• Journal of Power Electronics
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• v.19 no.4
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• pp.989-999
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• 2019

This paper proposes a fault-tolerant control strategy with finite control set model predictive control (FCS-MPC) based on hierarchical optimization for five-level H-bridge neutral-point-clamped (5L-HNPC) inverter-fed induction motor drives. Fault-tolerant operation is analyzed, and the fault-tolerant control algorithm is improved. Adopting FCS-MPC based on hierarchical optimization, where the voltage is used as the controlled objective, called model predictive voltage control (MPVC), the postfault controller is simplified as a two layer control. The first layer is the voltage jump limit, and the second layer is the voltage following control, which adopts the optimal control strategy to ensure the current following performance and uniqueness of the optimal solution. Finally, simulation and experimental results verify that 5L-HNPC inverter-fed induction motor drives have strong fault tolerant capability and that the FCS-MPVC based on hierarchical optimization is feasible.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.199-209
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• 2015

This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.259-261
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• 2000

MPC 제어기가 고정된 출력준위에서 선형 증기발생기 모델을 위해 설계되었다. 고정된 출력준위에서 설계된 제어기는 단지 입력가중치만을 변경하므로써 어떤 다른 출력준위에서 좋은 성능을 보여주었다. 또한 증기발생기는 비선형 특성을 갖고 있기 때문에 제안된 제어 알고리듬이 실질적인 성능 및 안전성을 검증하기 위하여 증기발생기의 비선형 모델에 적용되었으며, 좋은 성능을 보여주었다.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.35-38
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• 2002

In this paper, the design of explicit rate-based congestion control in high speed communication networks is considered. The goal of congestion control is to achieve high link utilization, low packet loss, low delay, and fairness among the best-effort sources. To deal with the propagation delays associated with the best effort sources, An MPC technique is employed to solve the congestion problem[1] here. However, the problem with this method is that the closed loop performance relies highly on the knowledge of average service rate. This paper focuses on coping with the problem described above by using a CARIMA model for service rate(available rate).

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.801-806
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• 2015

In this paper, we propose cascade-form velocity controller for a permanent magnet synchronous motor (PMSM). The proposed controller consists of a sliding-mode controller (SMC) for the inner current control loop and a model-predictive controller (MPC) for the outer velocity control loop. With SMC, we can ensure that the current tracking error always converges to zero in finite time. The SMC is designed to track the desired currents. Additionally, with MPC, we can obtain the optimal velocity control input which minimizes the cost function. Constraint conditions for input and input variation are included in the MPC design. The simulation results are included to validate the performance of the proposed controller.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1889-1900
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• 2018

Harmonic current mitigation is vital in power distribution networks owing to the inflow of nonlinear loads, distributed generation, and renewable energy sources. The active power filter (APF) is the current electrical equipment that can dynamically compensate for harmonic distortion and eliminate asymmetrical loads. The compensation performance of an APF largely depends on the control strategy applied to the voltage source inverter (VSI). Model predictive control (MPC) has been demonstrated to be one of the effective control approaches to providing fast dynamic responses. This approach covers different types of power converters due to its several advantages, such as flexible control scheme and simple inclusion of nonlinearities and constraints within the controller design. In this study, a finite control set-MPC technique is proposed for the control of VSIs. Unlike conventional control methods, the proposed technique uses a discrete time model of the shunt APF to predict the future behavior of harmonic currents and determine the cost function so as to optimize current errors through the selection of appropriate switching states. The viability of this strategy in terms of harmonic mitigation is verified in MATLAB/Simulink. Experimental results show that MPC performs well in terms of reduced total harmonic distortion and is effective in APFs.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.4
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• pp.467-474
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• 2001

The mathematical model of a air-conditioning system is generally very complex and difficult to apply to controller design. In this paper, simple models applicable to the controller design are obtained by modeling the air-conditioning system by single-input single-output between compressor speed and indoor temperature, and by multi-input single-output between compressor speed, indoor fan speed and indoor temperature. Using these empirical models, model predictive control(MPC) technique was implemented for indoor temperature control of the air-conditioning system. It has been shown from various experiments that the indoor temperature control based on the MPC scheme yields reasonably good tracking performance with smooth changes in plant inputs. this multi-input multi-output MPC approach can be extended to multi air- conditioning systems where the conventional PID control scheme is very difficult to apply.

• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.396-397
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• 2019

본 논문은 유한요소 모델예측제어(FCS-MPC)의 샘플링 시간을 가변하여 표면부착형 영구자석 동기 전동기(SPMSM)의 토크 리플을 개선하고 스위칭 손실을 저감하는 가변 샘플링 시간이 적용된 모델예측제어 기법을 제안한다. 기존 FCS-MPC는 토크 리플을 저감하기 위해 고정 샘플링 시간을 짧게 설정하였다. 고정 샘플링 시간을 짧게 설정함에 따라, 전압벡터의 변경횟수가 증가하여 스위칭 손실이 증가하였다. 본 논문은 이러한 문제점을 해결하기 위해 가변 샘플링 시간이 적용된 FCS-MPC를 통해 토크 리플을 저감하고, 전압벡터의 변경횟수를 감소시켜 스위칭 손실을 저감하였다. 본 논문에서 제안하는 기법은 시뮬레이션을 통해 증명되었다.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.1.2-6
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• 1994

A state or the arts in Japanese chemical process control is reviewed based on experience in applying advanced process control schemes to several industrial chemical processes. The applications validate model predictive control (MPC), the most popular advanced control scheme in the process control community, as, indeed, a powerful and practical control algorithm. However, at the same time, it is elucidated that MPC can solve only the control algorithm part of the problem and one needs chemical and systems engineering aspects to solve the entire problem. By illustrating several industrial process control problems, the need for chemical engineering aspects as well as the future direction for process control are addressed, especially in light or current attitudes toward product quality.