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

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

• Journal of the Society of Naval Architects of Korea
• /
• v.61 no.3
• /
• pp.152-160
• /
• 2024

In the event of a maritime accident, search plans have traditionally been planned using experiential methods. However, these approaches cannot guarantee safety when the scale of a maritime accident increases. Therefore, this study proposes a model utilizing discrete event simulation (DES) to predict the diving time for compartment searches of a ship located on the seabed. The discrete event simulation model was created by applying the DEVS formalism. The M/V Sewol sinking was used as an example to simulate how to effectively navigate compartments of different sizes. The simulation results showed the optimal dive time with the number of decompression chambers needed to navigate the compartment as a variable. Based on this, we propose a methodology for efficient navigation planning while ensuring diver safety.

• Journal of Mechanical Science and Technology
• /
• v.14 no.8
• /
• pp.807-815
• /
• 2000

In this paper, modified discrete time preview control algorithms for active and semi-active suspension systems are derived based on a simple mathematical 4 DOF half-car model. The discrete time preview control laws for ride comfort are employed in the simulation. The algorithms for MIMO system contain control strategies reacting against body forces that occur at cornering, accelerating, braking, or under payload, in addition to road disturbances. Matlab simulation results for the discrete time case are compared with those for the continuous time case and the appropriateness of the discrete time algorithms are verified by the of simulation results. Passive, active, and semi-active system responses to a sinusoidal input and an asphalt road input are analysed and evaluated. The simulation results show the extent of performance degradation due to numerical errors related to the length of the sampling time and time delay.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.4
• /
• pp.45-52
• /
• 2011

In the real-field of control cases for robot manipulators, there always exists a modeling error, which results the model has the uncertainties in its parameters and/or structure. In many modem applications, digital computers are extensively used to implement control algorithms to control such systems. The discretization of the nonlinear dynamic equations of such systems results in a complicated discrete dynamic equations. Therefore, it will be difficult to design a discrete-time controller to give good tracking performances in the presence of certain uncertainties. In this paper, a discrete-time sliding mode control algorithm for nonlinear and time varying robot manipulators with uncertainties is presented. Sufficient conditions for guaranteeing the convergence of the discrete-time SMC system are derived. As example simulations, the proposed SMC algorithm is applied to a two-link robotic manipulator with unknown dynamics. The results of the simulation indicate that the developed control scheme is effective in manipulators and electro-mechanical system control.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.10 no.3
• /
• pp.219-225
• /
• 2005

In this paper, a new discrete-time small signal model of an average current mode control is proposed to predict the inductor current responses. Compared to the peak current mode control, the analysis of the average current mode control is difficult because of its presence of an compensation network. By utilizing sampler model, a new discrete-time small signal model is derived and used to predict the behaviors of an inductor current of average current mode control employing generalized compensation networks. In order to show the usefulness of the proposed model, prediction results of the proposed model are compared to those of the circuit level simulator, PSIM and experiment.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1411-1417
• /
• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2000.11a
• /
• pp.142-149
• /
• 2000

Most of supply chain simulation models have been developed on the basis of discrete-event simulation. Since supply chain systems are neither completely discrete nor continuous, the need of constructing a model with aspects of both discrete-event simulation and continuous is provoked, resulting in a combined discrete-continuous simulation. Continuous simulation concerns the modeling over time of a system by a representation in which the state variables change continuously with respect to time. In this paper, an architecture of combined modeling for supply chain simulation is proposed, which presents the equation of continuous part in supply chain and how these equations are used supply chain simulation models. A simple supply chain model is demonstrated the possibility and the capability of this approach.

• Journal of the Korean Statistical Society
• /
• v.22 no.1
• /
• pp.133-138
• /
• 1993

We consider two estimators of the infection rate in the simple stochastic epidemic model. It is shown that the maximum likelihood estimator of teh infection rate under the discrete time observation does not have the moment of any positive order. Some properties of the Choi-Severo estimator, an approximation to the maximum likelihood estimator, are also investigated.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1331-1336
• /
• 2008

This paper suggests a method using Bayesian inference to estimate the parameters of Weibull distribution and acceleration parameters under the condition that the stresses are time-dependent functions. A Bayesian model based on the discrete time approximation is formulated to infer the parameters of interest from the failure data of the virtual tests and a statistical analysis is considered to decide the most probable mean values of the parameters for reasoning of the failure data.