• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.735-743
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• 2012

In this paper, we present model predictive control (MPC) applied to lane keeping system (LKS) based on a vision module. Due to a slow sampling rate of the vision system, the conventional LKS using single rate control may result in uncomfortable steering control rate in a high vehicle speed. By applying MPC using multi-rate Kalman filter to active steering control, the proposed MPC-based active steering control system prevents undesirable saturated steering control command. The effectiveness of the MPC is validated by simulations for the LKS equipped with a camera module having a slow sampling rate on the curved lane with the minimum radius of 250[m] at a vehicle speed of 30[m/s].

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1400-1403
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• 1996

In this study, we applied the model predictive control(MPC) to Multi-loop control structure. Since MPC has many advantage for MIMO process and constraints handling, it induces the better performance to apply MPC to multi-loop control. And we suggest the advanced method to reduce the calculation load using the wavelet transform. It shows the possibility to substitute the existing PID control based structure with MPC.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1132-1138
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• 2012

The MPC (Model Predictive Control) is one of the techniques that can be used to control an unmanned vehicle. It predicts the future vehicle trajectory using the dynamic characteristic of the vehicle and generate the control value to track the reference path. If some obstacles are detected on the reference paths, the MPC can generate control value to avoid the obstacles imposing the inequality constraints on the MPC cost function. In this paper, we propose an obstacle modeling algorithm for MPC with inequality constraints for obstacle avoidance and a method to set selective constraint on the MPC for stable obstacle avoidance. Simulations with the field test data show successful obstacle avoidance and way point tracking performance.

• 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.

• 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.

• 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.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.601-611
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• 2017

This paper proposes a simultaneous control of frequency deviation and electric vehicles (EVs) battery state of charge (SOC) using load frequency control (LFC) and EV controllers. In order to provide both frequency stabilization and SOC schedule near optimal performance within the whole operating regions, a multiple-input multiple-output model predictive control (MIMO-MPC) is employed for the coordination of LFC and EV controllers. The MIMO-MPC is an effective model-based prediction which calculates future control signals by an optimization of quadratic programming based on the plant model, past manipulate, measured disturbance, and control signals. By optimizing the input and output weights of the MIMO-MPC using particle swarm optimization (PSO), the optimal MIMO-MPC for simultaneous control of the LFC and EVs, is able to stabilize the frequency fluctuation and maintain the desired battery SOC at the certain time, effectively. Simulation study in a two-area interconnected power system with wind farms shows the effectiveness of the proposed MIMO-MPC over the proportional integral (PI) controller and the decentralized vehicle to grid control (DVC) controller.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.237-242
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• 2015

This paper presents a method to enhance tracking performance of an input-constrained bilinear system using MPC (Model Predictive Control) when a feasible tracking control is known. Since the error dynamics induced by the known tracking control is asymptotically stable, there exists a Lyapunov function for the stable error dynamics. By defining a cost function including the Lyapunov function and describing tracking performance, an MPC law is derived. In simulation, the performance of the proposed MPC law is demonstrated by applying it to a converter model.

• 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.