• 대한수학회논문집
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• 제25권4호
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• pp.655-669
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• 2010

In this paper we propose new primal-dual interior point methods (IPMs) for $P_*(\kappa)$ linear complementarity problems (LCPs) and analyze the iteration complexity of the algorithm. New search directions and proximity measures are defined based on a class of kernel functions, $\psi(t)=\frac{t^2-1}{2}-{\int}^t_1e{^{q(\frac{1}{\xi}-1)}d{\xi}$, $q\;{\geq}\;1$. If a strictly feasible starting point is available and the parameter $q\;=\;\log\;\(1+a{\sqrt{\frac{2{\tau}+2{\sqrt{2n{\tau}}+{\theta}n}}{1-{\theta}}\)$, where $a\;=\;1\;+\;\frac{1}{\sqrt{1+2{\kappa}}}$, then new large-update primal-dual interior point algorithms have $O((1\;+\;2{\kappa})\sqrt{n}log\;n\;log\;{\frac{n}{\varepsilon}})$ iteration complexity which is the best known result for this method. For small-update methods, we have $O((1\;+\;2{\kappa})q{\sqrt{qn}}log\;{\frac{n}{\varepsilon}})$ iteration complexity.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.76-78
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• 2009

This paper considers the problem of global adaptive regulation for a class of nonlinear systems which have multiple unknown virtual control coefficient. By using a new parameter estimator and backstepping technique, we design a smooth state feedback control law, parameter update laws that estimate the unknown virtual control coefficients, and a continuously differentiable Lyapunov function which is positive definite and proper.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.441-444
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• 1989

This correspondence presents a recursive estimation algorithm which, unlike conventional ones; updates the estimates only when a sufficient improvement can be obtained with a bounded noise assumption, the resulting sequence of estimates is a sequence of convex sets(ellipsoids) in the parameter space. For the cases studied, the algorithm use less than 20 percent of the. data to update, the estimate and still acquired good accuracy for spectral estimation.

• 드라이브 ㆍ 컨트롤
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• 제17권1호
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Coupled systems mechanics
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• 제11권5호
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• pp.411-438
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• 2022

In this paper, we present the parameter identification for inelastic and multi-scale problems. First, the theoretical background of several fundamental methods used in the upscaling process is reviewed. Several key definitions including random field, Bayesian theorem, Polynomial chaos expansion (PCE), and Gauss-Markov-Kalman filter are briefly summarized. An illustrative example is given to assimilate fracture energy in a simple inelastic problem with linear hardening and softening phases. Second, the parameter identification using the Gauss-Markov-Kalman filter is employed for a multi-scale problem to identify bulk and shear moduli and other material properties in a macro-scale with the data from a micro-scale as quantities of interest (QoI). The problem can also be viewed as upscaling homogenization.

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.2162-2169
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• 2015

Cruise control is a technology for automatically maintaining a steady speed of vehicle as set by the driver via controlling throttle valve and brake of vehicle. In this paper we investigate cruise controller design method with consideration for distance to vehicle ahead. We employ linear time varying (LTV) model to describe longitudinal vehicle dynamic motion. With this LTV system we approximately model the nonlinear dynamics of vehicle speed by frequent update of the system parameters. In addition we reformulate the LTV system by transforming distance to leading vehicle into variation of system parameters of the model. Note that in conventional control problem formulation this distance is considered as disturbance which should be rejected. Consequently a controller can be designed by pole placement at each instance of parameter update, based on the linear model with the present system parameters. The validity of this design method is examined by simulation study.

• 한국통신학회논문지
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• 제36권3C호
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• pp.157-162
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• 2011

동시 등화는 통신 채널을 블라인드 등화 시 수렵 특성 개선에 유용하다. 그러나 동시 등화는 등화기가 정상상태에 수렴한 후에도 동시 적응을 계속함으로써 성능 개선이 제한적이다. 본 논문에서는 동시 등화의 수렴 특성과 함께 정상상태 성능을 개선하기 위해, 가변 수렴상수와 가중치기반의 탭 계수 갱신을 적용하는 새로운 동시 등화 기법을 제안한다. 제안하는 동시 vsCMA+DD 등화는 가변 수렴상수 CMA(variable step-size CMA: vsCMA)와 판정의거 (decision-directed: DD) 알고리즘의 오차 신호를 사용하여 가중치를 산출하고, 이를 이용하여 두 등화기를 각각 가중 갱신한다. 제안 방법은 vsCMA에 의해 CMA의 오차 성능을 개선하고, 가중치기반의 탭 계수 갱신에 의해 수렴 속도와 정상상대 성능을 개선하였다. 모의실험을 통해 제안 방식의 성능 개선을 검증하였다.

• Journal of Power Electronics
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• 제17권5호
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• pp.1211-1222
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• 2017

In order to decrease the parameter sensitivity of deadbeat direct torque control (DB-DTC), an improved deadbeat direct torque control method for surface mounted permanent-magnet synchronous motor (SPMSM) drives is proposed. First, the track errors of the stator flux and torque that are caused by model parameter mismatch are analyzed. Then a sliding mode observer is designed, which is able to predict the d-q axis currents of the next control period for one-step delay compensation, and to simultaneously estimate the model parameter disturbance. The estimated disturbance of this observer is used to estimate the stator resistance offline. Then the estimated resistance is required to update the designed sliding-mode observer, which can be used to estimate the inductance and permanent-magnetic flux linkage online. In addition, the flux and torque estimation of the next control period, which is unaffected by the model parameter disturbance, is achieved by using predictive d-q axis currents and estimated parameters. Hence, a low parameter sensitivity DB-DTC method is developed. Simulation and experimental results show the validity of the proposed direct control method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.285-294
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• 2007

In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network(DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm to two well known representations of DBNs: to a first-order Markov Chain(MC) model and to a Hidden Markov Model(HMM). A sliding window allows efficient adaptive computation in real time. We also examine the stochastic convergence and stability of the learning algorithm.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2016년도 춘계학술대회
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• pp.305-307
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• 2016

The purpose of this paper is to determine the optimal values of the gain parameters used in the tracking module for a highly dynamic warship. The algorithm of the tracking module uses the ${\alpha}-{\beta}-{\gamma}$ filter to compute accurate estimates and update the state variables, that is, positions, velocity and acceleration. The filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization is achieved by plotting a range of the damping parameter ${\xi}$ against the corresponding residual error and then selecting the best value of ${\xi}$ with the minimum residual error. Optimal values of the smoothing coefficients are subsequently computed from the selected damping parameter, ${\xi}$.