• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.420-422
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• 1998

In this paper, we propose a position-speed control of servo system with a state estimator. And also we utilized two mass modelling in order to deals with real system accurately. The overall control system consists of two parts: the position-speed controller and state estimator. The Kalman filter applied as state - feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear,unbiased and minimun error variance recursive algorithm to estimate the unknown state optimally. Therefore we consider the error problem about the servo system modelling, the measurement noise at low-speed ranges a stochastic system, and implement a optimal state observer. Performance of the proposed state estimator are demonstrated by computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.87.1-87
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• 2001

For a plant subject to several kinds of disturbances in the plant input side, we consider a problem of designing a controller based on the disturbance cancellation. The conventional loop transfer recovery (LTR) technique can not be used since the extended system consisting of the plant and the disturbance model is not necessarily stabilizable. We propose a new LTR technique that can be applied for our problem. As a target of the LTR, we choose a state feedback controller using a disturbance estimator. We find an LTR procedure based on the Riccati equation formalism where the stochastic model contains the filter gain matrix of the disturbance estimator in the target. The procedure recovers the target feedback ...

• Progress in Superconductivity and Cryogenics
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• v.14 no.2
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• pp.12-15
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• 2012

This paper presents simulation and small-scale experimental tests of a fault current controller. Smart fault controller as proposed and proven conceptually in our previous work is promising technology for the smart power grid where distributed and even stochastic generation sources are prevalent and grid operations are more dynamic. Existing protection schemes simply limiting the fault current to the pre-determined set values may not show best performance and even lead to coordination failures, potentially leading to catastrophic failure. Thus, this paper designs fault current controller with a full bridge thyristor rectifier, embedding a superconducting coil for which the controller is electrically invisible during normal operation because the loss due to the coil is near-zero. When a fault occurs and the resulting current through the superconducting coil exceeds a certain value set intelligently based on the current operating condition of the grid, the magnitude of the fault current is controlled to this desired value by adjusting the firing angles of thyristors such that the overall system integrity is successfully maintained. Detailed time-domain simulations are performed and lab-scale testing circuits are built to demonstrate the desired functionality and efficacy of the proposed fault current controller.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.34-46
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• 2016

Networked Control System (NCS) has evolved in the past decade through the advances in communication technology. The problems involved in NCS are broadly classified into two categories namely network issues due to network and control performance due to system network. The network problems are related to bandwidth allocation, scheduling and network security, and the control problems deal with stability analysis and delay compensation. Various delays with variable length occur due to sharing a common network medium. Though most delays are very less and mostly neglected, the network induced delay is significant. It occurs when sensors, actuators, and controllers exchange data packet across the communication network. Networked induced delay arises from sensor to controller and controller to actuator. This paper presents an adaptive delay compensation process for efficient control. Though Smith predictor has been commonly used as dead time compensators, it is not adaptive to match with the stochastic behavior of network characteristics. Time delay adaptive compensation gives an effective control to solve dead time, and creates a virtual environment using the plant model and computed delay which is used to compensate the effect of delay. This approach is simulated using TrueTime simulator that is a Matlab Simulink based simulator facilitates co-simulation of controller task execution in real-time kernels, network transmissions and continuous plant dynamics for NCS. The simulation result is analyzed, and it is confirmed that this control provides good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1170-1178
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• 2013

Thanks to various benefits, low-cost real-time communication networks so called fieldbus have been widely used in many industrial applications including military systems, such as aircrafts, submarines, and robots. This paper presents a reliability analysis of dual-channel CAN(Controller Area Network) fieldbus which is used for controlling various equipment of submarine combat system. A submarine combat system playing a critical role to the success of missions and survivability consists of various devices including sensors/actuators and computers. Since a communication network for submarine combat system must satisfy an extremely high level of reliability, a dual channel technique is commonly adopted. In this paper, a Petri Net based reliability model for dual-channel CAN is discussed. A reliability model called generalized stochastic Petri Nets (GSPN) is built by utilizing the information on physical faults with CAN. The effectiveness of the proposed model is analyzed in terms of unreliability with respect to failure rate and repair rate.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.636-642
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• 2015

This paper presents an application of heuristic harmony search (HS) optimization algorithm for the identification of linear continuous time-delay system from step response. Identification model is first-order plus dead time (FOPDT), which describes a linear monotonic process quite well in most chemical processes and HAVC process and is often sufficient for PID controller tuning. This recently developed HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. The effectiveness of the identification method has been demonstrated through a number of simulation examples.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.6
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• pp.37-46
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• 2001

In this research, a controller design method based on optimization is proposed that can satisfy constraints on maximum responses of building structures subject to around excitation modeled by partially stochastic process. The class of controllers to be optimized is restricted to LQR. Weighting matrix on controlled outputs is used as design variable. Objective function, constraint functions and their gradients are computed by the parameterization of control gain with Riccati matrix. Full state feedback controllers designed by proposed optimization method satisfy various design objectives and their necessary maximum control forces are computed for the production of actuator. LQG controllers composed of Kalman filter and LQR designed by proposed method perform well with little deterioration. So it is possible to design output feedback controllers satisfying constraints on various maximum responses of structures.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.7
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• pp.38-50
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• 1997

This paper concerns a VHDL design and simulation of an accurate and cost-effective fuzzy logic controller (FLC). The accurcy of the proposed FLC is obtained by using the center of gravity (COG) defuzzifier that considers both membership values and spans of membership functions in calculating a crisp value. The cost-effectiveness of the proposed FLC is obtained by restructuring the conventional FLC in the following ways: Firstly, the MAX-MIN inference is inference is replaced by a read-modify-write operation that can be implemented economically in the structure of register files. Secondly, the division in the COG defuzzifier is avoided by finding the moment equilibrium point. The proposed COG defuzzifier has two disadvantages that it requires additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the mulitpliers with stochastic AND operations and the second disadvantage is alleviated by using a coarse-to-fine searching algorithm. The proposed FLC is described in VHDL structurally and behaviorally and whether it is working well or not is checked on SYNOPSYS VHDL simulator by using the truck backer-upper control problem.