• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.575-583
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• 2017

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.259-262
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• 1996

This paper proposes an approach in modeling a 4x60inch Allis Chalmers Hydrocone Crusher [1] hydroset and presents some numerical simulation results. The mining and quarry industry is one of the industries which extensively use hydrocone crushers, which are a family of cone crushers, for rock size reduction. Field studies have proved that if proper control and management of these machines is undertaken, they can yield an increased production output of more than 30%, in addition substantial savings in both energy consumption per unit ton produced and manpower can be easily realized. In order to achieve these economic benefits, high performance from these machines is expected. Implementing automatic control for such machines would be a great leap towards achieving both economic benefits and more effective fool-proof predictive maintenance. But, unfortunately, for such a control system to be designed, it necessary to make a mechatronical model of this plant. The plant model is able to give us an insight into variations of both the plant gap setting (displacement) and system pressure due to variable loading arising from the crushing process.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.9
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• pp.33-41
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• 2015

To improve the power quality of distributed generation (DG) systems even in the presence of distorted grid condition, dual current control scheme of a grid-connected inverter is proposed. The proposed current control scheme is achieved by decomposing the inverter state equations into the fundamental and harmonic components. The derived models are employed to design dual current controllers. The conventional PI decoupling current controller is used in the fundamental model to control the main power flow in DG systems. At the same time, the predictive control is applied in the harmonic model to suppress undesired harmonic currents to zero quickly. To decompose the voltage inputs and state variables into the fundamental and harmonic components, the fourth order band pass filter (BPF) is designed in the discrete-time domain for a digital implementation. For experimental verification, 2kVA prototype of a grid-connected inverter has been constructed using digital signal processor (DSP) TMS320F28335. The effectiveness of the proposed strategy is demonstrated through comparative simulation and experimental results.

• Journal of Drive and Control
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• v.21 no.3
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• pp.28-35
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• 2024

This study compared performances of PID (Proportional Integral Derivative), SMC (Sliding Mode Control), and MPC (Model Predictive Control) strategies applied to a 2DOF (Degree Of Freedom) drone tracking robot. The developed 2DOF robot utilized a depth camera with an IMU (Inertial Measurement Unit), laser pointers, and servo motors to rapidly detect and track objects. Image processing was conducted using the YOLO deep learning model. Through this setup, controllers were attached to the robot to track random drone movements, comparing performances in terms of accuracy and energy consumption. This study revealed that while SMC demonstrated precise tracking without deviating from the path, both PID and MPC controllers showed deviations. Performance-wise, SMC is superior. However, considering economic aspects, PID is more advantageous due to its lower power consumption and relatively minor tracking errors.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.373-397
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• 2020

Heave compensation is a vital part of various marine and offshore operations. It is used in various applications, including the transfer of cargo between two vessels in the open ocean, installation of topsides of an offshore structure, offshore drilling and for surveillance, reconnaissance and monitoring. These applications typically involve a load suspended from a hydraulically powered winch that is connected to a vessel that is undergoing dynamic motion in the ocean environment. The goal in these applications is to design a winch controller to keep the load at a regulated height by rejecting the net heave motion of the winch arising from ship motions at sea. In this study, we analyze and compare the performance of various control algorithms in stabilizing a suspended load while the vessel is subjected to changing sea conditions. The KCS container ship is chosen as the vessel undergoing dynamic motion in the ocean. The negative of the net heave motion at the winch is provided as a reference signal to track. Various control strategies like Proportional-Derivative (PD) Control, Model Predictive Control (MPC), Linear Quadratic Integral Control (LQI), and Sliding Mode Control (SMC) are implemented and tuned for effective heave compensation. The performance of the controllers is compared with respect to heave compensation, disturbance rejection and noise attenuation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.6
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• pp.132-141
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• 2013

Robotic Drilling Systems(RDSs) set the standard for the factory automation systems in aerospace manufacturing. With the benefits of cost effective drilling and predictive maintenance, RDSs can provide greater flexibility in the manufacturing process. The system can be easily adopted to manage very complex and time-consuming processes, such as automated fastening hole drilling processes of large aircraft sections, where it would be difficult accomplished by workers following teaching or conventional guided methods. However, in order to build an RDS based on a CAD model, the precise calibration of the Tool Center Point(TCP) must be performed in order to define the relationships between the fastening-hole target and the End Effector(EEF). Based on the kinematics principle, the robot manipulator requires a new method to correct the 3D errors between the CAD model of the reference coordinate system and the actual measurements. The system can be called as a successful system if following conditions can be met; a. seamless integration of the industrial robot controller and the IO Level communication, b. performing pre-defined drilling procedures automatically. This study focuses on implementing a new technology called iGPS into the fastening-hole-drilling process, which is a critical process in aircraft manufacturing. The proposed system exhibits better than 100-micron 3D accuracy under the predefined working space. Based on the proposed EEF fastening-hole machining process, the corresponding processes and programs are developed, and its feasibility is studied.

• Journal of Advanced Navigation Technology
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• v.18 no.3
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• pp.215-222
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• 2014

In this paper, parafoil airdrop system has been designed and analyzed. 6-degrees of freedom (6-DOF) model of the parafoil system is set up. Nonlinear model predictive control (NMPC) and Proportion integration differentiation (PID) methods were separately applied to adjust the flap yaw angle. Compared the results of setting time and overshoot time of yaw angle, it is found that the of yaw angle is more stable by using PID method. Then, trajectory following controller was designed based on the simulation results of trajectory following effects, which was carried out by using MATLAB. The lateral offset error of parafoil trajectory can be eliminated by its lateral deviation control. The later offset deviation reference was obtained by the interpolation of the current planning path. Moreover, using the designed trajectory, the trajectory following system was simulated by adding the wind disturbances. It is found that the simulation result is highly agreed with the designed trajectory, which means that wind disturbances have been eliminated with the change of yaw angle controlled by PID method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.4 s.310
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• pp.35-44
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• 2006

The network induced transmission delay deteriorates the performance and stability of the real-time distributed control system on LonWorks over IP (LonWorks/IP) virtual device network (VDN). LonWorks/IP virtual device network is an integrated form of LonWorks device network and IP data network. The time delay in servo control on the LonWorks/IP-based VDN has highly stochastic nature. In the real-time distributed servo applications for predictive maintenance on the factory floor, timely response is essential.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.