• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.149-152
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• 2005

In this paper, a robust $H\infty$ stabilization problem to a uncertain discrete-time fuzzy systems with time-varying delay via static output feedback is investigated. The Takagi -Sugeno (T-S) fuzzy model is employed to represent an uncertain nonlinear systems with time-varying delayed state. Using a single Lyapunov function, the globally asymptotic stability and disturbance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust $H\infty$ controllers are given in terms of linear matrix inequalities.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1284-1288
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• 1990

In this paper, we are going to study the stabilization of the semilinear heat equation with inhomogenous boundary conditions, whose solutions are not (in general) stable. Here, we use the discrete-time feedback inputs through the boundary of geometric domain to the semilinear system under some additional conditions and assumptions. It is shown that under these conditions, the stabilization can be realized by applying pole assignment argument to the principal linear part of the system and that the solutions exist globally in discrete-time t without any finite escape time.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.1-9
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• 1996

In this paper, a parameter identification method for a discrete-time linear system using multi-layer neural network is proposed. The parameters are identified with the combination of weights and the output of neuraons of a neural network, which can be used for a linear and a nonlinear controller. An adaptive output tracking architecture is designed for the linear controller. And, the nonlinear controller. A sliding mode control law is applied to the stabilizing the nonlinear controller such that output errors can be reduced. The effectiveness of the proposed control scheme is illustrated through simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.780-785
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• 2007

This paper proposes the decentralized dynamic output feedback controller for discrete-time nonlinear interconnected systems using Takagi-Sugeno (T-S) fuzzy model. Through T-S fuzzy model of each subsystem, the decentralized dynamic output feedback controller is designed. By the closed-loop subsystems with controller, it represents the linear matrix inequality (LMI) for stability of whole interconnected system. The value of control gain are obtained by LMI. An example is given to show the experimentally verification discussed throughout the paper.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.4
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• pp.295-301
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• 2012

This paper deals with the design of robust DSMC (Discrete-Time Sliding Mode Control) scheme in order to overcome system uncertainty in steering system with mechanically joined structure. The proposed control scheme is one of robust control schemes based on system dynamics. Therefore, system dynamics required is not obtained from physical law but SCM (Signal Compression Method) through experiment in order to avoid complicate mathematical development and save time. However, SCM has a shortcoming that is the limitation of with $2^{nd}$ order linear model which does not include the dynamic of high-frequency band. Thus, considering system uncertainty, DSMC is designed. In addition, to reduce the chattering problem of DSMC, DSMC is derived from the reaching law and the Lyapunov stability condition. It is found that the proposed control scheme has robustness in spite of the perturbation of system uncertainty through computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.121-127
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• 2009

This paper deals with the design problem of robust stabilization and non-fragile controller for discrete-time singular systems with parameter uncertainties and time-varying delays in state and input by delay-dependent Linear Matrix Inequality (LMI) approach. A new delay-dependent bounded real lemma for singular systems with time-varying delays is derived. Robust stabilization and robust non-fragile state feedback control laws are proposed, which guarantees that the resultant closed-loop system is regular, causal and stable in spite of time-varying delays, parameter uncertainties, and controller gain variations. A numerical example is given to show the validity of the design method.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.330-340
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• 2002

In this Paper. a non-linear approach to a design of model reference adaptive control is presented. The approach is demonstrated by a case study of a simple single-pole and no zero, linear, discrete-time plant. The essence of the idea is to generate a full non-linear model of the plant dynamics and the parameter adaptation dynamics as a gradient descent algorithm with respect to a Riemannian metric. It is shown how a Riemannian metric can be chosen so that the modelled plant dynamics do in fact match the true plant dynamics. The performance of the proposed scheme is compared to a traditional model reference adaptive control scheme using the classical sensitivity derivatives (Euclidean gradients) for the descent algorithm.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1056-1059
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• 1996

In this study, a new OSA controller is proposed for controlling discrete-time chaotic systems efficiently. A new OSA controller uses NARMAX models, and its feedback gain is designed on the basis of conventional linear control theory. In order to evaluate the performance of a new OSA controller, a new OSA controller is applied to Henon system which is a discrete-time chaotic system, and then the control performance of a new OSA controller are compared with that of the previous model-base controller through computer simulations.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.216-220
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• 2019

In this paper, we propose a finite memory filter (estimator) for discrete-time stochastic linear systems with delays in state and measurement. A novel filtering algorithm is designed based on finite memory strategies, to achieve high estimation accuracy and stability under parametric uncertainties. The new finite memory filter uses a set of recent observations with appropriately chosen initial horizon conditions. The key contribution is the derivation of Lyapunov-like equations for finite memory mean and covariance of system state with an arbitrary number of time delays. A numerical example demonstrates that the proposed algorithm is more robust and accurate than the Kalman filter against dynamic model uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.170-174
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• 2014

In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.