• Proceedings of the KSME Conference
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• 2000.04a
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• pp.562-565
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• 2000

For solving problems of manpower and improving working environment, Robot System have been introduced. In the past, Robot System was adopted mass production, indoor factory condition, but present Robot System is applied to many other fields. This applied field is Robot System was adopted fruit harvest, maintenance, and so on. The developed Multi-Linked Lifter is applied to eminant multi-purpose working. The purpose of this study is to develop control algorithm for this equipment composed of multi-linked manipulator.

• Journal of Power System Engineering
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• v.2 no.2
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• pp.41-46
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• 1998

This paper presents the vibration control for the two degree-of-freedom which is a reduced model of multi-layer structure. This reduced model is designed for the first and second order resonance in the low frequency domain where the disturbance such as the earth quake has the large energy. And a designed controller using the LQ type $H_{\infty}$ control theory shows the good performance for the impulse disturbance through the experimental results and the simulation results respectively.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.9
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• pp.71-81
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• 2001

This paper analyzes the effectiveness of minimizing vibration and sound transmission on/through a thin rectangular plate by both feedback control and hybrid control which combines adaptive feedforward control with a feedback loop. An experimental system identification technique using the matrix-fractional curve-fitting of the frequency response data is introduced for complex shaped structures. This identification technique reduces the model order o the MIMO(Multi-Input Multi-Output) system which simplifies the practical implementation. The adaptive feedforward control uses a Multiple filtered-x LMS(Least Mean Square) algorithm and the feedback control uses a multivariable digital LQG(Linear Quadratic Gaussian) algorithm. Experimental results show that an effective reduction of sound transmission is achieved by the hybrid control scheme when both vibration and noise measurement signals are incorporated in the controller.

• Smart Structures and Systems
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• v.22 no.1
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• pp.81-94
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• 2018

Automatic Generation Control (AGC) has functionally controlled the interchange power flow in order to suppress the dynamic oscillations of frequency and tie-line power deviations as a perturbation occurs in the interconnected multi-area power system. Furthermore, Flexible AC Transmission Systems (FACTS) can effectively assist AGC to more enhance the dynamic stability of power system. So, Static Synchronous Series Compensator (SSSC), one of the well-known FACTS devices, is here applied to accurately control and regulate the load frequency of multi-area multi-source interconnected power system. The research and efforts made in this regard have caused to introduce the Fractional Order Proportional Integral Derivative (FOPID) based SSSC, to alleviate both the most significant issues in multi-area interconnected power systems i.e., frequency and tie-line power deviations. Due to multi-objective nature of aforementioned problem, suppression of the frequency and tie-line power deviations is formularized in the form of a multi-object problem. Considering the high performance of Multi Objective Bees Algorithm (MOBA) in solution of the non-linear objectives, it has been utilized to appropriately unravel the optimization problem. To verify and validate the dynamic performance of self-defined FOPID-SSSC, it has been thoroughly evaluated in three different multi-area interconnected power systems. Meanwhile, the dynamic performance of FOPID-SSSC has been accurately compared with a conventional controller based SSSC while the power systems are affected by different Step Load Perturbations (SLPs). Eventually, the simulation results of all three power systems have transparently demonstrated the dynamic performance of FOPID-SSSC to significantly suppress the frequency and tie-line power deviations as compared to conventional controller based SSSC.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.1-13
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• 2002

We study a multi-component production/inventory system in which individual components are made to meet various demand types. We assume that the demands arrive according to a Poisson process, but there is a fixed probability that a demand requests a particular kit of different components. Each component is produced by a flow line with several stations. The production of each component is operated by the CONWIP control mechanism. To analyse this system, we propose an approximation method based on aggregation method. In application of the aggregation method, a product-form approximation technique as well as a matrix-geometric method is used. Comparisons with simulation show that the approximation method provides fairly good results.

• Proceedings of the KIEE Conference
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• 2005.10a
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• pp.191-196
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• 2005

In this paper, a multi-functional control module is developed to improve the confidence of CO2 welding. In the developed system, a main controller of an automatic welding system including of I/O port is modulated by microprocessor. The developed multi-functional control module is familiarized to the conventional automatic welding system. Therefore, the confidence of CO2 welding is improved when the main controller of an automatic welding system is broken down.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.141-146
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• 2012

This paper introduces development of multi-rotor system and vision based autonomous landing system. Multi-rotor platform is modeled by rigid body motion with Newton Euler concept. Also Multi-rotor platform is simulated and tuned by LQR control algorithm. Vision based Autonomous Landing system uses a single camera that is mounted Multi-rotor system. Augmented reality algorithm is used as marker detection algorithm and autonomous landing code is test with GCS for the precision landing.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.672-675
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• 1997

This paper proposes a structure of the control system for the step & scan exposure tool. The step & scan exposure tool is used for the manufacturing process of the semiconductor DRAM memory of giga bit. The control system employs the VME bus instead of the conventional ISA bus so that all control signals and data can be managed separately by the 4 VME-PCs for fast and fault-free flow of signals for multi-tasking. A high speed I/O card is equipped for the real-time monitoring and control of the sub module equipment. Then all the subsystems are integrated and aligned for the operation of the step & scan exposure tool with the VME bus and, I/O card.

• Journal of the Korean Society of Industry Convergence
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• v.2 no.1
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.