• 한국항해항만학회지
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• 제47권2호
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• pp.66-74
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• 2023

With the increase of interest in developing Maritime Autonomous Surface Ships (MASS), an optimal ship route planning is gradually gaining popularity as one of the important subsystems for autonomy of modern marine vessels. In the present paper, an optimal ship route planning model for MASS is proposed using a nonlinear MPC approach together with a nonlinear MMG model. Results drawn from this study demonstrated that the optimization problem for the ship route was successfully solved with satisfaction of the nonlinear dynamics of the ship and all constraints for the state and manipulated variables using the nonlinear MPC approach. Given that a route generation system capable of accounting for nonlinear dynamics of the ship and equality/inequality constraints is essential for achieving fully autonomous navigation at sea, it is expected that this paper will contribute to the field of autonomous vehicles by demonstrating the performance of the proposed optimal ship route planning model.

• 대한건축학회논문집:구조계
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• 제35권2호
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• pp.45-52
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• 2019

BEMS(Building Energy Management Systems) have been applied to office buildings and collect relevant building energy data, e.g. temperatures, mass flow rates and energy consumptions of building mechanical systems and indoor spaces. The aforementioned measured data can be beneficially utilized for developing data-driven machine learning models which can be then used as part of MPC(Model Predictive Control) and/or optimal control strategies. In this study, the authors developed ANN(Artificial Neural Network) models of an AHU (Air Handling Unit) and a chiller for a real-life office building using BEMS data. Based on the ANN models, the authors developed optimal control strategies, e.g. daily operation schedule with regard to optimal start and stop of the AHU and the chiller (500 RT). It was found that due to the optimal start and stop of the AHU and the chiller, 4.5% and 16.4% of operation hours of the AHU and the chiller could be saved, compared to an existing operation.

• 드라이브 ㆍ 컨트롤
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• 제16권2호
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.173-176
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• 1994

In this paper a closed-form predictive control which takes the intervalwise receding horizon strategy is presented and its stability properties are investigated. A slate-space form output predictor is derived which is composed of the one-step ahead optimal output prediction, input and output data of the system. A set of feedback gains are obtained using the dynamic programming algorithm so that they minimize a multi-stage quadratic cost function and they are used periodically.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• Journal of Power Electronics
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• 제18권6호
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• pp.1791-1804
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• 2018

To achieve a fast dynamic response and to solve the multi-objective control problems of the output currents, capacitor voltages and system constraints, this paper proposes a deadbeat and hierarchical predictive control with space-vector modulation (DB-HPC-SVM) for five-level nested neutral point piloted (NNPP) converters. First, deadbeat control (DBC) is adopted to track the reference currents by calculating the deadbeat reference voltage vector (DB-RVV). After that, all of the candidate switching sequences that synthesize the DB-RVV are obtained by using the fast SVM principle. Furthermore, according to the redundancies of the switch combination and switching sequence, a hierarchical model predictive control (MPC) is presented to select the optimal switch combination (OSC) and optimal switching sequence (OSS). The proposed DB-HPC-SVM maintains the advantages of DBC and SVM, such as fast dynamic response, zero steady-state error and fixed switching frequency, and combines the characteristics of MPC, such as multi-objective control and simple inclusion of constraints. Finally, comparative simulation and experimental results of a five-level NNPP converter verify the correctness of the proposed DB-HPC-SVM.

• 제어로봇시스템학회논문지
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• 제2권1호
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• pp.60-66
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• 1996

Model Based Predictive Control(MBPC) has been widely used in predictive control since 80's. GPC[1] which is the superset of many MBPC strategies a popular method, but GPC has some weakness, such as insufficient stability analysis, non-applicability to internally unstable systems. However, CRHPC[2] proposed in 1991 overcomes the above limitations. So we chose RHPC based on CRHPC for electric furnace control. An electric furnace which has nonlinear properties and large time delay is difficult to control by linear controller because it needs nearly perfect modelling and optimal gain in case of PID. As a result, those controls are very time-consuming. In this paper, we applied RHPC with equality constraint to electric furnace. The reults of experiments also include the case of RHPC with monotonic weighting improving the transient response and including unmodelled dynamics. So, This paper proved the practical aspect of RHPC for real processes.

• 제어로봇시스템학회논문지
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• 제13권3호
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• pp.268-272
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• 2007

These days many scientists make studies of feedback control system for stability on non-linear state and for the maneuver of flying objects. These feedback control systems have to satisfy trajectory condition and angular conditions, that is to say, controllability and stability simultaneously to achieve mission. In this paper, a design methods using model based control system which consists of spiral predictive model, Q-function included into generalized-work function is shown. It is made a clear that the proposed algorithm using SPM maneuvers for controllability and stability at the same time is successful in attaining our purpose. The feature of the proposed algorithm is illustrated by simulation results. As a conclusion, the proposed algorithm is useful for the control of moving objects.

• 전력전자학회논문지
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• 제23권6호
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• pp.403-407
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• 2018

This study proposes a predictive direct power control method in a modular multilevel converter (MMC) high-voltage direct-current (HVDC) system. The conventional proportional integral (PI)-based control method uses a cascaded connection and requires an optimal gain selection procedure and additional decoupling scheme. However, the proposed control method has a simple structure for active/reactive power control due to the direct power control scheme and exhibits a fast dynamic response by predicting the future status of system variables and considering time delay. The effectiveness of the proposed method is verified by simulation results.