• Journal of Electrical Engineering and Technology
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• 제4권4호
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• pp.566-569
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• 2009

In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.

• 대한수학회지
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• 제37권5호
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• pp.835-847
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• 2000

We investigate various ${\Phi}$(t)-stability of comparison differential equations and we obtain necessary and/or sufficient conditions for the asymptotic and uniform asymptotic stability of the differential equations x'=f(t, x).

• 전자공학회논문지S
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• 제34S권9호
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• pp.41-49
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• 1997

In this paper, we describe the algorithm which controls an unknown nonlinear system with disturbance a using 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 an unknown nonlinear system by using of multilayer neural netowrk. WE include a disturbance among the modelling error, and the weight-update rule of multilayer neural network is derived to satisfy Laypunov stability. The whole control system constitutes controller using the 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.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권10호
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• pp.555-562
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• 2000

We propose an adaptive nonlinear control algorithm for compensation of the stick-slip friction in a dynamic system. The friction force and mass of the system are estimated and compensated by adaptive control law. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Laypunov stability theory, and its convergence is guaranteed under the bounded noise or torque disturbance. We verified the performance of the proposed algorithm by computer simulation on one-DOF mechanical system with friction.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.873-876
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• 1993

A fuzzy controller designed by mini-max-gravity(MMG) method is essentially nonlinear with respect to the controller's input and output relationship, and stability analysis is thus needed to construct a stable control system. This paper deals with a design method of a position-type MMG fuzzy controller stable in a sense of Lyapunov when considered is a single-input-single-output linear, stable plant. We first introduce a method to construct a Laypunov function by using an eigen-value of A matrix of the linear, stable plant dynamics and then we derive an asymtotic stability condition in terms of scale factors for fuzzy state variables and controller gain. The stability condition is found reasonably practical through comparing the theoretical stability region with that obtained from simulations.

• 한국정보통신학회논문지
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• 제5권2호
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• pp.343-348
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• 2001

본 논문에서는 국부적인 미디안(median)규칙에 파라 움직이는 셀룰러 오토마타를 이용해 영상에 대한 사전 지식이 필요 없는 영상의 잡음제거 알고리즘을 제안한다. 각 규칙은 원영상이 가지는 특징의 손실없이 국부적으로 자기값(gray level)을 증감시킨다. 이러한 셀룰러 오토마타는 순차적이고 병렬적인 움직임을 가지며, 이 움직임은 Lyapunov functional을 만족하는 함수로 표현된다. 따라서 본 셀룰러 오토마타를 이용한 영상의 잡음제거 알고리즘은 매우 빠른 속도로 수렴하고, 안정적인 결과를 나타낸다. 실험을 통해 본 방법의 유효성을 확인한다.