• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.3
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• pp.200-208
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• 2001

In indoor temperature control of a heat pump, a reduction in energy consumption is very important. However, most control schemes for heat pumps have focused only on control performance such s settling time and steady-state error. In this paper, the model predictive control (MPC) which includes the energy-related variable in this cost function is proposed. By computing the control signal minimizing this cost function, the trade-off between energy reduction and temperature control performance can be obtained. Since the MPC required the process model, the dynamic mode of a heat pump is also obtained by the system identification technique. Performance of the proposed MPC considering energy efficiency is compared with the two other control schemes. It si shown that the proposed scheme can consume less energy thant hte others in achieving similar control performance.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.5
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• pp.429-434
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• 2013

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

• Journal of Ocean Engineering and Technology
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• v.33 no.2
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• pp.139-145
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• 2019

It is important that an MGO Chiller System, which is one of the sulfur oxide emission control technologies, is designed to meet the fuel temperature requirements, even with sudden engine load changes. Three different control algorithms (PI, Cascade, and MPC) were applied to an indirect MGO chiller system to compare and analyze the outlet temperature dynamic characteristics of the system through a case study. The results showed that the MPC control method had the best temperature following characteristics in the case study, and the temperature deviation range was reduced by approximately 5% compared to the PI control method.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.21-22
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• 2014

본 논문은 5-레벨 NPC/H-브릿지 (Neutral Point Clamped/H-bridge) 인버터의 최적 제어 세트 (finite-control-set) 모델 예측 제어 (MPC: Model Predictive Control) 방법을 제안한다. NPC/H-브릿지 인버터의 출력 전류 제어 및 DC-link 커패시터 전압 균형을 유지하기 위해 출력 전류와 DC-link 커패시터 전압을 예측하고, 하나의 비용 함수 (cost function)을 통해 최적의 스위칭 상태를 출력한다. PSIM 시뮬레이션을 통해 제안된 제어 알고리즘의 검증하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.562-567
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• 2006

Model Predictive Control(MPC) system uses the current input which minimizes the difference between the desired output and the estimated output in the receding horizon scheme. In many cases (for example, system with constraints or nonlinear system), however, it is not easy to find the optimal solution to the above problem. In this paper, we show that real number coding genetic algorithm can be used to solve the optimal problem for MPC effectively. Also, we show by simulation that the real coding algorithm is mote natural and advantageous than the digital coding one.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.327-333
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• 2015

Wind turbines, in the case of less than rated wind speed, is controlled to achieve maximum power. MPC(Maximun Power Control) method, by controlling the rotational speed of the generator, is a method to achieve maximum power but should know the wind speed. However, for several reasons, there have been proposed methods of estimating the wind speed rather than measuring wind speed. TSR(Tip Speed Ratio) is needed to know to estimate the wind speed. However, a complex interaction formula has to be solved to find a TSR. Therefore, many methods have been suggested to solve a complex interaction formula. In this paper, the new method has been proposed to simplify the complicated interaction formula by using the regression method. Matlab/Simulink is used to simulate and to verify the proposed method.

• 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.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.