• 제어로봇시스템학회논문지
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• 제9권11호
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• pp.963-968
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• 2003

A feedback control implementation for a high speed train pressurization system is proposed based on CAN (Controller Area Network). Firstly, system model including network latencies by CAN arbitration mechanisms is proposed, and an analytical compensation method of control parameters based on the system model is proposed for the network latencies. For the practical implementation of the control, global synchronization is adopted for controller to measure network latencies and to utilize them for the compensation of the control parameters. Simulation results are shown with practical tunnel data response. The proposed method is evaluated to be the most effective for the system through the control performances comparing among a controller not considering network latencies, other two off-line compensation methods, and the proposed method.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.433-438
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• 2012

In this paper, a operation network system equipped with onboard wireless communication systems and ground-based mission control systems is proposed for swarm flight of small UAVs. This operating system can be divided into two networks, UAV communication network and ground control system. The UAV communication network is intend to exchange the informations of navigation, mission and flight status with minimum time delay. The ground control system consisted of mission control systems and UDP network. Proposed operation network system can make a swarm flight of various UAVs, execute complex missions decentralizing mission to several UAVs and cooperte several missions. Finally, PILS environments are developed based on the total operating system.

• 유공압시스템학회논문집
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• 제8권4호
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• pp.17-23
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• 2011

Proportional control solenoid is a type of modulating valve that can continuously control the valve position with magnetic force of solenoid. Recent microcontroller based digital servocontroller for proportional valve is being developed toward the smart valve with additional features such as enhanced control algorithm for finer process and intelligent on-board diagnosis for maintenance. In this paper, development of servocontroller network control with CAN bus which is free from problems of security and network traffic jam is presented. Design of network control system includes modes of communication between master and slave, assignment of 29bit message identifier and message objects, transaction of communication sequence, etc. Monitoring function and control experiments for remote valve through CAN network prove the extended function of smart valve control system.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.414-421
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• 2007

This paper presents a engine/brake integrated VDC(Vehicle Dynamic Control) system using neural network algorithm methods for wheel slip and yaw rate control. For stable performance of vehicle, not only is the lateral motion control(wheel slip control) important but the yaw motion control of the vehicle is crucial. The proposed NNPI(Neural Network Proportional-Integral) controller operates at throttle angle to improve the performance of wheel slip. Also, the suggested NNPID controller performs at brake system to improve steering performance. The proposed controller consists of multi-hidden layer neural network structure and PID control strategy for self-learning of gain scheduling. Computer Simulation have been performed to verify the proposed neural network based control scheme of 17 dof vehicle dynamic model which is implemented in MATLAB Simulink.

• 제어로봇시스템학회논문지
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• 제6권10호
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• pp.911-917
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• 2000

As many sensors and actuators are used in various automated systems, the application of network to real-time distributed control system is gaining acceptance in many industries. In order to take advantages of networking, however, the network should be carefully designed to satisfy real-time distributed control. This paper presents an implementation method of closed-loop control using Profibus-FMS. In order to implement a closed-loop control system, we used industrial computers with Profibus-FMS network cards and a DC servo motor. Through various experiments, the step response of the control system with network was compared with the reference response without network.

• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.596-606
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• 2008

In this paper, we present a co-design methodology of dynamic optimal network-bandwidth allocation (ONBA) and adaptive control for networked control systems (NCSs) to optimize overall control performance and reduce total network-bandwidth usage. The proposed dynamic co-design strategy integrates adaptive feedback control with real-time scheduling. As part of this co-design methodology, a "closed-loop" ONBA algorithm for NCSs with communication constraints is presented. Network-bandwidth is dynamically assigned to each control loop according to the quality of performance (QoP) information of each control loop. As another part of the co-design methodology, a network quality of service (QoS)-adaptive control design approach is also presented. The idea is based on calculating new control values with reference to the network QoS parameters such as time delays and packet losses measured online. Simulation results show that this co-design approach significantly improves overall control performance and utilizes less bandwidth compared to static strategies.

• 전자공학회논문지SC
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• 제44권4호통권316호
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• pp.79-88
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• 2007

산업계에서 디바이스 제어 네트워크에 접속을 위한 기술과 인터넷을 통한 빌딩 자동화 시스템에 접속을 위한 기술의 수요가 커지고 있다. 이러한 기술에서는 디바이스 제어 네트워크와 인터넷 같은 데이터 네트워크의 통합과 광역 분산제어 시스템으로 조직화하느 것이 필요하며, 이는 VDN(virtual device network)라는 구조 하에서 실현될 수 있다 [1,2]. 디바이스 제어 네트워크와 데이터 네트워크의 내역은 그 응용의 차이 때문에 아주 다르다. 따라서 VDN의 구현을 위하여는 디바이스 제어 네트워크와 데이터 네트워크 간에 통신 프로토콜을 번역하고, 목적지로 정보를 효과적으로 송신할 수 있는 라우터가 필요하다. 이 논문은 VDN에 기반한 NCS(networked control system)의 개념을 제안하고 임베디드 시스템[3]을 이용하는 라우팅 알고리듬을 제안한다.

• 전자공학회논문지B
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• 제30B권1호
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• pp.79-89
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• 1993

This paper deals with a computer aided control of an adjustment process for the complete electronic devices by means of an application of artificial neural network and an implementation of neuro-controller for intelligent control. Multi-layer neural network model is employed as artificial neural network with the learning method of the error back propagation. Information initially available from real plant under control are the initial values of plant output, and the augmented plant input and its corresponding plant output at that time. For the intelligent control of adjustment process utilizing artificial neural network, the neural network emulator (NNE) and the neural network controller(NNC) are developed. The initial weights of each neural network are determined through off line learning for the given product and it is also employed to cope with environments of the another product by on line learning. Computer simulation, as well as the application to the real situation of proposed intelligent control system is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.260-266
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• 1996

In this paper, we presents neural network identification and control of highly complicated nonlinear Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Generally the LVAD system need to compensate nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with Neural Network Identification. Once the NNI has learned the dynamic model of LVAD system, the other network, called Neural Network Controller(NNC), is designed for control of a LVAD system. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated by computer simulation.