• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.908-913
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• 2014

In this paper, we propose a method by which QP (Quadratic Programming) problems can be solved in realtime so that we can implement the predictive control algorithm on a microcontroller system. Firstly, we derive a solution to QP problems by converting the original QP problems to its equivalent least squares with inequalities. Secondly, we propose a predictive control algorithm that can give good realtime computation performance by utilizing the fact that some parameters needed for solving QP problems can be computed offline. Finally, we illustrate that the proposed method can give good realtime features by running the C-code application constructed using the proposed method on a microncontroller system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.376-383
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• 2001

Factors leading to poor control of the steam generator in a nuclear power plant are nonminimum phase characteristics, unreliable of flow measurements and nonlinear characteristics, which increase more at low power(below 20%) operation. And the study of problems for water level control in the steam generator is that design water level controller only power renge, not entire. This paper introduces a model predictive control(MPC) algorithm for solving poor control factors and quadratic programming(QP) for solving input constraints. Also presents the design method of stable model predictive controller in the entire power range. The simulation results show the efficiency of proposed MPC controller by comparing with PI controller, and effect of the design parameters.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.1 s.109
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• pp.61-66
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• 2005

The grade change operations In paper mills exhibit inherent nonlinear dynamic characteristics. For this reason, the conventional model predictive control techniques based on linear process models are not adequate for the grade change operations. In this paper, a bilinear model for the nonlinear grade change processes was presented first and optimal input variables were calculated by using one-step-ahead predictive control method. Numerical simulations showed that the control performance lied within acceptable range and that the bilinear model predictive control scheme was highly promising control strategy for the grade change operations.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.778-780
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• 1996

The predictive maximum demand controllers often bring about large number of control actions during the every integrating period and/or undesirable load-disconnecting operations during the begining stage if the integrating period. To solve these problems, a fuzzy predictive maximum demand control algorithm is proposed, which determines the sensitivity if control action by urgency if the load interrupting action along with the predicted demand reading to the target or the time arriving at the end stage if the integrating period. A prototype controller employing the proposed algorithm also is developed and its performances are tested by PROCOM SYSTEMS Corperation of Korea.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.57-61
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• 2001

A conceptional framework is proposed in which a general feedback predictive controller is taken to be a feedback interconnection of controller and GPC (General predictive Control). Numerical example are included to illustrate the procedure and to show the performance of the control system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.4
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• pp.206-210
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• 2002

The transmission delays in the networked control systems affect the performance and stability. For covering the delays, this paper proposes NCS including the predictive strategy. The proposed method shows that the two cases such as stable and unstable system are well behaved.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.566-569
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• 2009

In this paper, we propose a less conservative a linear matrix inequality (LMI) condition for the constrained robust model predictive control of systems with input constraints and polytopic uncertainty. Systems with input constraints are represented as perturbed systems with sector bounded conditions. For the infinite horizon control, closed-loop stability conditions are obtained by using a parameter dependent Lyapunov function. The effectiveness of the proposed method is shown by an example.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.145-149
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• 1993

This paper considers the adaptive predictive control problem of a system characterized by a multiplexed measurements and multirate sampling mechanism. Plant outputs are measured in various sampling rates through a multiplexed measurement system where a single common instrument is shared by several controllers. In general, output measurement sampling rate is assumed to be slower that input update rate. An adaptive predictive control algorithm is developed for systems with multiplexed measurements.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.419-424
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• 1992

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.