• Journal of Astronomy and Space Sciences
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• v.30 no.1
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• pp.1-10
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• 2013

An integrated orbit and attitude control algorithm for satellite formation flying was developed, and an integrated orbit and attitude software-in-the-loop (SIL) simulator was also developed to test and verify the integrated control algorithm. The integrated algorithm includes state-dependent Riccati equation (SDRE) control algorithm and PD feedback control algorithm as orbit and attitude controller respectively and configures the two algorithms with an integrating effect. The integrated SIL simulator largely comprises an orbit SIL simulator for orbit determination and control, and attitude SIL simulator for attitude determination and control. The two SIL simulators were designed considering the performance and characteristics of related hardware-in-the-loop (HIL) simulators and were combined into the integrated SIL simulator. To verify the developed integrated SIL simulator with the integrated control algorithm, an orbit simulation and integrated orbit and attitude simulation were performed for a formation reconfiguration scenario using the orbit SIL simulator and the integrated SIL simulator, respectively. Then, the two simulation results were compared and analyzed with each other. As a result, the user satellite in both simulations achieved successful formation reconfiguration, and the results of the integrated simulation were closer to those of actual satellite than the orbit simulation. The integrated orbit and attitude control algorithm verified in this study enables us to perform more realistic orbit control for satellite formation flying. In addition, the integrated orbit and attitude SIL simulator is able to provide the environment of easy test and verification not only for the existing diverse orbit or attitude control algorithms but also for integrated orbit and attitude control algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.503-506
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• 1996

An integrated Robot control system for SCARA robot is developed. The system consists of an off-line programming(OLP), software and a robot controller using four digital signal processor(TMS32OC50). The OLP has functions of teaching task, dynamic simulator, three dimensional animation, and trajectory planning. To develop robust dynamic control algorithm, a new sliding mode control algorithm for the robot is proposed. The trajectory tracking performance of these algorithm is evaluated by implementing to SCARA robot(SM5 type) using DSP controller which has conventional PI-FF control algorithm. To make SCARA robot operate according to off-line teaching, an interface between OLP and robot controller in the integrated system is designed. To demonstrate performance of the integrated system, the proposed control algorithm is applied to the system.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.112-121
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• 2001

In this paper, an engine-CVT integrated control algorithm is suggested by considering the powertrain loss, inertia torque and the CVT ratio response lag. The integrated control algorithm consists of (1) the optimal engine power calculation and (2) determining of the optimal throttle valve opening and the optimal CVT ratio. The optimal engine power is obtained by compensating the inertia torque due to the CVT ratio change and the powertrain loss that is calculated iteration procedure. In addition, an algorithm to compensate the effect of the CVT ratio response lag on the drive torque is suggested by the engine speed compensation causing the increased optimal CVT ratio. Simulation results show that the engine-CVT integrated control algorithm developed in this study makes it possible to obtain better engine operation on the optimal operating line, which results in the improved fuel economy while satisfying the driver's demand.

• Journal of Mechanical Science and Technology
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• v.14 no.7
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• pp.764-772
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• 2000

In this paper, an engine-CVT integrated control algorithm is suggested by considering the inertia torque and the CVT ratio change response lag in acceleration. In order to compensate for drive torque time delay due to CVT response lag, two algorithms are presented: (1) an optimal engine torque compensation algorithm, and (2) an optimal engine speed compensation algorithm. Simulation results show that the optimal engine speed compensation algorithm gives better engine operation around the optimal operation point compared to the optimal torque compensation while showing nearly the same acceleration response. The performance of the proposed engine-CVT integrated control algorithms are compared with those of conventional CVT control, and It is found that optimal engine operation can be achieved by using integrated control during acceleration, and improved fuel economy can be expected while also satisfying the driver's demands.

