• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.864-867
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• 1995

Presented in the paper is a structured approach to modeling automated manufacturing system (AMS) in the form of an event graph. The proposed two-phase procedure for formal modeling is 1) reference modeling by schematic supervisory control modeling and 2) event graph transformation from supervisory control model. Also described is a formal model for a small-sized FMS in the form of an event graph.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2941-2946
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• 2007

A model of the cardiovascular system coupling cell, hemodynamics and autonomic nervecontrol function is proposed for analyzing heart mechanics. We developed a comprehensive cardiovascular model with multi-physics and multi-scale characteristics that simulates the physiological events from membrane excitation of a cardiac cell to contraction of the human heart and systemic blood circulation and ultimately to autonomic nerve control. Using this model, we delineatedthe cellular mechanism of heart contractility mediated by nerve control function. To verify the integrated method, we simulated a 10% hemorrhage, which involves cardiac cell mechanics, circulatory hemodynamics, and nerve control function. The computed and experimental results were compared. Using this methodology, the state of cardiac contractility, influenced by diverse properties such as the afterload and nerve control systems, is easily assessed in an integrated manner.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1435-1437
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• 2003

In this paper, we proposed static output feedback model predictive control for Wiener models. We adopted polytopic uncertainty description of Wiener Model Predictive Control (WMPC) algorithms for considering output nonlinearities. Robust stability conditions have been presented under which the closed loop stability of static output feedback MPC is guaranteed. The proposed control law is determined from the static output feedback WMPC based on the current estimated state with explicit satisfaction of input constraints.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.211-214
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.967-973
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• 2009

The model matching problem of asynchronous sequential machines is to design a corrective controller such that the stable-state behavior of the closed-loop system matches that of a prescribed model. In this paper, we address model matching when the external input set consists of controllable inputs and uncontrollable ones. Like in the frame of supervisory control of Discrete-Event Systems (DES), uncontrollable inputs cannot be disabled and must be transmitted to the plant without any change. We postulate necessary and sufficient conditions for the existence of a corrective controller that solves model matching despite the influence of uncontrollable events. Whenever a controller exists, the algorithm for its design is outlined. To illustrate the physical meaning of the proposed problem, the closed-loop system of an asynchronous machine with the proposed control scheme is implemented in VHDL code.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1131-1135
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• 2014

This paper proposes the enhanced faulty node detection scheme with hybrid algorithm using Markov-chain model on BCH (Bose-Chaudhuri-Hocquenghem) code in naval distributed control networked systems. The probabilistic model-driven approach, on Markov-chain model, in this paper uses the faulty weighting interval factors, which are based on the BCH code. In this scheme, the master node examines each slave-nodes continuously using three defined states : Good, Warning, Bad-state. These states change using the probabilistic calculation method. This method can improve the performance of detecting the faulty state node more efficiently. Simulation results show that the proposed method can improve the accuracy in faulty node detection scheme for real-time naval distributed control networked systems.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.105-105
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• 2000

In this paper, an integrated control of the semi-active suspension system using a reduced full-car model is investigated. By including the reduced full-car dynamics in the control law, the semi-act ive suspension system is able to control not only the vertical acceleration but also the roll and pitch mot ions of the car body, which is not Possible with a quarter-car model or a half-car model. The damping forces for the semi-active dampers are designed to track the damping forces of the skyhook controller designed from the reduced full car dynamics. Computer simulations and experimental results using a real car are also included.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1471-1474
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.673-675
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• 1995

A nonlinear process-model based control for main steam temperature control of a 100MW oil-fired drum-type fossil power plant is delveloped and its performances are compared to those of the conventional PID control. The process model for simulation is derived based "first priciple approach" and is validated in steady and transient conditions. The model is in good agreements with the field test data. Performances of the nonlinear PMBC for main steam temperature control are far superior to those of PID in all aspects for the disturbances of ramp increase in load and step change in fuel Btu value.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.