• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.489-496
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• 2004

We study the h- stability of nonlinear difference system x(n+1) =f(n, x(n)) and its perturbed system y(n+l) =f(n, y(n))+g(n, y(n), Sy(n)).

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2863-2866
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• 2003

In nonlinear control fields, for irregular nonlinear systems, control form which consists of approximate tracking control law and exact tracking control law and which switches between two laws has been proposed recently. In this thesis, we design new switching control law which connect approximate linearization control law and exact linearization control law by fuzzy rules for irregular nonlinear system, ball and beam system. Fuzzy switching controller designed by fuzzy concept is proved that designed scheme overcomes singularities of irregular system, improves unstability problem of switching procedure, and has more efficient control value through simulation. Stability of fuzzy control system proved by Lyapunov's stability theorems.

• 전기의세계
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• v.16 no.3
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• pp.16-20
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• 1967

To analyze the stability creterion and the dynamic performance of the nonlinear reactor control system which involve the on-off element and gear backlash, the concept of discribing function is developed for the system of two nonlinear elements are connected by linear element. Using the derived discribing function and frequency responce method, the stability creterion and the dynamic performance of the nonlinear reactor control system are analyzed, and the results of the analysis are conformed by analog computor.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.33-36
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• 2002

In this paper, we propose a control method of an unknown nonlinear system using a dynamical neural network. The method proposed in this paper performs for a nonlinear system with unknown system, identification with using the dynamical neural network, and then a nonlinear adaptive controller is designed with these identified informations. In order to verify the effectiveness of the proposed algorithm, we simulated one-link manipulator. The simulation result showed the effectiveness of using the dynamical neural network in the adaptive control of one-link manipulator.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.5-8
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• 2003

This paper we study the exact controllability for the nonlinear fuzzy control system in E$_{N}$$^{n}$ by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in R$^{n}$ . fuzzy number of dimension n ; fuzzy control ; nonlinear fuzzy control system ; exact controllabilityty

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.347-354
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• 1994

This paper describes a diagnostic protocol of nonlinear dynamic characteristics of biological system using chaos theory. An integrated chaos analysis system for the diagnosis of biological system was designed. We suggest a procedure of attractor reconstruction for reliable qualitative and quantitative analysis. The effect of autonomic nervous system activity on heart rate variability with power spectral analysis and its characteristics of chaotic attractors are investigated. The results show the applicability to evaluate the mental and physical conditions using nonlinear characteristics of biological signal.

• Kyungpook Mathematical Journal
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• v.59 no.1
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• pp.101-123
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• 2019

In this paper, we consider a system of nonlinear variational type inclusions involving ($H,{\varphi},{\eta}$)-monotone operators in real Banach spaces. Further, we define a proximal operator associated with an ($H,{\varphi},{\eta}$)-monotone operator and show that it is single valued and Lipschitz continuous. Using proximal point operator techniques, we prove the existence and uniqueness of a solution and suggest an iterative algorithm for the system of nonlinear variational type inclusions. Furthermore, we discuss the convergence of the iterative sequences generated by the algorithms.

• Journal of applied mathematics & informatics
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• v.7 no.3
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• pp.969-982
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• 2000

The approximate controllability for the nonlinear control system with nonlinear monotone hemicontinuous and coercive operator is studied. The existence, uniqueness and a variation of solutions of the system are also given.

• International Journal of Precision Engineering and Manufacturing
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• v.1 no.1
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• pp.79-83
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• 2000

Although it is well known that fuzzy learning controller is powerful for nonlinear systems, it is very difficult to apply a learning method if they are unstable. An unstable system diverges for impulse input. This divergence makes it difficult to learn the rules unless we can find the initial rules to make the system table prior to learning. Therefore, we introduced LQR(Linear Quadratic Regulator) technique to stabilize the system. It is a state feedback control to move unstable poles of a linear system to stable ones. But, if the system is nonlinear or complicated to get a liner model, we cannot expect good results with only LQR. In this paper, we propose that the LQR law is derived from a roughly approximated linear model, and next the fuzzy controller is tuned by the adaptive on-line learning with the real nonlinear plant. This hybrid controller of LQR and fuzzy learning was superior to the LQR of a linearized model in unstable nonlinear systems.