• Journal of Advanced Marine Engineering and Technology
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• v.29 no.3
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• pp.251-259
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• 2005

A discrete time model reference adaptive control has been applied in order to compensate the nonlinear friction characteristics in a hydraulic proportional position control system. As nonlinear friction, static and coulomb friction forces are considered and modeled as dead zone and external disturbance respectively. The model reference adaptive control system consists of a cascade combination of the dead zone. external disturbance and linear dynamic block. For adaptive control experiment. the DSP(Digital Signal Processor) board has been interfaced the hydraulic proportional position control system. The experimental results show that the MRAC(Model Reference Adaptive Control) for compensation of static and coulomb friction are very effective.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.57-62
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• 2004

This paper deals with a waypoint trajectory following problem for the tilt-rotor UAV under development in Korea (TR-KUAV). In this problem, dynamic model inversion based on the linearized model and Sigma-Phi neural network with adaptive weight update are involved to realize the waypoint following algorithm for the vehicle in the helicopter flight mode (nacelle angle=0 deg). This algorithms consists of two main parts: outer-loop system as a command generator and inner-loop system as stabilizing controller. In this waypoint following problem, the position information in the inertial axis is given to the outer-loop system. From this information, Attitude Command/Attitude Hold logic in the longitudinal channel and Rate Command/Attitude Hold logic in the lateral channel are realized in the inner-loop part of the overall structure of the waypoint following algorithm. The nonlinear simulation based on the TR-KUAV is carried out to evaluate the stability and performance of the algorithm. From the numerical simulation results, the algorithm shows very good tracking performance of passing the waypoints given. Especially, it is observed that ACAH/RCAH logic in the inner-loop has the satisfactory performance due to adaptive neural network in spite of the model error coming from the linear model based inversion.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.198-198
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• 1991

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.12-18
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• 2011

Signal fading due to rain is one of the most significant factors degrading link quality in satellite communication systems. Adaptive transmission is considered to be the most efficient means to countermeasure the rain attenuation. In order to develop and design a good adaptive transmission system, we need a dynamic rain attenuation model which can synthesize time series of rain attenuation. In this paper, we present a modeling technique for dynamic rain attenuation using a Markov process. We derive statistical fading properties of the rain attenuation data measured in second time interval and define four states in the Markov process. We synthesize the rain attenuation data using the 4-state Markov process, and compare statistical properties of the simulated data to those of the measured data.

• Journal of KIISE
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• v.44 no.8
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• pp.842-853
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• 2017

Streaming data is a sequence of data samples that are consistently generated over time. The data distribution or concept can change over time, and this change becomes a factor to reduce the performance of a classification model. Adaptive incremental learning can maintain the classification performance by updating the current classification model with the weight adjusted according to the degree of concept drift. However, selecting the proper weight value depending on the degree of concept drift is difficult. In this paper, we propose a dynamic ensemble method based on adaptive weight adjustment according to the degree of concept drift. Experimental results demonstrate that the proposed method shows higher performance than the other compared methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.323-326
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• 1996

In a simple system, the control schemes work well provided that the characteristic of the plant or the coefficients are known and fixed. But the condition is not met in the system like satellite, for example, varying over time and the coefficients of dynamic system change due to disturbance, etc, and the better precise model is required to control the given dynamic system well. Conversely, the fixed controller make the unmodel dynamic system with a wide class of modelling error be stable within the error tolerance limits. Also, a robust model reference adaptive control scheme is designed for the plant, paying attention to the derivation of the appropriate parametric model and the design of the normalizing signal to guarantee that it has the desired properties.

• Journal of KIISE
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• v.43 no.7
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• pp.801-811
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• 2016

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.163-172
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• 2004

Nowadays, to analyze the dynamic characteristics of the automotive driving system, the computer simulation linked up with VR(Virtual Reality) technology is treated as the useful method with the improvement of computing ability. In this paper, the VR simulation system has been developed to investigate the driving characteristics of the ASV(Advanced Safety Vehicle) equipped with an ACC(Adaptive Cruise Control) system. For the purpose, VR environment which generates 3D graphic and sound information of the vehicle, the road, the facilities, and the terrain has been organized for the driving reality. Mathematical models of vehicle dynamic analysis including the ACC model have been constructed for computer simulation. The ACC modulates the throttle and brake functions to regulate the vehicle speed so that vehicles could keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamic simulation with the graphic rendering. With the developed VR simulation system, simple scenarios are applied to analyze the dynamic characteristics. It is shown that the VR simulation system could be useful to evaluate the adaptive cruise controlled vehicle on various driving conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8B
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• pp.691-698
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• 2003

Throughput response characteristics depending on the network bandwidth allocation need to be modeled to devise adaptive control mechanism to support QoS of the network. Thus, two models are proposed in this study. The first one is a dynamic system model and the other one is a stochastic model. The dynamic system model is developed to represent dynamic characteristics of the network and the stochastic model is developed to represent distribution of measured throughput data. An optimization technique is used for decision of proposed model's factor. The result confirms that the characteristics of proposed models are similar with actual network's characteristics.

• Smart Structures and Systems
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• v.11 no.3
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• pp.315-329
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• 2013

The construction of an experimental nonlinear structural model with little cost and unlimited repeatability for vibration control study represents a challenging task, especially for material nonlinearity. This paper reports the design, analysis and vibration control of a nonlinear structural experiment platform with adjustable hinges. In our approach, magnetorheological rotary brakes are substituted for the joints of a frame structure to simulate the nonlinear material behaviors of plastic hinges. For vibration control, a separate magnetorheological damper was employed to provide semi-active damping force to the nonlinear structure. A dynamic neural network was designed as a state observer to enable the feedback based semi-active vibration control. Based on the dynamic neural network observer, an adaptive fuzzy sliding mode based output control was developed for the magnetorheological damper to suppress the vibrations of the structure. The performance of the intelligent control algorithm was studied by subjecting the structure to shake table experiments. Experimental results show that the magnetorheological rotary brake can simulate the nonlinearity of the structural model with good repeatability. Moreover, different nonlinear behaviors can be achieved by controlling the input voltage of magnetorheological rotary damper. Different levels of nonlinearity in the vibration response of the structure can be achieved with the above adaptive fuzzy sliding mode control algorithm using a dynamic neural network observer.