• Journal of Power Electronics
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• v.16 no.6
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• pp.2212-2220
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• 2016

Based on the conventional PID algorithm and the adaptive PID (AD-PID) algorithm, a separate sampling adaptive PID (SSA-PID) algorithm is proposed to improve the transient response of digitally controlled DC-DC converters. The SSA-PID algorithm, which can be divided into an oversampled adaptive P (AD-P) control and an adaptive ID (AD-ID) control, adopts a higher sampling frequency for AD-P control and a conventional sampling frequency for AD-ID control. In addition, it can also adaptively adjust the PID parameters (i.e. $K_p$, $K_i$ and $K_d$) based on the system state. Simulation results show that the proposed algorithm has better line transient and load transient responses than the conventional PID and AD-PID algorithms. Compared with the conventional PID and AD-PID algorithms, the experimental results based on a FPGA indicate that the recovery time of the SSA-PID algorithm is reduced by 80% and 67% separately, and that overshoot is decreased by 33% and 12% for a 700mA load step. Moreover, the SSA-PID algorithm can achieve zero overshoot during startup.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.353-360
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• 2016

Series hybrid electric vehicles (SHEVs) having multiple power sources such as an engine- generator (EnGen), a battery, and an ultra-capacitor require a power control unit with high power density and reliable control operation. However, manufacturing using separate individual power converters has the disadvantage of low power density and requires a large number of power and signal cable wires. It is also difficult to implement the optimal power distribution and fault management algorithm because of the communication delay between the units. In order to address these concerns, this approach presents a design methodology and a power control algorithm of an integrated power converter for the SHEVs powered by multiple power sources. In this work, the design methodology of the integrated power control unit (IPCU) is firstly elaborately described, and then efficient and reliable power distribution algorithms are proposed. The design works are verified with product-level and vehicle-level performance experiments on a 10-ton SHEV.

• International journal of steel structures
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• v.18 no.5
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• pp.1598-1606
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• 2018

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

• Smart Structures and Systems
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• v.23 no.6
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• pp.695-702
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• 2019

The number of cable-stayed bridges has been increasing worldwide, causing issues in maintaining the structural safety and integrity of bridges. The stay cable, one of the most critical members in cable-stayed bridges, is vulnerable to wind-induced vibrations owing to its inherent low damping capacity. Thus, vibration mitigation of stay cables has been an important issue both in academia and practice. While a semi-active control scheme shows effective vibration reduction compared to a passive control scheme, real-world applications are quite limited because it requires complicated equipment, including for data acquisition, and power supply. This study aims to develop an Arduino-based integrated cable vibration control system implementing a semi-active control algorithm. The integrated control system is built on the low-cost, low-power Arduino platform, embedding a semi-active control algorithm. A MEMS accelerometer is installed in the platform to conduct a state feedback for the semi-active control. The Linear Quadratic Gaussian control is applied to estimate a cable state and obtain a control gain, and the clipped optimal algorithm is implemented to control the damping device. This study selects the magnetorheological damper as a semi-active damping device, controlled by the proposed control system. The developed integrated system is applied to a laboratory size cable with a series of experimental studies for identifying the effect of the system on cable vibration reduction. The semi-active control embedded in the integrated system is compared with free and passive mode cases and is shown to reduce the vibration of stay-cables effectively.

• Journal of Power Electronics
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• v.15 no.2
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• pp.346-355
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• 2015

On the basis of the conventional PID control algorithm, a modified adaptive PID (MA-PID) control algorithm is presented to improve the steady-state and dynamic performance of closed-loop systems. The proposed method has a straightforward structure without excessively increasing the complexity and cost. It can adaptively adjust the values of the control parameters ($K_p$, $K_i$ and $K_d$) by following a new control law. Simulation results show that the line transient response of the MA-PID is better than that of the adaptive digital PID because the differential coefficient $K_d$ is introduced to changes. In addition, experimental results based on a FPGA indicate that the MA-PID control algorithm reduces the recovery time by 62.5% in response to a 1V line transient, 50% in response to a 500mA load transient, and 23.6% in response to a steady-state deviation, when compared with the conventional PID control algorithm.

• Journal of Auto-vehicle Safety Association
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• v.11 no.2
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• pp.35-43
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• 2019

This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.