• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.453-462
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• 2016

This paper proposes a torque control method for surface mounted permanent magnetic synchronous motor (PMSM) driven by a 2-level voltage source driven inverter, which has fast torque response and small torque ripple. The proposed torque control method follows the finite control set model predictive control (FCS-MPC) strategy. A reference state is derived at each time step for the given time varying torque reference and the cost index is defined so that the tracking error for this reference state should be penalized. The choice of an optimal output voltage vector is made first from the 6 possible active voltage vectors of the 2-level voltage source inverter. Then a modulation factor for the chosen optimal voltage vector is obtained so that the torque ripple can be reduced further. It is shown that the proposed FCS-MPC control method yields fast torque tracking response and small torque ripple through simulation and experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2189-2196
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• 2016

This paper proposes a new finite control set-model predictive control (FCS-MPC) method for induction motors. In the method, the reference state that satisfies the given torque and rotor flux requirements is derived. Cost indices for the FCS-MPC are defined using the state tracking error, and a linear matrix inequality is formulated to obtain a proper weighting matrix for the state tracking error. The on-line procedure of the proposed FCS-MPC comprises of two steps: select the output voltage vector of the two level inverter minimizing the cost index and compute the optimal modulation factor of the minimizing output voltage vector in order to reduce the state tracking error and torque ripple. The steady state tracking error is removed by using an integrator to adjust the reference state. The simulation and experimental results demonstrated that the proposed FCS-MPC shows good torque, rotor flux control performances at different rotating speeds.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.786-796
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• 2014

This paper presents a new approach to design a robust tracking controller for linear time-invariant systems with uncertain time-delay. By introducing the model following control (MFC) structure which consists of two loops in nature, we show that the controller is capable of having a predictive control action and effectively tracking the reference output with a desired transient response as well. Three design techniques to achieve good tracking performance are suggested. It is also analytically shown that the tracking performance of the proposed scheme is more robust than that of typical single-loop feedback structure. An illustrative example is given to compare the tracking performances of the proposed methods with a single loop method.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.369-378
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• 2012

A load-following operation in APR+ nuclear plants is necessary to reduce the need to adjust the boric acid concentration and to efficiently control the control rods for flexible operation. In particular, a disproportion in the axial flux distribution, which is normally caused by a load-following operation in a reactor core, causes xenon oscillation because the absorption cross-section of xenon is extremely large and its effects in a reactor are delayed by the iodine precursor. A model predictive control (MPC) method was used to design an automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control for APR+ nuclear plants. Some tracking controllers employ the current tracking command only. On the other hand, the MPC can achieve better tracking performance because it considers future commands in addition to the current tracking command. The basic concept of the MPC is to solve an optimization problem for generating finite future control inputs at the current time and to implement as the current control input only the first 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 support vector regression (SVR) model that is used widely for function approximation problems is used to predict the future outputs based on previous inputs and outputs. In addition, a genetic algorithm is employed to minimize the objective function of a MPC control algorithm with multiple constraints. The power level and ASI are controlled by regulating the control banks and part-strength control banks together with an automatic adjustment of the boric acid concentration. The 3-dimensional MASTER code, which models APR+ nuclear plants, is interfaced to the proposed controller to confirm the performance of the controlling reactor power level and ASI. Numerical simulations showed that the proposed controller exhibits very fast tracking responses.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.225-228
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• 1990

The major difficulty in distillation column control lies in executing the set point tracking and the disturbance rejection, because of continuous changes in model order and dead time. For that, generalized predictive control(GPC) was applied to distillation column control. Recursive least square method was used to adjust the changes of model order and dead time. Quadratic progamming(QP) was used to solve the constraint problems in control action and the rate of control action. As a result of the simulation on the dynamic simulator(SPEEDUP) and the experiment on pilot plant, the ability of the set point tracking and the disturbance rejection was acceptable to apply to the real distillation column.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.160-163
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• 1996

This paper deals with MCMBPC(Multivariable Constrained Model Based Predictive Controller) for nonlinear boiler system with noise and disturbance. MCMBPC is designed by linear state space model obtained from some operating point of nonlinear boiler system and Kalman filter is used to estimate the state with noise and disturbance. The solution of optimization of the cost function constrained on input and/or output variables is achieved using quadratic programming, viz. singular value decomposition (SVD). The controller designed is shown to have excellent tracking performance via simulation applied to nonlinear dynamic drum boiler turbine model for 16OMW unit.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.49-55
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• 1992

In this paper, we developed a Receding Horizon Predictive Control for Stochastic state space models(RHPCS). RHPCS was designed to minimize a quadratic cost function. RHPCS consists of Receding Horizon Tracking Control(RHTC) and a state observer. It was shown that RHPCS is equivalent to Generalized Predictive Control(GPC) when the underlying state space model is equivalent to the I/O model used in the design of GPC. The equivalence between GPC and RHPCS was shown through. the comparison of the transfer functions of the two controllers. RHPCS provides a time-invarient optimal control law for systems for which GPC can not be used. The stability properties of RHPCS was derived. From the GPC's equivalence to RHPCS, the stability properties of GPC were shown to be the same as those for RHTC.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.3
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• pp.249-256
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• 2014

This paper proposes a torque control method of permanent magnet synchronous motor, which has small torque ripple. The proposed control method is using the finite control set-model predictive control(FCS-MPC) strategy. An optimal input voltage vector minimizing a cost function is chosen among 6 passible active input voltage vectors following the FCS-MPC strategy. Then, a modulation factor for the optimal input voltage vector is computed to minimize the torque ripple. Thus, the proposed control method yields fast torque response and small torque ripple. The efficacy of the proposed method was verified through simulation and experiment.