• Smart Structures and Systems
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• v.11 no.6
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• pp.623-635
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• 2013

This paper investigates application of a control algorithm called model predictive sliding mode control (MPSMC) to active vibration suppression of a cantilevered aluminum beam. MPSMC is a relatively new control algorithm where model predictive control is employed to enhance sliding mode control by enforcing the system to reach the sliding surface in an optimal manner. In previous studies, it was shown that MPSMC can be applied to reduce hysteretic effects of piezoelectric actuators in dynamic displacement tracking applications. In the current study, a cantilevered beam with unknown mass distribution is selected as an experimental test bed in order to verify the robustness of MPSMC in active vibration control applications. Experimental results show that MPSMC can reduce vibration of an aluminum cantilevered beam at least by 29% regardless of modified mass distribution.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.134-138
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• 1996

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• Nuclear Engineering and Technology
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• v.38 no.1
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• pp.81-92
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• 2006

In this study, the core dynamics of a PWR reactor is identified online by a recursive least-squares method. Based on the identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to designing an automatic controller for the thermal power control of PWR reactors. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, this procedure for solving the optimization problem is repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired temperature, as well as minimizing the variation of the control rod positions. In addition, the objectives are subject to the maximum and minimum control rod positions as well as the maximum control rod speed. Therefore, a genetic algorithm that is appropriate for the accomplishment of multiple objectives is utilized in order to optimize the model predictive controller. A three-dimensional nuclear reactor analysis code, MASTER that was developed by the Korea Atomic Energy Research Institute (KAERI) , is used to verify the proposed controller for a nuclear reactor. From the results of a numerical simulation that was carried out in order to verify the performance of the proposed controller with a $5\%/min$ ramp increase or decrease of a desired load and a $10\%$ step increase or decrease (which were design requirements), it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.104-106
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• 2005

In this work, a PWR reactor core dynamics is identified online by a recursive least squares method. Based on this identified reactor model consisting of the control rod position and the core average coolant temperature, the future average coolant temperature is predicted. A model predictive control method is applied to design an automatic controller for thermal power control in PWRs. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted core coolant temperature and the desired one, and the variation of the control rod positions. Also, the objectives are subject to maximum and minimum control rod positions and maximum control rod speed. Therefore, the genetic algorithm that is appropriate to accomplish multiple objectives is used to optimize the model predictive controller. A 3-dimensional nuclear reactor analysis code, MASTER that was developed by Korea Atomic Energy Research Institute (KAERI), is used to verify the proposed controller for a nuclear reactor. From results of numerical simulation to check the performance of the proposed controller at the 5%/min ramp increase or decrease of a desired load and its 10% step increase or decrease which are design requirements, it was found that the nuclear power level controlled by the proposed controller could track the desired power level very well.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1975-1980
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• 2004

A parallel active power filter is designed and implemented to compensate for undesired current harmonics generated by a nonlinear load. The filter works based on PWM strategy and control signal is generated using a model predictive controller. To evaluate the achievements, a PI controller is also designed and implemented. Experimental results indicate about 50% increase in the efficiency over PI controller.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• Journal of Power Electronics
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• v.19 no.1
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• pp.168-178
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• 2019

Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

• 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.