• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.322-326
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• 2003

In this paper, we establish a new robust $H_{\infty}$ performance condition for uncertain discrete-time systems with convex polytopic uncertainties. We express the condition as a set of linear matrix inequalities (LMIs), which are used to check stability and $H_{\infty}$ disturbance attenuation level by a parameter-dependent Lyapunov matrix. We show that the new condition provides less conservative result than the existing ones which use single Lyapunov matrix. We also show that the robust $H_{\infty}$ state feedback design problem for such uncertain discrete-time systems can be easily dealt with using the approach. The key point in this paper is to propose a kind of decoupling between the Lyapunov matrix and the system matrices in the parameter-dependent matrix inequality by introducing one new matrix variable.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1053-1060
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• 2000

In this paper, a discrete-time variable structure control method using recursively defined switching function and a decoupled variable structure disturbance compensator is used to achieve high performance circular motion control of a CNC machining center. The discrete-time variable structure control with the decoupled disturbance compensator method developed in this paper uses a recursive switching function defined as the sum of the current tracking error vector and the previous value of the switching function multiplied by a positive constant less than one. This recursive switching function provides much improved performance compared to the method that uses a switching function defined only as a linear combination of the current tracking error. Enhancements in tracking performance are demonstrated in the circular motion control using a CNC milling machine.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.777-780
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• 1991

The practical implementation of model reference adaptive systems(MRAS) using digital computer requires the derivation of discrete-time adaptation laws. This is specially important in the case of direct driver robot and light weight manipulator where inertia changes ang gravity effects are significant. We develope a discrete-time model reference adaptive control scheme for trajectory tracking of robot manipulator. Instead of the conventional Lyapunov approach hyperstabillty theory is more appealing than the Lyapunov approach. It is better suited to discrete time systems and offers more flexibility in design by providing additional free design parameters.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1144-1150
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• 2009

This paper investigates the feasibility of the Bayesian discrete time approximation method to estimate the parameters of Weibull distributions of failures for two non-identical components connected in series system. A Bayesian model based on the discrete time approximation method is formulated to infer the Weibull parameters of two non-identical components with the failure data of the virtual tests. The study of this paper comes to a conclusion that the method is feasible only for some special cases under the given constraints on the concerned parameters.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.351-353
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• 2006

In this paper, we present the traditional GPS Position- Velocity (PV) model to apply for both Discrete-Time Kalman Filter and Discrete-Time $H_{\infty}$ Filter. The positioning algorithms of both filters are proposed for a stand-alone low-cost GPS module to increase its accuracy. For disturbance cancellation, the Kalman Filter requires the statistical information about process and measurement noises while the $H_{\infty}$ Filter only requires that these noises are bounded. Experiments show that with the same measurement data, $H_{\infty}$ Filter gives us better positioning results compared with Least-Squared method and Kalman Filter.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1142-1146
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• 2014

This paper addresses on a $H_{\infty}$ sampled-data stabilization of a Takagi-Sugeno (T-S) fuzzy system. The sampled-data stabilization problem is formulated as a discrete-time stabilization one via a direct discrete-time design approach. It is shown that the sampled-data fuzzy control system is asymptotically stable whenever its exactly discretized model is asymptotically stable. Based on an exact discrete-time model, sufficient design conditions are derived in the format of linear matrix inequalities (LMIs). An example is provided to illustrate the effectiveness of the proposed methodology.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.11
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• pp.53-62
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• 1997

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. The proposed control method is based on Generalized Predictive Control and uses NARMAX models as controlled models. In order to evaluate the performance of the proposed method, a proposed controller is applied to discrete-time chaotic systems, and then the control performance and initial sensitivity of the proposed controller are compared with those of the conventional model-based controler through computer simulations. Through simulations results, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller and shown that the peorposed controller is less sensitive to initial values of discrete-time chaotic systems in comparison with the conventional model-based controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1069-1074
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• 1989

A discrete-time learning control for robotic manipulators is studied using its pulse transfer function. Firstly, discrete-time learning stability condition which is applicable to single-input two-outputs systems is derived. Secondly, stability of learning algorithm with position signal is studied. In this case, when sampling period is small, the algorithm is not stable because of an unstable zero of the system. Thirdly, stability of algorithm with position and velocity signals is studied. In this case, we can stabilize the learning control system which is unstable in learning with only position signal. Finally, simulation results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. This simulation results agree well with the analytical ones.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.352-355
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• 1995

In this paper, estimation error bounds of the optimal FIR (Finite Impulse Response) filter, which is proposed by Kwon et al.[1, 2], are presented in discrete-time systems with the model uncertainty. Performance bounds are here represented by the upper bounds on the difference of the estimation error covariances between the nominal and real values in case of the systems with the noise or model parameter uncertainty. The estimation error bounds of the discrete-time optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the simulation problem by Toda and Patel[3]. Simulation results show that the former has robuster performance than the latter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.407-414
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• 1996

In this paper we consider the variable structure control for a class of discrete-time uncertain multivariable systems where the nominal system is linear. Discrete-time switching dynamics are introduced so that a new type of state trajectories called sliding mode may exist on the sliding surface by state feedback. The quantitative analysis for the matched uncertainties will show that every response of the system with the proposed switching dynamics is bounded within small neighborhoods of the state-space origin. Also, by the similarity transformation it will be shown that the eigenvalues of the closed-loop systems are composed of those of the subsystems which govern the range-space dynamics and null-space dynamics. It will be also shown that ideal sliding mode can be obtained in the absence of uncertainties due to one-step attraction to the sliding surface regardless of initial position of states. (author). 12 refs., 2 figs.