• Journal of Electrical Engineering and Technology
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• v.4 no.4
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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.

• Journal of the Korean Mathematical Society
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• v.37 no.5
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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).

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.9
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.10
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.343-348
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• 2001

In this paper we propose a noise reduction algorithm which based on cellular automata with the local median rule. It is supposed that there is no information about the features of the image that must be improved. The proposed method behavior is to locally increase or decrease the gray level differences of the image without loss of the main characteristics of the image. The dynamical behavior of these automata is completely determined by Lyapunov operators for sequential and parallel update. We have found that the automata present very fast convergence to fixed points, stability in front of random noisy images. Based on the experimental results we discuss the advantage and efficiency.