• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.11
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• pp.753-763
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• 2015

This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1170-1176
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• 2010

This study addresses adaptive control of UAVs(Unmanned Aerial Vehicles) pitch-axis maneuver. The MRAC(Model Referenced Adaptive Control) approach is employed to accommodate uncertainties which are introduced by feedback linearization of pitch attitude control by elevator input. The model uncertainty is handled by adaptation laws which update model parameters while the UAV is under control by the feedback control law. Steady-state pitch attitude achieved by the stabilizing control law is derived to provide insight on the closed-loop behavior of the controlled system. The proposed idea is free of linearization, gain-scheduling procedures, so that one can design high maneuverability of UAVs for pitching motion in the presence of significant model uncertainty.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.280-286
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• 2004

The widespread use of the Internet and the maturing of digital video technology have led to an increase in various streaming media application. However, new classes of hosts such as mobile devices are gaining popularity, while the transmission became more heterogeneous. Due to the characteristics of mobile networks such as low speed, high error bit rate, etc., the applications over the wireless channel have different needs and limitations from desktop computers. An intermediary between two communicating endpoints to hide the heterogeneous network links is thought as one of the best approaches. In this paper, we adopted the concept of inter-packet gap and the sequence number between continuously received packets as the error discriminator, and designed an adaptive packet sizing mechanism to improve the network efficiency under varying channel conditions. Based on the proposed mechanism, the packetization scheme with error protection is proposed to scalable encoded video delivery. Finally, simulation results reveal that our proposed mechanism can react to the varying BER conditions with better network efficiency and gain the obvious improvement to video quality for stored MPEG-4 FGS video delivery.

• Journal of Animal Environmental Science
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• v.20 no.4
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• pp.173-176
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• 2014

This study was conducted to investigate the effect of weaning period on the vocalization frequency of Hanwoo calf. Twelve Hanwoo calves were allocated into 4 groups, control (forced weaning on 90days) and treatment (weaning on 70, 90 and 120days with 5 adaptation days). After weaning, behavior and vocalization of Hanwoo calves were recorded on 3 consecutive days with closed circuit television (DTC-R5254, Digite Co., Ltd., Korea) and digital audio tape recorder (SR-900, Idamtech Co., Ltd., Korea). Vocalization frequency of Hanwoo calf were not significance difference with control and treatment group. Thus, additional studies of feed intake and body weight gain were needed to determine the weaning period of Hanwoo calves.

• Journal of Family Resource Management and Policy Review
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• v.17 no.2
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• pp.121-145
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• 2013

The purpose of this study is to understand married female immigrants' life adjustment process in Korea by explaining the resources to which they have access and how they use them. The data were collected through in-depth interviews with ten female participants who have more than one child, have participated in programs of the multicultural family support center, have work experience, can communicate with Koreans, and live in Daegu. The major findings are as follows. The participants' personal resources differed. English language skills were very useful resources for making money and for earning the respect of family members and others. However, the participants without English language skills had sincerely and actively tried to learn the Korean language and gain bilingual competence. The participants obtained diverse family resources from their husbands and parents-in-law after adapting themselves to perform their gender role. Further, the participants used the social resources offered by public support systems as a starting point for learning the Korean language in their early adaptation process, and formed personal networks with staff members at the multicultural family support center. The results show that the participants used many kinds of resources for acculturation by interacting positively with their environment. Moreover, the resources from diverse levels of their environments affected their acculturation process.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9A
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• pp.889-895
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• 2010

In this paper, we have studied the implementation and adaptation of a smart antenna system for mobile terminals. We have designed a smart antenna system with switching beam structure in order to reduce the hardware and computational complexity. Additionally we have analyzed the reduction of the effect of multipath fading due to beamforming using real measurement data from commercial CDMA cellular channel environments. After analyzing the measurement data, we found out that the effect of fading reduces by ~3dB due to the effect of $2{\times}2$ beamforming in mobile terminals with 6dB beamforming gain.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.39-46
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• 2005

In this paper, adaptive higher harmonic control technique using Neural Networks (NN) is proposed. First, linear transfer function is estimated to relate the input harmonics and output harmonics, then NN which has the universal function approximation property is applied to expand application range of the transfer function. Optimal control gain matrix computed from the transfer function is used to train NN weights. Online weight adaptation laws are derived from Lyapunov's direct method to guarantee internal stability. Results of the simulation of 6-input 2-output nonlinear system show that adaptive HHC is applicable to the system with uncertain transfer function.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.