• Title/Summary/Keyword: Fuzzy Simulator

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Design of Sewage Treatment Process Simulator with the Aid of IG-based RBFNNs (정보입자기반 RBFNNs에 의한 하수처리공정 시뮬레이터의 설계)

  • Lee, Seung-Joo;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1958-1959
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    • 2011
  • RBFNNs(Radial Basis Function Neural Networks) 모델의 경우 Min-Max, HCM(Hard C-means)클러스터링 그리고 FCM(Fuzzy C-means)클러스터링 중 한가지를 통해 데이터 입자는 로드 규칙을 생성한 후 퍼지 공간을 분할 및 가우시안 함수의 정점을 정의한다. 본 논문은 기존의 방법과는 다르게 Min-Max와 FCM클러스터링을 혼합하여 로드의 규칙을 생성한 후 퍼지 공간을 분할 및 가우시안 함수의 정정을 정의하는 방법으로 사용하고자 한다. PSO최적화 알고리즘을 이용하여 같은조건에서 최적화한 기존의 방법으로 모델링된 RBFNNs와 Min-Max와 FCM 클러스터링을 혼합하여 사용한 방법의 비교를 통하여 어떤 모델의 성능이 더욱 좋은지 비교하고자 한다.

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Verification of a hybrid control approach for spacecraft attitude stabilization through hardware-in-the-loop simulation

  • Kim, Sung-Woo;Park, Sang-Young
    • Bulletin of the Korean Space Science Society
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    • 2011.04a
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    • pp.32.2-32.2
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    • 2011
  • State dependent Riccati equation (SDRE) control technique has been widely used in the control society. Although it solves nonlinear optimal control problems, which minimizes state error and control efforts simultaneously, it has drawbacks when it is to be applied to the real time systems in that it requires much computational efforts. So the real time system whose computational ability is limited (for example, satellites) cannot afford to use SDRE controller. To solve this problem, a hybrid controller which is based on MSDRE (Modified SDRE) and ANFIS (Adaptive Neuro-Fuzzy Inference System) has been proposed by Abdelrahman et al. (2010). We propose a hybrid controller based on SDRE and ANFIS, and apply the hybrid controller to the hardware attitude simulator to perform a HIL (Hardware-In-the-Loop) simulation. Through HIL simulation, it is demonstrated that the hybrid controller satisfies the control requirement and the computation load is reduced significantly. In addition, the effects of statistical properties of the ANFIS training data to the performance of the ANFIS controller have been analyzed.

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Development of neural network algorithm for an advanced distributed control system (고급 분산 제어시스템을 위한 신경 회로망 제어 알고리즘의 개발)

  • 이승준;박세화;박동조;김병국;변증남
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10a
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    • pp.953-958
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    • 1993
  • We develop a neural network control algorithm for the ACS (Advanced Control System). The ACS is an extended version of the DCS (Distributed Control System) to which functions of fault detection and diagnosis and advanced control algorithms are added such as neural networks, fuzzy logics, and so on. In spite of its usefulness proven by computer simulations, the neural network control algorithm, as far as we know, has no tool which makes it applicable to process control. It is necessary that the neural network controller should be turned into the function code for its application to the ACS. So we develop a general method to implement the neural network control systems for the ACS. By simulations using the simulator for the boiler of 'Seoul fire power plant unit 4', the methodology proposed in this paper is validated to have the applicability to process control.

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A Study on Intelligent Trajectrory Control for Prosthetic Arm using EMG Signals (근전도신호를 이용한 의수의 지능적 궤적제어에 관한 연구)

  • 장영건;권장우;홍승홍
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.7
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    • pp.1010-1024
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    • 1995
  • An intelligent trajectory control method that controls a direction and a average velocity for a prosthetic arm by force and direction estimations using EMG signals is proposed. 3 stage linear filters are used as a real time joint trajectory planner to minimize the impact to human body induced by arm motions and to reduce muscle fatigues. We use combination of MLP and fuzzy filter for a limb direction estimation and a time model of force for determining a cartesian trajectory control parameter. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. Simulation results of the proposed method show that the arm is effectively followed the desired trajectory by estimated foreces and directions. This method reduces the number of electrodes and attatched sites compared with the method using Hogan's impedance control.

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A Study on Intelligent Trajectory Control for Prosthetic Arm by Pattern Recognition & Force Estimation Using EMG Signals (근전도신호의 패턴인식 및 힘추정을 통한 의수의 지능적 궤적제어에 관한 연구)

  • 장영건;홍승홍
    • Journal of Biomedical Engineering Research
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    • v.15 no.4
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    • pp.455-464
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    • 1994
  • The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMG signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

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Scatternet Formation Algorithm based on Relative Neighborhood Graph

  • Cho, Chung-Ho;Son, Dong-Cheul;Kim, Chang-Suk
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.2
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    • pp.132-139
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    • 2008
  • This paper proposes a scatternet topology formation, self-healing, and self-routing path optimization algorithm based on Relative Neighborhood Graph. The performance of the algorithm using ns-2 and extensible Bluetooth simulator called blueware shows that even though RNG-FHR does not have superior performance, it is simpler and easier to implement in deploying the Ad-Hoc network in the distributed dynamic environments due to the exchange of fewer messages and the only dependency on local information. We realize that our proposed algorithm is more practicable in a reasonable size network than in a large scale.

