• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.492-495
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• 1995

A simple method to design observers for linear describtor systems with unmeasureable disturbance is represented by the response of a linear free system. The sufficient conditions for the existence of the observer are given. The design procedures of an identify and a minimal order observers are shown, respectively.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.233-236
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• 2015

In this paper, a nonlinear optimization problem is proposed to obtain a structured static output feedback controller for discrete time linear systems. The proposed optimization problem has LMI (Linear Matrix Inequality) constraints and a non-convex objective function. Using the conditional gradient method, we can obtain suboptimal solutions of the proposed optimization problem. Numerical examples show the effectives of the proposed approach.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.260-263
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• 2000

In this paper, a tracking control technique of Takagi-Sugeno(TS) fuzzy systems with parametric uncertainties is developed. The uncertain TS fuzzy system is represented as an uncertain multiple linear system. The tracking problem of TS fuzzy system is converted into the regulation problem of a multiple linear system. A sufficient condition for robust tracking is obtained in terms of linear matrix inequalities(LMI). A Design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.476-479
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• 2000

This paper deals with a robust pole placement method for uncertain linear systems. For all admissible uncertain parameters, a static output feedback controller is designed such that all the poles of the closed loop system are located within the prespecfied disk. It is shown that the existence of a positive definite matrix belonging to a convex set such that its inverse belongs to another convex set guarantees the existence of the output feedback gain matrix for our control problem. By a sequence of convex optimization the aforementioned matrix is obtained. A numerical example is solved in order to illustrate efficacy of our design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.115-120
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• 1997

System identification is the task of inferring a mathematical description of a dynamic system from a series of measurements of the system. There are several motives for establishing mathematical descriptions of dynamic systems. Typical applications encompass simulation, prediction, fault diagnostics, and control system design. The paper demonstrates that neural networks can be used effective for the identification of nonlinear dynamical systems. The content of this paper concerns dynamic neural network models, where not all inputs to and outputs from the networks are measurable. Only one model type is treated, the well-known Innovation State Space model(Kalman Predictor). The identification is based only on input/output measurements, so in fact a non-linear Extended Kalman Filter problem is solved. Even for linear models this is a non-linear problem without any assurance of convergence, and in spite of this fact an attempt is made to apply the principles from linear models, an extend them to non-linear models. Computer simulation results reveal that the identification scheme suggested are practically feasible.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.934-938
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• 1988

In this paper we present a method of determining the unknown degree of any 2D discrete linear shift-invariant system which is characterized only by the coefficients of the double power series of a transfer function, i.e. a 2D impulse response array. Our method is based on a 2D extension of Berlekamp-Massey algorithm for synthesis of linear feedback shift registers, and it gives a novel approach to identification and approximation of 2D linear systems, which can be distinguished in its simplicity and potential of applicability from the other 2D Levinson-type algorithms. Furthermore, we can solve problems of 2D Pade approximation and 2D system reduction on a reasonable assumption in the context of 2D linear systems theory.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.574-578
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• 1998

This paper considers the robust positive real problem for linear systems with linear fractional-type norm-bounded repeated scalar block parameter uncertainty. It is shown that the robust positive real problem can be converted into the standard positive real problem without uncertainty that can be used for the analysis of the given uncertain linear system and the synthesis of a controller that robustly stabilizes and achieves the extended strict positive realness property of the closed-loop transfer function. These results can be also applied to the linear system with general structured uncertainty containing repeated scalar block parameters and are extensions of the previous works that consider only norm-boundedness of the affine unstructured uncertainty.

• Journal of IKEEE
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• v.18 no.4
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• pp.517-522
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• 2014

This paper investigates the consensus problem for linear multi-agent systems with an arbitrary network delay. The sufficient conditions for a state consensus of linear multi-agent systems are provided by using linear matrix inequalities. Moreover, it is shown that under the proposed protocol, the consensus can be achieved even in the presence of an arbitrarily large network delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.183-190
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• 2000

It is well-known that the poles of a system are closely related with the dynamics of the systems, and the pole assignment problem, which locates the poles in the desired regions, in one of the major problem in control theory. Also, it is always possible to assign poles to specific points for exactly known linear systems. But, it is impossible for the uncertain linear systems because of the uncertainties that originate from modeling error, system variations, sensing error and disturbances, so we must consider some regions instead of points. In this paper, we consider both the analysis and the design of robust pole assignment problem of linear system with time-varying uncertainty. The considered uncertainties are the unstructured uncertainty and the structured uncertainty, and the considered region is the circular region. Based on Lyapunov stability theorem and linear matrix inequality(LMI), we first present the analysis result for robust pole assignment, and then we present the design result for robust pole assignment. Finally, we give some numerical examples to show the applicability and usefulness of our presented results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system