• 전기학회논문지
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• 제62권1호
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• pp.95-100
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• 2013

This paper presents a new state feedback control approach for communication networks based control systems in which control input and output observation time-delay natures are generally occurred in practice. We first establish a generic state feedback control framework based on well-known linear system theory. A maximum time-delay value which allows critical stability of whole control system are defined to make a positive definite Lyapunov function which is mathematically composed of controlled system states. We analytically derive its control parameters by using a steepest descent optimization method in order to guarantee a stability condition through Lyapunov theory. Computer simulation is numerically carried out for demonstrating reliability of the proposed NCS algorithm and a comparative study is accomplished to prove its superiority for which the traditional control approach for NCS is made use of under same simulation scenarios.

• Nuclear Engineering and Technology
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• 제27권2호
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• pp.155-164
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• 1995

증기발생기 수위조절계통의 무작위추출 비선형 제어계통의 경우로 연구되었다. 무작위 변수로는 시간불연속 계통의 추출시간간격 이 고려되었다. Lyapunov 함수를 구하지 않는 확률론적 Lyapunov 방법이 용되었다. 유도된 안정성 요건은 CANDU 형 원자로인 월성 1호기의 자료를 이용하여 시간 존속 모사로 검증하였다.

• 대한교통학회지
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• 제25권4호
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• pp.99-108
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• 2007

본 연구에서는 가변수요를 고려한 확률적 사용자균형 통행배정모형을 제시한다. 교통망에서 수요와 공급간의 균형을 가정할 경우, 통행비용의 함수인 가변수요는 통행저항함수(공급함수)와 함께 균형상태로 수렴하며, 이때 확률적 통행배정모형은 통행자들간의 경로인지 통행비용이 동일해지는 확률적 사용자균형상태에 도달하게 된다. 본 연구에서 제시하는 확률적 사용자균형모형은 기존 연구들과는 달리 동적체계(dynamic system)를 기초로 개발된다. 동적체계는 시간의 흐름에 따라 하나의 상태가 다음 상태로 변화하는 과정을 표현하는 수리적인 방법으로 시간의 변화에 따라 그 상태가 변하는 여러 분야에 적용이 가능한데, 주로 제어공학(control engineering)분야에서 활용되어 왔다. 동적 체계의 개념을 도입하면, 기존 모형들과는 달리 쉽게 모형화(formulation)할 수 있으며 풀이과정(solution algorithm)도 간단하다는 장점이 있다. 본 연구에서도 동적체계를 이용하여 확률적 사용자균형 통행배정(user equilibrium traffic assignment)모형을 제시하고 제시된 모형이 안정적인 해(stable solution)로 수렴한다는 것을 Lyapunov함수를 통하여 증명한다. 또한, 예제 교통망을 통하여 여러가지 의미있는 결과를 도출한다.

• 한국컴퓨터정보학회논문지
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• 제18권6호
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• pp.9-19
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• 2013

본 논문에서는 상태 및 출력변수에 시간지연을 가지는 이산 비선형 마코비안 점프시스템의 퍼지 $H_{\infty}$ 필터 설계 방법을 다룬다. 리아프노프(Lyapunov) 함수를 이용하여 상태추정 오차시스템이 확률적 안정하며 외부외란 및 초기값 불확실성에 대하여 $H_{\infty}$ 성능을 만족하는 조건식을 유도하고 필터 존재 조건을 선형행렬부등식으로 나타낸다. 완화된 필터 존재 조건식을 유도하기 위하여 리아프노프 함수 선택 시에 시스템 모드에 종속적일뿐만 아니라 퍼지 멤버십 함수를 포함하는 확률-퍼지 리아프노프 함수를 선택한다. 또한 $H_{\infty}$ 성능 조건식 유도 시에 외부외란 뿐만 아니라 최기값 불확실성을 고려한다. 수치적 예제 및 컴퓨터 시뮬레이션을 통하여 제안된 방법의 타당성을 보인다.

