• Journal of Power Electronics
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• v.17 no.3
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• pp.610-620
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• 2017

The Finite Control Set-Model Predictive Controller (FCS-MPC) for quasi Z-Source Inverters (qZSIs) is designed to reduce the number of sensors by proposing a current observer for the inductor current. Unlike the traditional FCS-MPC algorithm, the proposed model removes the inductor current sensor and observes the inductor current value based on the deposited prior optimized state as well as the capacitor voltage during this state. The proposed observer has been validated versus a typical MPC. Then, a comparative study between the proposed Modified Finite Control Set-Model Predictive Controller (MFCS-MPC) and a linear PID controller is provided under the same operating conditions. This study demonstrates that the dynamic response of the control objectives by MFCS-MPC is faster than that of the PID. On the other hand, the PID controller has a lower Total Harmonic Distortion (THD) when compared to the MFCS-MPC at the same average switching. Experimental results validate both methods using a DSP F28335.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.911-918
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• 2012

Steam generator level control at low power is difficult due to its adverse thermal hydraulic properties, and is usually conducted by an operator. The basic model predictive control (MPC) is similar to the action of an operator in that the operator knows the desired reference trajectory for a finite period of time and takes the necessary control actions needed to ensure the desired trajectory. An MPC is based on a model; the performance as well as the efficiency of the MPC depends heavily on the exactness of the model. In this study, steam generator models that can describe in detail its thermal hydraulic behaviors, particularly at low power, are used in the MPC design. The design scope is divided into two parts. First, the MPC feedwater controller of the feedwater station is determined, and then the MPC level controller for the overall system is designed. Because the dynamic properties of a steam generator change with the power levels, a realistic situation is simulated by changing the transfer functions of the steam generator at every time step. The resulting MPC controller shows good performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.359-359
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• 2000

Generally, level control in the steam generator of a nuclear power plant is difficulty process control, because the low power operating can lead nonminimum phase characteristics(swell and shrink phenomenon) and flow measurement are unreliable and nonlinear characteristics. This paper presents a framework for solving this problem based on the constrained linear model predictive control and introduces the design of method for the level of the controller in the entire operating power of the steam generator, and compares with conventional PI controller.

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.472-477
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• 2023

In this paper, we proposes the use of Model Predictive Control (MPC) techniques to enable quadcopter drones to effectively follow paths and maintain flight safety even under dynamic external environments and disturbances. Through simulations conducted in MATLAB/Simulink, the performance of two controllers, PID and MPC, is compared in flight scenarios with disturbances. The proposed design method shows that the MPC controller, when compared to the PID controller, exhibits a difference in the Mean Squared Error between the intended flight path and the actual path of the quadcopter drone. This difference is 0.2 in performance under no disturbance, and it increases to 0.8 under disturbance, demonstrating the improved path following accuracy of the MPC controller.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.685-689
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• 2005

In this paper, we consider a robust output feedback model predictive controller(MPC) design for Wiener model. Nonlinearities that couldn't be represented in static nonlinearity block of Wiener model are regarded as uncertainties in linear block. An dynamic output feedback controller design method is presented for Wiener MPC. According to MPC algorithm, the control law is computed based on linear matrix inequality(LMI)at each sampling time by solving convex optimization. Also, a new parameter dependent Lyapunov function is proposed to get a less conservative condition. The results are illustrated with numerical example.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1202-1208
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• 2006

Development of a model predictive control(MPC) algorithm and its application to the condensate recycle process of a cogeneration power station has been conducted. The cogeneration power station has different characteristics from other industrial processes where MPC has been dominantly applied in that the operating mode changes continuously with seasons and we Ether. Such a characteristic makes it difficulty, a linearized model was derived from mass and pressure balances and linearization. The MPC algorithm has been developed so that the controller tuning is easy with one tuning knob for each output and the constrained optimization is solved by an interior point method. Performance of the MPC algorithm has been verified with the numerically simulated process under various disturbance scenarios and mode changes.

• 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.15 no.1
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• pp.202-215
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• 2015

This paper investigates the stability and performance of model predictive controlled active-front-end (AFE) rectifiers for energy storage systems, which has been increasingly applied in power distribution sectors and in renewable energy sources to ensure an uninterruptable power supply. The model predictive control (MPC) algorithm utilizes the discrete behavior of power converters to determine appropriate switching states by defining a cost function. The stability of the MPC algorithm is analyzed with the discrete z-domain response and the nonlinear simulation model. The results confirms that the control method of the active-front-end (AFE) rectifier is stable, and that is operates with an infinite gain margin and a very fast dynamic response. Moreover, the performance of the MPC controlled AFE rectifier is verified with a 3.0 kW experimental system. This shows that the MPC controlled AFE rectifier operates with a unity power factor, an acceptable THD (4.0 %) level for the input current and a very low DC voltage ripple. Finally, an efficiency comparison is performed between the MPC and the VOC-based PWM controllers for AFE rectifiers. This comparison demonstrates the effectiveness of the MPC controller.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2495-2497
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• 2000

Level control in the steam generator of a nuclear power plant is important process. But, the low power operation of nuclear power plant causes nonlinear characteristics and non minimum phase characteristics (swell and shrink), change of delay. So, we can't lead good results with conventional PID controller. Particularly, the design of controller with constraints is necessary. This paper introduces MPC(Model Predictive Control) with constraints and designs a good performance MPC controller in spite of the input constraints and nonlinear characteristics, non-minimum phase characteristics