• Journal of Power Electronics
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• v.18 no.1
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• pp.103-115
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• 2018

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system of electrical vehicles, which may lead to performance degradation or failure, and eventually threaten reliable operation. Therefore, a model-free deadbeat predictive current controller (MFDPCC) for PMSM drive systems is proposed in this study. The data-driven ultra-local model of a surface-mounted PMSM (SMPMSM) drive system that consists of parametric uncertainties and inverter nonlinearity is first established through the input and output data of a SMPMSM drive system. Subsequently, MFDPCC is designed. The performance comparisons and analyses of the proposed MFDPCC, the conventional proportional-integral controller, and the model-based deadbeat predictive current controller for SMPMSM drive systems are implemented via system simulation and experimental tests. Results show the effectiveness and technical advantages of the proposed MFDPCC.

• 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.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.268-269
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• 2017

This paper presents an improved current control strategy for a three-phase grid-connected inverter under distorted grid conditions. Distorted grid condition is undesirable due to negative effects such as power losses and heating problem in electrical equipments. To enhance the power quality of distributed generation systems under such a condition, a modulated finite control set - model predictive control (MFCS-MPC) scheme will be proposed, in which the optimal switching signals of inverter are chosen by online basis using the principle of current error minimization. In addition, the moving average filter (MAF) is used to improve the phase-lock loop in order to obtain the harmonic-free reference currents on the stationary frame. The usefulness of the proposed MFCS-MPC method is proved by the comparative simulation results under different operating conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Current Optics and Photonics
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• v.5 no.6
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• pp.641-651
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• 2021

To handle the servo delay in a real-time adaptive optics system, a linear subspace system identification algorithm was employed to model the system, and the accuracy of the system identification was verified by numerical calculation. Experimental verification was conducted in a real test bed system. Through analysis and comparison of the experimental results, the convergence can be achieved only 200 times with prediction and 300 times without prediction. After the wavefront peak-to-valley value converges, its mean values are 0.27, 4.27, and 10.14 ㎛ when the communication distances are 1.2, 4.5, and 10.2 km, respectively. The prediction algorithm can effectively improve the convergence speed of the peak-to-valley value and improve the free-space optical communication performance.