• 제어로봇시스템학회논문지
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• 제16권3호
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• pp.256-263
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• 2010

The paper proposes a sensorless speed control strategy for a PMSM (Permanent Magnet Synchronous Motor) based on a new SMO (Sliding Mode Observer), which substitutes a signum function with a sigmoid function. To apply robust sensorless control of PMSM against parameter fluctuations and disturbance, the high speed SMO is proposed, which estimates the rotor position and angular velocity from the back EMF. The low-pass filter and additional position compensation of the rotor are used to reduce the chattering problem commonly found in sliding mode observer with signum function, which becomes possible by applying the sigmoid function with the control of a switching function. Also the proposed sliding mode observer with the sigmoid function has better efficiency than the conventional sliding mode observer since it adjusts the observer gain by variable boundary layer and estimates the stator resistance. The stability of the proposed sliding mode observer is verified by the Lyapunov second method in determining the observer gain. The validity of the proposed high speed PMSM sensorless velocity control has been demonstrated by real experiments.

• 제어로봇시스템학회논문지
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• 제12권5호
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• pp.411-418
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• 2006

There are mainly two types of bang-bang controllers for nominal linear time-invariant (LTI) system. Optimal bang-bang controller is designed based on optimal control theory and suboptimal bang-bang controller is obtained by using Lyapunov stability condition. In this paper, the suboptimal bang-bang control method is extended to LTI system involving both control input saturation and structured real parameter uncertainties by using Lyapunov robust stability condition. Two robust optimal bang-bang controllers are derived by minimizing the time derivative of Lyapunov function subjected to the limit of control input. The one is developed based on the classical quadratic stability(QS), and the other is developed based on the affine quadratic stability(AQS). And characteristics of the two controllers are compared. Especially, bounds of parameter uncertainties which theoretically guarantee robust stability of the two controllers are compared quantitatively for 1DOF vibrating system. Moreover, the validity of robust optimal bang-bang controller based on the AQS is shown through numerical simulations for this system.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.142-147
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• 1996

In this paper, we describe the algorithm which controls an unknown nonlinear system with multilayer neural network. The multilayer neural network can be used to approximate any continuous function to any desired degree of accuracy. With the former fact, we approximate unknown nonlinear function on the nonlinear system by using of multilayer neural network. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. The whole control system constitutes controller using feedback linearization method. The weight of neural network which is used to implement nonlinear function is updated by the derived update-rule. The proposed control algorithm is verified through computer simulation.

• 제어로봇시스템학회논문지
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• 제21권3호
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• pp.237-242
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• 2015

This paper presents a method to enhance tracking performance of an input-constrained bilinear system using MPC (Model Predictive Control) when a feasible tracking control is known. Since the error dynamics induced by the known tracking control is asymptotically stable, there exists a Lyapunov function for the stable error dynamics. By defining a cost function including the Lyapunov function and describing tracking performance, an MPC law is derived. In simulation, the performance of the proposed MPC law is demonstrated by applying it to a converter model.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.43-50
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• 2002

In the recent years neural networks have fulfilled the promise of providing model-free learning controllers for nonlinear systems; however, it is very difficult to guarantee the stability and robustness of neural network control systems. This paper proposes an adaptive neural network control for robot manipulators based on the radial basis function netwo.k (RBFN). The RBFN is a branch of the neural networks and is mathematically tractable. So we adopt the RBFN to approximate nonlinear robot dynamics. The RBFN generates control input signals based on the Lyapunov stability that is often used in the conventional control schemes. The saturation function is also chosen as an auxiliary controller to guarantee the stability and robustness of the control system under the external disturbances and modeling uncertainties.

• 한국군사과학기술학회지
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• 제20권1호
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• pp.108-118
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• 2017

In this paper, the path tracking controller for a surface vessel based on the sliding mode control (SMC) with the switching law is proposed. In order to have no restriction on movement and improved tracking performance, the proposed control system is developed as follows: First, the kinematic and dynamic models in Cartesian coordinates are considered to solve the singularity problem at the origin. Second, the new multiple sliding surfaces are designed with the SMC and approach angle concept to solve the under-actuated property. Third, the switching control system is designed to improve tracking performance. To prove the stability of the proposed switching system under the arbitrary switching, the Lyapunov stability analysis method with the common Lyapunov function is used. Finally, the computer simulations are performed to demonstrate the performance, effectiveness and stability of the proposed tracking controller of a surface vessel.