• 전기학회논문지
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• 제61권3호
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• pp.442-450
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• 2012

Almost domestic power plants are being operated by foreign distributed control system. Many korean power plants are being operated over their lifetime so they need to be retrofitted. So we are developing the distributed control system to solve this problem by our own technique. The simulator was already made to verify the reliability of the algorithms. The unit loop function tests of all algorithms were finished in the actual distributed control system for installation of power plant and their results were satisfactory. The unit loop function tests are for each unit equipment algorithm. So the total operation tests will be made with all algorithms together in the actual distributed control system to be applied to power plant. When the verification through all tests is finished, algorithms with hardware will be scheduled to be installed and operated in the actual power plant. This research result will contribute to the safe operation of the deteriorated power plant and korean electric power supply as well as domestic technical progress. This entire processes and results for the development are written for the example of boiler feedwater master algorithm out of all algorithms in this paper.

• 전기학회논문지
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• 제60권1호
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• pp.1-7
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• 2011

Active power control scheme for distributed generation in microgrid consists of feeder flow control and unit power control. Feeder flow control is more useful than the unit power control for demand-side management, because microgrid can be treated as a dispatchable load at the point of common coupling(PCC). This paper presents detailed descriptions of the feeder flow control scheme for the hybrid system in microgrid. It is divided into three parts, namely, the setting of feeder flow reference range for stable hybrid system operation, feeder flow control algorithm depending on load change in microgrid and hysteresis control. Simulation results using the PSCAD/EMTDC are presented to validate the inverter control method for a feeder flow control mode. As a result, the feeder flow control algorithm for the hybrid system in microgrid is efficient for supplying continuously active power to customers without interruption.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2000년도 춘계학술대회 논문집
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• pp.292-299
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• 2000

An OBCS(on-board control system) which controls an operation of high speed train is a distributed control system which centrally supervises and controls a distributed system if independent units. A complicated process of design, implementation and test is needed to develop this system. The OBCS and its control algorithm are designed and verified by using the CASE Tool before it is implemented. Its functions are verified by the OBCS simulator. Hereafter, this verification process for Korean high-speed train being developed is presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.112-117
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• 1992

The Real-time Distributed Control Systems(RDCS) consist of several distributed control processes which share a network medium to exchange their data. Performance of feedback control loops in the RDCS is subject to the network-induced delays from sensor to controller and from controller to actuator. The network-induced delays are directly dependent upon the data sampling times of the control components which share a network medium. In this study, a scheduling algorithm of determining data sampling times is developed using the window concept, where the sampling data from the control components dynamically share a limited number of windows.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2030-2035
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• 2005

In this paper, we study the distributed coordination of a set of synchronous, anonymous, memoryless mobile robots that can freely move on a two-dimensional plane but are unable to communicate directly. Based on this model, we analyze the application problem that consists in having a group of robots form a barricade line to protect from car traffic a crowd of demonstrators parading on the street. For the sake of robustness, we privilege fully decentralized solutions to the problem. In particular, we give a self-stabilizing distributed algorithm to address the problem, in this presentation

• International Journal of Control, Automation, and Systems
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• 제1권3호
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• pp.358-367
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• 2003

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.

• International Journal of Control, Automation, and Systems
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• 제6권1호
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• pp.135-141
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• 2008

A new approach to $H_{\infty}$ multi-step prediction is developed by applying the innovation analysis theory. Although the predictor is derived by resorting to state augmentation, nevertheless, it is completely different from the previous works with state augmentation. The augmented state here is considered just as a theoretical mathematic tool for deriving the estimator. A distributed algorithm for the Riccati equation of the augmented system is presented. By using the reorganized innovation analysis, calculation of the estimator does not require any augmentation. A numerical example demonstrates the effect in reducing computing burden.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.120.2-120
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• 2002

In this paper auto-tuning scheme of weight function in the neural networks has been suggested by immune algorithm for nonlinear process. A number of structures of the neural networks are considered as learning methods for control system. A general view is provided that they are the special cases of either the membership functions or the modification of network structure in the neural networks. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also. It can provi..