinear properties associated with motor temperature and load torque, which make accurate speed control difficult. These properties are considered in designing a control method through the application of mathematical models. In these strategies, a detailed speed model of the USM is required which makes actual applications impractical. In the proposed method, a three-layer NN estimates the speed of the USM from the drive frequency, the root mean square value of input voltage and the surface temperature of the USM, where no mechanical speed sensor is needed. The NN speed based estimator enables inclusion of variations in driving conditions due to input signals of the NN involved during the driving state of the USM. The disuse of sensors offers many advantages on both the cost and maintenance front. Moreover, the model free sensorless control method offers practical controller construction within a small number of parameters. To validate the proposed speed sensorless control method for a USM, experiments have been executed under several conditions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• Journal of Power Electronics
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• 제8권1호
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• pp.23-34
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• 2008

This paper deals with a Direct Torque Control (DTC) of an Interior Permanent Magnet Synchronous Motor (IPMSM) for the Electric Vehicle (EV) propulsion system using a Neural Network (NN). The Conventional DTC with optimized switching lookup table and three level torque controller generates relatively large torque ripples in an electric vehicle motor drive. For reducing the torque ripples, a three level torque controller is hereby replaced by the five level torque controller. Furthermore, the switching lookup table of the five level torque controller based DTC is replaced with a Neural Network. These DTC schemes of an IPMSM drive are simulated using MATLAB/SIMULINK. The simulated results are compared with the conventional DTC and it is found that the ripples in the torque, as well as in the stator current, are reduced drastically.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2002년도 춘계학술대회논문집
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• pp.229-234
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• 2002

Immune system is an evolutionary biological system to protect Innumerable foreign materials such as virus, germ cell, and et cetera. Immune algorithm is the modeling of this system'response that has adaptation and reliableness when disturbance occur. In this paper, immune algorithm controller was proposed to control four wheels steering(4ws) Automated Guided vehicle(AGV) in container yard. And then the simulation result was analysed and compared with the results of NN-PID controller.