• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

• Fourth Industrial Review
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• v.3 no.1
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• pp.13-20
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• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.84-86
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• 2005

Adaptive tracking, disturbance rejection and power system stabilizer. First, this paper deals with power system stabilization problem using asymptotic tracking of arbitrary smooth bounded reference output signals, with simultaneous rejection of disturbances generated by an unknown linear exosystem. Second, this paper presents a power system stabilizer(PSS) using nonlinear adaptive observer backstepping controller.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.124-132
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• 2004

A hydraulic system is difficult to keep the performance due to non-linearity, load pressure which changes according to working condition and system parameter variation, the requirement of control algorithm has been risen in order to satisfy them. An adaptive control is a control method which is suggested to achieve a control object though plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp, adaptive control can keep the characteristics of closed-loop system regularly. In this study GMVAC(generalized minimum variance adaptive control) combined with output error feedback is proposed in order to solve problems of non-minimum phase, vibration and overshoot in initial response of the plant. The control performance according to the variation of characteristics of the plant is evaluated by changing the supply pressure only.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.324-329
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• 1999

This paper proposes new torque control of an induction motor, which is robust against time verying parameters. The control is based on adaptive vector control with serial block adaptive algorithm. Motor parameters used to estimates slip frequency and torque. Frequency mismatch in the control system detrimentally affects slip frequency estimation and torque response. In order to compensate for degradation of the responses an adaptive identifier for the magnetizing inductance and the secondary time constand is introduced. adaptive vector control system consisted of two subsystems, a vector control system realized on synchronous frame and a parameter identification system on stationary frame. the effectiveness of the proposed method was verified by some digital simulations.

• 전기의세계
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• v.30 no.12
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• pp.822-831
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• 1981

Many different schemes of the adaptive observer and controller have been developed for both continuous and discrete systems. In this paper we have presented a new scheme of the reduced order adaptive observer for the single input discrete linear time invariant plant. The output equation of the plant, is transformed into the bilinear form in terms of system parameters and the states of the state variable filters. Using the plant output equation the discrete implicit adaptive observer based on the similar philosophy to Nuyan and Carroll is derived and the parameter adaptation algorithm is derived based on the exponentially weighted least square method. The adaptive model following control system is also constructed according to the proposed observer scheme. The proposed observer and controller are rather than simple structure and have a fast adaptive algorithm, so it may be expected that the scheme is suitable to the practical application of control system design. The effectiveness of the algorithm and structure is illustrated by the computer simulation of a third order system. The simulation results show that the convergence speed is proportinal to the increasing of weighting factor alpha, and that the full order and reduced order observer have similar convergence characteristics.

• Journal of Mechanical Science and Technology
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• v.18 no.7
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• pp.1150-1158
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• 2004

In this paper, we present an adaptive observer for nonlinear systems that include unknown constant parameters and are not necessarily observable. Sufficient conditions are given for a nonlinear system to be transformed by state-space change of coordinates into an adaptive observer canonical form. Once a nonlinear system is transformed into the proposed adaptive observer canonical form, an adaptive observer can be designed under the assumption that a certain system is strictly positive real. An illustrative example is included to show the effectiveness of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06b
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• pp.416-421
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• 2008

Self-adaptive 시스템에서 adaptation은 시스템 운영의 부담으로 작용할 수 있다. Self-adaptive system의 구조적인 고려는 이러한 부담을 줄일 수 있는 방안의 힌트로써 작용 할 수 있다. 본 논문에서는 self-adaptive 시스템을 구성할 때 가능한 소프트웨어 구조인 사용자 레벨 adaptation과 커널 레벨 adaptation을 살펴보고 각 구조에서 대상에 대한 adaptation을 수행할 때 발생하는 상호작용에 따른 비용을 추정하여 비교하였다. 특히 빠른 적응과 다양한 변화에 따른 적응이 요구되는 Networked Embedded Self-adaptive system을 대상으로 하였다. 본 논문은 Self-adaptive S/W 시스템을 설계할 때 기존의 기능적인 고려 외에 구조적인 고려가 필요함을 말하고자 하며 본 논문의 분석 결과는 Selfadaptive 시스템을 설계하고자 할 때 좋은 참고 자료가 될 수 있을 것이다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.101-106
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• 1997

This paper proposes a stable adaptive neural network control(NNC) for fixable joint manipulators. For designing the stable adaptive NNC, the flexible system dynamics is separated into fast and slow subdynamics according to singular perturbation concept. For the slow subdynamics, an adaptive NNC is designed to warrant the system stability and NN learning by lyapunov stability criterion. And to stabilize the fast dynamics, derivative control loop is installed. Through numerical simulation, the performance of the proposed NNC was compared to that of an adaptive controller designed based on the knowledge of the system dynamics. The proposed NNC shows much improvement over the conventional adaptive controller.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.379-385
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• 2020

The satellite employs an adaptive beamformer to efficiently detect various signals and to suppress multiple interference signals, simultaneously. Although the adaptive beamforming satellite system needs Angle-of-Arrival (AOA) information of the desired signal, it is difficult to estimate the signal AOAs on the satellite environment. However, the AOA estimation on the ground control tower is more efficient and accurate comparing to the satellite environment. In this paper, we propose an adaptive beamforming satellite system based on the signal location information on the ground, consisting on an angle estimator, an adaptive beamformer, and signal processing & D/B unit. The ground control tower estimates the accurate location of the signal source, and it sends the estimated coordinates of the desired signal to the satellite. The angle estimator mounted on the satellite calculates the desired signal AOA, based on the signal location information transmitted from the ground control center. The satellite beamformer detects the desired signal and suppresses unwanted signals based on the signal AOA calculated by the angle estimator. We provide computer simulation results to present the performance of the proposed satellite adaptive beamforming system based on the signal location information.