• Title/Summary/Keyword: NN controller

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Realization of Intelligence Controller Using Genetic Algorithm.Neural Network.Fuzzy Logic (유전알고리즘.신경회로망.퍼지논리가 결합된 지능제어기의 구현)

  • Lee Sang-Boo;Kim Hyung-Soo
    • Journal of Digital Contents Society
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    • v.2 no.1
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    • pp.51-61
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    • 2001
  • The FLC(Fuzzy Logic Controller) is stronger to the disturbance and has the excellent characteristic to the overshoot of the initialized value than the classical controller, and also can carry out the proper control being out of all relation to the mathematical model and parameter value of the system. But it has the restriction which can't adopt the environment changes of the control system because of generating the fuzzy control rule through an expert's experience and the fixed value of the once determined control rule, and also can't converge correctly to the desired value because of haying the minute error of the controller output value. Now there are many suggested methods to eliminate the minute error, we also suggest the GA-FNNIC(Genetic Algorithm Fuzzy Neural Network Intelligence Controller) combined FLC with NN(Neural Network) and GA(Genetic Algorithm). In this paper, we compare the suggested GA-FNNIC with FLC and analyze the output characteristics, convergence speed, overshoot and rising time. Finally we show that the GA-FNNIC converge correctly to the desirable value without any error.

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Performance Improvement of Controller using Fuzzy Inference Results of System Output (시스템 출력의 퍼지추론결과를 이용한 제어기의 성능 개선)

  • 이우영;최홍문
    • Journal of the Korean Institute of Intelligent Systems
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    • v.5 no.4
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    • pp.77-86
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    • 1995
  • The new architecture that fuzzy logic control(FLC) with difficulties for tuning membership function (MF) is parallel with neural networks(NN) to be learned from the output of FLC is proposed. Therefore proposed scheme has the characteristics to utilize the expert knowledge in design process, to be learned during the operation without any learning mode. In this architecture, the function of the FLC is to supply the sliding surface which is constructed on the phase plane by rule base for giving the desired control characteristics and learning criterion of NN and the stabilization of the control performance before NN is learned, The function of the NN is to let the system trajectory be tracked to the sliding surface and reached to the stable point.

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Design of Regulation Controller for Electromagnetic Suspension System Using Neural Network (NN을 이용한 자기부상 시스템에서의 레귤레이션 제어기 설계)

  • Jang, S.M.;Sung, S.Y.;Sung, S.K.;Jo, H.J.
    • Proceedings of the KIEE Conference
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    • 2000.07b
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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.

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A Design of Adaptive Controller based on Immune System (면역시스템에 기반한 적응제어기 설계에 관한 연구)

  • Lee Kwon Soon;Lee Young Jin
    • Journal of Institute of Control, Robotics and Systems
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    • v.10 no.12
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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.

A Study on the Load Frequency Control of 2-Area Power System Using Neural Network PID Controller (신경회로망 PID 제어기를 이용한 전력계통의 부하주파수제어에 관한 연구)

  • Chong, H.H.;Kim, S.H.;Joo, S.M.;Kim, K.H.;Yoo, J.Y.
    • Proceedings of the KIEE Conference
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    • 1997.07c
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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.

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A Study on design of Fuzzy neural network Intelligence controller using Evolution Programming (진화프로그래밍을 이용한 퍼지 신경망 지능 제어기 설계에 관한 연구)

  • 이상부;임영도
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1997.10a
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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.

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Speed Sensorless Control of Ultrasonic Motors Using Neural Network

  • Yoshida Tomohiro;Senjyu Tomonobu;Nakamura Mitsuru;Urasaki Naomitsu;Funabashi Toshihisa;Sekine Hideomi
    • Journal of Power Electronics
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    • v.6 no.1
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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.

Experimental Studies of Real- Time Decentralized Neural Network Control for an X-Y Table Robot

  • Cho, Hyun-Taek;Kim, Sung-Su;Jung, Seul
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.3
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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.

Torque Ripples Minimization of DTC IPMSM Drive for the EV Propulsion System using a Neural Network

  • Singh, Bhim;Jain, Pradeep;Mittal, A.P.;Gupta, J.R.P.
    • Journal of Power Electronics
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    • v.8 no.1
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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.

A Design of Two Degree of Freedom PID Controller for AGV using Immune Algorithm (면역 알고리즘을 이용한 AGV의 2자유도 PID조향 제어기 설계에 관한 연구)

  • 이창훈;이진우;이영진;이권순
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2002.03a
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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.

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