Development of Fuzzy Model Simulator For Automation Fishing System (조업 자동화 시스템을 위한 퍼지 모델 시뮬레이터 개발)

  • Park, Keon-Kuk;Kim, Young-Bong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2018.10a
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    • pp.457-459
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    • 2018
  • 지난 해 롤스로이스의 무인 선박 개발 프로젝트(AAWA) 본격화 등 선박과 관련된 무인 자동화 기술 개발이 활발해지면서 국내의 R&D 사업이 많이 증가하였다. 특히 국내에서는 퍼지 모델을 이용한 RVC 지능 시스템 등 퍼지 이론을 사용한 기술들이 최근까지도 발표되고 있다. 퍼지 모델을 결정하기 위해선 해당 시스템에 대한 전문 지실뿐만이 아니라 다양한 환경에서의 반복적인 실험과 수정을 필요로 하기 때문에 시뮬레이터를 만들어 실험하게 되는데 다양한 환경에서의 반복적인 실험과 수정을 필요로 하기 때문에 시뮬레이터를 만들어 실험하게 되는데 대부분의 연구에서 시뮬레이터가 제작되는 비용과 시간에도 불구하고 해당 퍼지 모델을 위해서만 쓰이게 된다. 따라서 본 논문에서는 다양한 환경에서 퍼지 모델을 반복 실험할 수 있도록 시뮬레이터를 개발하였으며 기존의 퍼지 모델 일부를 본 시뮬레이터에 적용하여 같은 실험을 할 수 있음을 보이고 이를 통해 퍼지 모델을 만드는데 드는 시간과 비용을 줄일 수 있음을 보였다.

Fault Diagnosis of a Voltage-Fed PWM Inverter for a Three-parallel Power Conversion System in a Wind Turbine

  • Ko, Young-Jong;Lee, Kyo-Beum
    • Journal of Power Electronics
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    • v.10 no.6
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    • pp.686-693
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    • 2010
  • In this paper, a fault diagnosis method based on fuzzy logic for the three-parallel power converter in a wind turbine system is presented. The method can not only detect both open and short faults but can also identify faulty switching devices without additional voltage sensors or an analysis modeling of the system. The location of a faulty switch can be indicated by six-patterns of a stator current vector and the fault switching device detection is achieved by analyzing the current vector. A fault tolerant algorithm is also presented to maintain proper performance under faulty conditions. The reliability of the proposed fault detection technique has been proven by simulations and experiments with a 10kW simulator.

The detection and diagnosis model for small scale MSLB accident

  • Wang, Meng;Chen, Wenzhen
    • Nuclear Engineering and Technology
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    • v.53 no.10
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    • pp.3256-3263
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    • 2021
  • The main steam line break accident is an essential initiating event of the pressurized water reactor. In present work, the fuzzy set theory and the signal-based fault detection method has been used to detect the occurrence and diagnosis of the location and break area for the small scale MSLB. The models are validated by the AP1000 accident simulator based on MAAP5. From the test results it can be seen that the proposed approach has a rapid and proper response on accident detection and location diagnosis. The method proposed to evaluate the break area shows good performances for small scale MSLB with the relative deviation within ±3%.

A Study on Improving Accuracy of Subway Location Tracking using WiFi Fingerprinting (WiFi 핑거프린트를 이용한 지하철 위치 추적 정확성 향상을 위한 연구)

  • An, Taeki;Ahn, Chihyung;Nam, Myungwoo;Park, Jinhong;Lee, Youngseok
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.1
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    • pp.1-8
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    • 2016
  • In this study, an WiFi fingerprinting method based on the k-nn algorithm was applied to improve the accuracy of location tracking of a moving train on a platform and evaluate the performance to minimize the estimation error of location tracking. The data related to the position of the moving train are monitored by the control center for trains and used widely for the safety and comfort of passengers. The train location tracking methods based on WiFi installed by telecom companies were evaluated. In this study, a simulator was developed to consider the environments of two cases; in already installed WiFi devices and new installed WiFi devices. The developed simulator can simulate the localized estimation of the position under a variety of conditions, such as the number of WiFi devices, the area of platform and entry velocity of train. To apply location tracking algorithms, a k-nn algorithm and fuzzy k-nn algorithm were applied selectively according to the underlying condition and also four distance measurement algorithms were applied to compare the error of location tracking. In conclusion, the best method to estimate train location tracking is a combination of the k-nn algorithm and Minkoski distance measurement at a 0.5m grid unit and 8 WiFi AP installed.