• Transactions on Control, Automation and Systems Engineering
• /
• 제2권2호
• /
• pp.136-139
• /
• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• 한국공작기계학회:학술대회논문집
• /
• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
• /
• pp.135-139
• /
• 2000

This research proposes a nonlinear sliding mode control. The sliding mode control is designed according to Lyapunov function. The equivalent control term is estimated by neural network. To estimate the unknown part in the control law in on-line fashion, A recurrent neural network is given as on-line estimator. The stability of the control system is guaranteed owing to the on-line learning ability of the recurrent neural network. It is certificated through simulation results to be applied to nonlinear system that the function approximation and the proposed control scheme is very effective.

• 한국통신학회논문지
• /
• 제37B권10호
• /
• pp.877-888
• /
• 2012

네트워크 가상화 기술은 NaaS (Network as a Service) 또는 SDN (Software Defined Network)으로 불리는 서비스 지향 아키텍처를 추구하는 미래인터넷에서 중요하게 다루고 있는 연구 분야이다. 네트워크 가상화는 혁신적인 프로토콜들에 대한 상호 독립적인 시험이 가능한 네트워크 테스트베드 구축 기술로서 미래인터넷 연구에서 중요한 역할을 할 것으로 기대하고 있다. 본 논문에서는 단일 도메인의 중소규모 연구망에서 네트워크 가상화를 통해 사용자가 정의하는 토폴로지와 대역폭을 제공하여 다중 사용자의 서비스 트래픽들을 분리, 관리함을 목적으로 하는 제어프레임워크를 제안한다. 본 프레임워크(VIMS; Virtual network Integrated control and Management System)는 이기종의 가상네트워크 장비 제어평면을 수용함으로써 장비에 변경을 요하지 않고 확장할 수 있는 구조를 지닌다. KOREN (Korea advanced REsearch Network)에 적용, 실현 가능성을 확인하였으며 GENI의 제어프레임워크와 비교를 통해 본 프레임워크와의 차이점과 개선을 위한 향후 연구 방향을 도출한다.

• 한국공작기계학회논문집
• /
• 제15권5호
• /
• pp.114-122
• /
• 2006

This paper presents a new control scheme that combines a sliding mode control and a neural network. In the proposed sliding mode control, a continuous control is employed removing the switching phenomena and the equivalent control within the boundary layer is estimated through on-line teaming of the neural network. The performances of the proposed control are compared with off-line neural network and on-line neural sliding mode control by computer simulation. The simulation results show that the proposed control reduces high frequency chattering and tracking error in example of the two link manipulator.

• International Journal of Control, Automation, and Systems
• /
• 제5권5호
• /
• pp.515-525
• /
• 2007

In this paper, we develop a co-design methodology of stochastic optimal controllers and network parameters that optimizes the overall quality of control (QoC) in networked control systems (NCSs). A new dynamic model for NCSs is provided. The relationship between the system stability and performance and the sampling frequency is investigated, and the analysis of co-design of control and network parameters is presented to determine the working range of the sampling frequency in an NCS. This optimal sampling frequency range is derived based on the system dynamics and the network characteristics such as data rate, time-delay upper bound, data-packet size, and device processing time. With the optimal sampling frequency, stochastic optimal controllers are designed to improve the overall QoC in an NCS. This co-design methodology is a useful rule of thumb to choose the network and control parameters for NCS implementation. The feasibility and effectiveness of this co-design methodology is verified experimentally by our NCS test bed, a ball magnetic-levitation (maglev) system.

• 한국항해항만학회지
• /
• 제31권4호
• /
• pp.289-293
• /
• 2007

In this paper, new application of adaptive neural network to design a ship's Rudder-Roll Damping(RRD) control system is presented Firstly, the ANNAI neural network controller is presented. Secondly, new RRD control system using this neural network approach is developed. It uses two neural network controllers for heading control and roll damping control separately. Finally, Computer simulation of this RRD control system is carried out to compare with a linear quadratic optimal RRD control system; discussions and conclusions are provided. The simulation results show the feasibility of using ANNAI controller for RRD. Also, the necessity of mathematical ship model in designing RRD control system is removed by using NN control technique.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.1478-1481
• /
• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network 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 analysis results to verify the effectiveness of the new method.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2001년도 춘계학술대회 논문집
• /
• pp.582-585
• /
• 2001

Requirements for device networks differ greatly from those of data(business) networks. Consequently, any control network technology which uses a fieldbus protocol is, in general, different from IP network protocol TCP/IP. One needs to integrate fieldbus protocol and TCP/IP to realize distributed control over IP network or internet. This paper suggests a basic concept that can be applied to distributed control over IP network or internet. Specifically, LonWorks technology that uses LonTalk protocol is reviewed as device network. LonWorks technology provides networked intelligent I/O and controllers which make it a powerful, expandable solution. It is also addressed that many hardwired PLCs can be replaced by LonWorks devices. Connecting these remote LonWorks networks to the Internet can provide a powerful, integrated, distributed control system.

• 제어로봇시스템학회논문지
• /
• 제2권3호
• /
• pp.142-147
• /
• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제7권2호
• /
• pp.127-131
• /
• 2007

The control of electrical appliances residing in the home network can be accomplished via Internet with the UPnP expansion without modifying an existing UPnP. In this paper, we propose the Internet Gateway that consists of an UPnP IGD(Internet Gateway Device) DCP(Device Control Protocol) and an UPnP Bridge as a system to control electrical appliances of home network. UPnP IGD DCP is to enable the configurable initiation and sharing of Internet connections as well as assuring advanced connection-management features and management of host configuration service. It also supports transparent Internet access by non-UPnP-certified devices. UPnP Bridge searches for local home network devices by sending control messages, while control point of UPnP Bridge looks up devices of interest on the Internet, subsequently furnishing the inter-networking controlling among devices which belong to different home network systems. With our approach, devices on one home network can control home electrical appliances on the other home network via Internet through IGD DCP with control commands of UPnP.