• Title/Summary/Keyword: communication controller

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Multi-Axes Robot Controller with CAN (CAN 통신을 이용한 다축 로봇 제어기)

  • Choi, Young-Seob;Chun, Kwang-Su;Lee, Dong-Hyun;Kim, Hak-Jin
    • Proceedings of the KIPE Conference
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    • 2007.07a
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    • pp.491-493
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    • 2007
  • This paper is suggesting the method to embody Multi-Axes robot controller by using CAN which has been the most popular industrial networks. The robot controller guarantees the efficiency and reliability by using CAN as a communication tool between upper robot control parts and lower control parts.

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Maximum Torque Control of SynRM using AFNIS(Adaptive Fuzzy Neuro Inference) (AFNIS를 이용한 SynRM의 최대토크 제어)

  • Jung, Byung-Jin;Ko, Jae-Sub;Choi, Jung-Sik;Jung, Chul-Ho;Kim, Do-Yeon;Chung, Dong-Hwa
    • Proceedings of the KIEE Conference
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    • 2008.04a
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    • pp.219-220
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    • 2008
  • The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AFNIS and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the AFNIS and ANN controller.

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Fuzzy-Neural Control for Speed Control and estimation of SPMSM drive (SPMSM 드라이브의 속도제어 및 추정을 위한 퍼지-뉴로 제어)

  • Nam Su-Myeong;Lee Jung-Chul;Lee Hong-Gyun;Lee Young-Sil;Park Bung-Sang;Chung Dong-Hwa
    • Proceedings of the KIEE Conference
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    • summer
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    • pp.1251-1253
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    • 2004
  • This paper is proposed a fuzzy neural network 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 neuro-fuzzy control(NFC) and estimation of speed 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.

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Security Monitoring System for Apartment House Building Using Paver Line Carrier (전력선 통신을 이용한 집단주택 안전관리 시스템)

  • Kim, In-Soo;Kim, Kwan-Ho;Kim, Yo-Hee
    • Proceedings of the KIEE Conference
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    • 1993.07a
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    • pp.476-478
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    • 1993
  • Instead of installing new communication wiring to each house in the apartment house building. the power distribution network. which is already installed in the building. can be use as communication medium. In a safty management, by adoption of power line communication system, at remote we can monitor safty related sensors such as fire, gas leakage, burglar intrusion and emergency call which are located at each house. From this viewpoint, we developed security monitoring system for apartment house building using power lines. Security monitoring system consists of Power Line Communication-Sub Controller (PLC-SC). Power Line Communication-Main Controller (PLC-MC) and Management System (MS). Between a PLC-MC and a PLC-SC, the transmission rate is 1200 bps in power lines and modulation technique is frequency shirt keying (FSK). In between a PLC-MC and a MS, the transmission rate is 1200 bps in communication line (RS-485). As a result of this research. transmission loss is 0.1dB per meter of intrabuilding distribution network. Transmission can be reach in 250 meters. So it is enough to communicate for security monitoring system in apartment house building.

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A Development of a wireless control module for PLC system (근거리 원격 자동화를 위한 무선 PLC 통신 모듈 개발)

  • 박종석;현웅근
    • Proceedings of the IEEK Conference
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    • 2000.06e
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    • pp.89-92
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    • 2000
  • A wire communication module for PLC controller was developed. This system consists of RF module interface part, RS232 serial communication control part, LED display part and control S/W with GUI interface. RF communication frequency is controlled by PLL controlling and communication rate is also controlled as 1200 and 4800 bps. Communication and control status are displayed on LED. As a S/W part, graphic user interface on Window 95 O.S. was developed. To show the validity of the developed system, severial communication and control of PLC system were experemented.

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A multiple PLC wireless communication module using master-slave control concept (Mastey-Slave 개념을 이용한 PLC용 다대다 통신 모듈 개발)

  • 박종석;현웅근;김이곤
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.48-48
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    • 2000
  • We developed a wireless communication system for multiple instrumentation system. The developed system has a function of remote instrumentation and PLC(Programmable Logic Controller) control. The system consists of CC(Control and Communication) protocol, wireless media access and physical media system. For multiple wireless communication, we propose a hub polling protocol based on master-slave control concept. To show the validity of the developed system, several experiments are illustrated.

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A Fuzzy Traffic Controller Considering the spillback on the Multiple Crossroads

  • Kim, Young-Sik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.6
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    • pp.722-728
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    • 2003
  • In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

Formation Control of Mobile Robots using Adaptive PID Controller (적응 PID 제어기를 이용한 이동로봇의 군집제어)

  • Park, Jin-Hyun;Choi, Young-Kiu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.11
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    • pp.2554-2561
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    • 2015
  • In this paper, we strengthen the advantages of a simple PID controller as a study on the formation control of mobile robots and propose an adaptive PID controller with robust performance at the dynamics characteristics of following robot. Simulation studies show that the adaptive PID controller has better keeping constant distance and angle such as tracking performance of following robot for the formation control than a conventional PID controller. This is the proposed adaptive PID controller to change the gains is found to represent the best performance. This is able to verify that the performance of the proposed adaptive PID controller is excellent.

Implementation of Integration Module of Vision and Motion Controller using Zynq (Zynq를 이용한 비전 및 모션 컨트롤러 통합모듈 구현)

  • Moon, Yong-Seon;Roh, Sang-Hyun;Lee, Young-Pil
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.1
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    • pp.159-164
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    • 2013
  • Recently the solution integrated of vision and motion controller which are important element in automatiomn system has been many developed. However typically such a solutions has a many case that integrated vision processing and motion control into network or organized two chip solution on one module. We implement one chip solution integrated into vision and motion controller using Zynq-7000 that is developed recently as extended processing platform. We also apply EtherCAT to motion control that is industrial Ethernet protocol which have compatibility for open standardization Ethernet in order to control of motion because EtherCAT has a secure to realtime control and can treat massive data.

Neural Network Based Guidance Control of a Mobile Robot

  • Jang, Pyoung-Soo;Jang, Eun-Soo;Jeon, Sang-Woon;Jung, Seul
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1099-1104
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    • 2003
  • In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.

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