• 한국항공우주학회지
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• 제38권12호
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• pp.1170-1176
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• 2010

본 연구는 적응제어기법을 이용한 무인항공기이 피치 자세 기동에 대한 연구 내용을 소개한다. 모델기반적응제어(Model Reference Adaptive Control)을 이용하여 피치 자세각과 엘리베이터 입력 사이의 피드백 선형화 과정에서 발생하는 불확실성을 처리하였다. 모델 불활실성 파라미터는 피드백 제어기가 작동하는 중에 적응법칙을 이용하여 추정할 수 있도록 설계 되었다. 안정화 제어기에 의해 달성되는 최종 피치 자세각에 대한 분석을 통해 폐루프 시스템의 특성을 파악할 수 있도록 하였다. 제안된 제어 기법은 기존 제어기에서 주로 채택하고 있는 선형화나 게인 스케쥴링등의 과정이 필요하지 않아 상당한 모델 오차가 존재하는 상황에서 무인항공기의 고기동 피치 제어기 설계에 도움을 줄 것으로 생각한다.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.405-415
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• 2016

This paper presents a model predictive control for shunt active power filters in synchronous reference frame using space vector pulse-width modulation (SVPWM). The three phase load currents are transformed into synchronous rotating reference frame in order to reduce the order of the control system. The proposed current controller calculates reference current command for harmonic current components in synchronous frame. The fundamental load current components are transformed into dc components revealing only the harmonics. The predictive current controller will add robustness and fast compensation to generate commands to the SVPWM which minimizes switching frequency while maintaining fast harmonic compensation. By using the model predictive control, the optimal switching state to be applied to the next sampling time is selected. The filter current contains only the harmonic components, which are the reference compensating currents. In this method the supply current will be equal to the fundamental component of load current and a part of the current at fundamental frequency for losses of the inverter. Mathematical analysis and the feasibility of the suggested approach are verified through simulation results under steady state and transient conditions for non-linear load. The effectiveness of the proposed controller is confirmed through experimental validation.

• 전기학회논문지
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• 제65권11호
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• pp.1860-1867
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• 2016

In this paper, first, a linearized modeling of a phase shift full-bridge converter used in chargers of electric vehicles is derived by using state-space approach and transfer functions from the duty ratio to output voltage and the inductor current are also verified. second, control systems for the output voltage and the inductor current are designed using the root locus technique. It is illustrated by experimental results that the control performance on the output variables is satisfied with the designed digital control system based on a automobile qualified 32-bit microcontroller.

• 한국지능시스템학회논문지
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• 제11권7호
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• pp.680-688
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• 2001

In this paper, we propose a reliable method for searching the optimally performing generalized brain-state-in-a-box (GBSB) neural associative memory using an evolution program (EP) given a set of prototype patterns to be stored as stable equilibrium points. First, we exploit some qualitative guidelines necessary to synthesize the GBSB model. Next, we parameterize the solution space utilizing the limited number of parameters to represent the solution space. Then, we recast the synthesis of GBSB neural associative memories as two constrained optimization problems, which are equivalent to finding a solution to the original synthesis problem. Finally, we employ an evolution program (EP), which enables us to find an optimal set of parameters related to the size of domains of attraction (DOA) for prototype patterns. The validity of this approach is illustrated by a design example and computer simulations.

• 한국항공우주학회지
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• 제34권8호
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• pp.71-78
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• 2006

본 논문에서는 직교 좌표계에서 추적필터가 설계될 때, 표적의 거리와 방위에 대한 관측오차 공분산의 변화를 고려하기 위하여 정상상태 칼만필터의 해석적 해를 이용하는 IMM 추적기를 설계하였다. 제안된 정상상태 칼만필터 기반 IMM 추적기의 성능분석 및 검증을 위하여 거리의 변화가 작은 표적과 거리의 변화가 큰 표적에 대하여 각각 100회의 Monte Carlo 시뮬레이션을 수행하고, 고정이득 및 칼만필터 기반의 IMM 추적기와 RMS 오차분석을 통하여 비교하였다. 모의실험 결과로부터 제안된 방법이 칼만필터 기반 IMM 추적필터에 비하여 연산량을 크게 감소시킬 수 있으며, 유사한 추적성능을 제공할 수 있음을 확인하였다.

• 대한원격탐사학회지
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• 제1권1호
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• pp.5-27
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• 1985

A hill-sliding technique was devised to extract Gaussian clusters from the multivariate probability density estimates of sample data for the first step of iterative unsupervised classification. The underlying assumption in this approach was that each cluster possessed a unimodal normal distribution. The key idea was that a clustering function proposed could distinguish elements of a cluster under formation from the rest in the feature space. Initial clusters were extracted one by one according to the hill-sliding tactics. A dimensionless cluster compactness parameter was proposed as a universal measure of cluster goodness and used satisfactorily in test runs with Landsat multispectral scanner (MSS) data. The normalized divergence, defined by the cluster divergence divided by the entropy of the entire sample data, was utilized as a general separability measure between clusters. An overall clustering objective function was set forth in terms of cluster covariance matrices, from which the cluster compactness measure could be deduced. Minimal improvement of initial data partitioning was evaluated by this objective function in eliminating scattered sparse data points. The hill-sliding clustering technique developed herein has the potential applicability to decomposition of any multivariate mixture distribution into a number of unimodal distributions when an appropriate diatribution function to the data set is employed.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.321-324
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement Beaming is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement loaming is different from supervised teaming in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement loaming algorithms like Q-learning do not require defining or loaming any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning(AMMQL) as an improvement of the existing Modular Q-Learning(MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• 응용통계연구
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• 제36권4호
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• pp.349-360
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• 2023

동적 넬슨-시겔 모형은 채권과 같은 기간 구조를 갖고 있는 금융상품의 이자율 곡선모형에서 널리 사용되고 있다. 본 연구에서는 동적 넬슨-시겔 모형을 상태 공간 모형의 관점에서 설명하고 해당 모형에 적용할 수 있는 베이지안 접근법에 대해 알아보고자 한다. 그리고 SOFR 기간 데이터를 베이지안 동적 넬슨-시겔 모형에 적용하여 그 성능을 확인하고 바시첵 모형, 빈도주의 접근법을 활용한 동적 넬슨-시겔 모형, 2요인 베이지안 동적 넬슨-시겔 모형과 같은 다른 경쟁 모형들과 성능을 비교해보고자 한다. 우리는 베이지안 동적 넬슨-시겔 모형이 SOFR 기간 데이터에 대해서 다른 모형들보다 우수한 성능을 보여준다는 것을 확인할 수 있었다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 Proceedings ICPE 01 2001 International Conference on Power Electronics
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• pp.276-280
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• 2001

In this paper, an approach to the design of negative impedance stabilizing controllers for PWM DC/DC converters that are used in DC switching. power supplies with constant power loads is presented. The control approach is based on the feedback linearization technique. Because of the negative impedance destabilizing characteristics of constant power loads, classical linear control methods have stability limitations around the operating points. However, the proposed stabilizing technique improves large-signal stability and dynamic responses. The proposed controllers are simulated and their responses under different operations are studied. Stability of the control technique is also verified using the second theorem of Lyapunov.

• Structural Engineering and Mechanics
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• 제32권1호
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• pp.21-36
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• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.