• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.267-271
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• 1996

In this study, performance improvement of the inverse modeling as the on-line control method for the estimation, control experiment is performed. As the modeling errors is occurred in duct system arbitrarily, a case using the filtered-x LMS algorithm only as the control method, a case using tile inverse modeling method only and a case using the inverse modeling with the adaptive line enhancer are compared. The estimation errors between real secondary path transfer functions and the estimated and the control performances of primary noises with these estimated transfer functions are compared.

• International Journal of Aeronautical and Space Sciences
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• 제17권2호
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• pp.214-221
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• 2016

An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

• 한국통신학회논문지
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• 제40권7호
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• pp.1293-1295
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• 2015

본 논문은 LTE-A 시스템과 같은 상향링크 다중사용자 환경에서 시변채널을 적절한 복잡도와 좋은 성능으로 추정할 수 있는 적응형 다중안테나 채널추정 기법을 제안하였다. 제안하는 채널추정기가 다양한 시변채널 환경 및 트래픽 패턴에서도 좋은 채널 트래킹 성능을 제공할 수 있음을 보인다.

• 전기학회논문지
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• 제58권11호
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• pp.2299-2301
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• 2009

The adaptive IIR filters for active noise control systems are more effective when acoustic feedback exists, but the adaptive IIR filters could be unstable when the filter algorithm is not yet converged. In this paper, auto-secondary path estimation techniques and a stabilizing process for adaptive Multi-Channel Recursive LMS (MCRLMS) filters are developed to improve the stability of multi-channel active noise control systems. Experiments using a TMS320VC33 digital signal processor in a three dimensional enclosure have performed to show the effectiveness of the proposed algorithm.

• Communications for Statistical Applications and Methods
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• 제28권3호
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• pp.281-294
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• 2021

In this paper, we propose an adaptive regression method based on the single-layer neural network structure. We adopt a symmetric activation function as units of the structure. The activation function has a flexibility of its form with a parametrization and has a localizing property that is useful to improve the quality of estimation. In order to provide a spatially adaptive estimator, we regularize coefficients of the activation functions via ℓ₁-penalization, through which the activation functions to be regarded as unnecessary are removed. In implementation, an efficient coordinate descent algorithm is applied for the proposed estimator. To obtain the stable results of estimation, we present an initialization scheme suited for our structure. Model selection procedure based on the Akaike information criterion is described. The simulation results show that the proposed estimator performs favorably in relation to existing methods and recovers the local structure of the underlying function based on the sample.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.37.2-37
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• 2001

This paper addresses the robust adaptive output feedback tracking for nonlinear systems under disturbances whose bounds are unknown. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The State estimation is solved using K-filters, together with the construction of a bound of an error in the state estimation due to the perturbation of the disturbance. Tuning functions are used to estimate unknown system parameters without overparametrization. The proposed control algorithm ensures that the out put tracking error converges to a residual set which can be arbitrarily small, while maintaining the boundedness of all other variables. A simulation shows the effectiveness of the proposed approach

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.214-215
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• 2006

In power systems, the state estimation takes an important role in security control. At present, the weighted least squares(WLS) method has been widely used to the state estimation computation. This paper presents an application of Adaptive Evolutionary Algorithm(AEA) to state estimation in power systems. AEA is a optimization method to overcome the problems of classical optimization. AEA is employed to solve state estimation on the 6 bus system.

• 한국통신학회논문지
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• 제21권5호
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• pp.1109-1122
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• 1996

Generally, moving images contain the various components in motions, which reange from a static object and background to a fast moving object. To extract the accurate motion parameters, we must consider the various motions. That requires a wide search egion in motion estimation. The wide search, however, causes a high computational complexity. If we have a few knowledge about the motion direction and magnitude before motion estimation, we can determine the search location and search window size using the already-known information about the motion. In this paper, we present a local adaptive motion estimation approach that predicts a block motion based on spatio-temporal neighborhood blocks and adaptively defines the search location and search window size. This paper presents a technique for reducing computational complexity, while having high accuracy in motion estimation. The proposed algorithm is introduced the forward and backward projection techniques. The search windeo size for a block is adaptively determined by previous motion vectors and prediction errors. Simulations show significant improvements in the qualities of the motion compensated images and in the reduction of the computational complexity.

• Journal of Advanced Marine Engineering and Technology
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• 제23권1호
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• pp.96-102
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• 1999

Sensorless speed estimation in induction motor systems is one of the most control engineers. Based on the estimated speed the vector control has been applied to the high precision torque control however most speed estimation methods use adaptive scheme so that it takes long time to estimate the speed. Thus the adaptive estimation scheme is not effective to the induction motor which requires short sampling time. In this paper a new linearized equation of induction motor system is proposed and a sensorless speed estimation algorithm based on observation techniques is developed. First the nonlinear induction motor equation is linearized at an equilibrium point. Second a proportional integral(PI) observer is applied to estimate the speed state in the induction motor system. Finally simulation results will assure the effectiveness of the new linearized equation and the sensorless estimation algorithm by using PI observer in the nonlinear induction motor system.