• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.139-149
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• 2015

In this paper, we describe a new approach to perform real-time implementation of an robust controller for robotic manipulator based on digital signal processors in this paper. The Texas Instruments DSPs chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved direct Lyapunov method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for robot manipulator consisting of dual arm with eight degrees of freedom at the joint space and cartesian space.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.494_495
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• 2009

This paper presents a study of DFIG wind power generation system for real-time simulation. For real-time simulation, the real-time digital simulator (RTDS) and its user friendly interface simulation software (RSCAD) are used. 2.2MW grid-connected variable speed DFIG wind power generation system is modeled and analyzed in this study. Stator-flux oriented vector control scheme is applied to stator, rotor side converter control, and back-to-back PWM converters are implemented for the decoupled control. The real-wind speed signal extracted by an anemometer is used for realistic and accurate simulation analysis. Block diagrams for DFIG and control scheme of stator, rotor-side are introduced. Real-time simulation cases are carried out and analyzed for the validity of this work.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.98-115
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• 2012

New trend called ubiquitous leads the recent business by standardization and integration. It should be the main issue how to guarantee the integration and accountability on each business, especially in mission critical system which is mainly supported by M2M (Machine to Machine) control mechanism. This study is from the analysis of digital forensics case study that is from the M2M Sensing Control Mechanism problem of the "Imjin River" case in 2009, where a group of family is swept away to death by water due to M2M control error. The ubiquitous surroundings bring the changes in the field of criminal investigation to real time controls such as M2M systems. The needs of digital forensics on M2M control are increasing on every crime scene but we suffer from the lack of control metrics to get this done efficiently. The court asks for more accurately analyzed results accounting high quality product development design. Investigators in the crime scene need real-time analysis against the crime caused by poor quality of mission critical systems. It seems to be every need of Real-Time-Enterprise, so called ubiquitous society on the case. We try to find the efficiency and productivity in discovering non-functional design defects in M2M convergence products focusing on three metrics in study model with quick implementation. Digital forensics system in present status depends on know-how of each investigator and is hard to expect professional analysis on every field. This study set up a hypothesis "Co-working of professional investigators on each field will qualify Performance and Integrity" especially in mission critical system such as M2M and suggests "Online co-work analysis model" to efficiently detect and prevent mission critical errors in advance. At the conclusion, this study proved the statistical research that was surveyed by digital forensics specialists around M2M crime scene cases with quick implementation of dash board.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.4
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• pp.543-552
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• 1996

The goal of this paper is to developed a DSP based power analyzing and control system by 3-Dimensional (3-D) space current co-ordinates. A developed system is made up of 486-PC and DSP (Digital Signal Processor) board, Active Power Filter, Non-linear thyristor load, and Power analyzing and control program for Windows. Power is analyzed using signal processing techniques based on the correlation between voltage and current waveforms. Since power analysis algorithm is performed by DSP, power analysis is achieved in real-time even under highly dynamic nonlinear loading conditions. Combining control algorithm with power analysis algorithm is performed by DSP, power analysis is achieved in real-time even under highly dynamic nonlinear loading conditions. Combining control algorithm with power analysis algorithm, flexibility of the proposed system which has both power analysis mode and control mode, is greatly enhanced. Non-active power generated while speed of induction motor is controlled by modulating firing angle of thyristor converter, is compensated by Active Power Filter for verifying a developed system. Power analysis results, before/after compensation, are numerically obtained and evaluated. From these results, various graphic screens for time/frequency/3-D current co-ordinate system are displayed on PC. By real-time analysis of power using a developed system, power quality is evaluated, and compared with that of conventional current co-ordinate system. (author). refs., figs. tabs.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.76-91
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• 1996

In this paper, we proposed the new medium access control protocol for the virtual token bus netowrk. The network is applied to inter-processor communication network of large capacity digital switching system and digital mobile system with distributed control architecture. in the virtual token bus netowrk, the existing medium access control protocols hav ea switchove rtime overhead when traffic load is light or asymmetric according ot arbitration address of node that has message to send. The proposed protocol optimized average message delay using cyclic bus access chain to exclude switchover time of node that do not have message to send. Therefore it enhanced bus tuilization and average message delay that degrades the performance of real time communication netowrks. It showed that the proposed protocol is more enhacned than virtual token medium access control protocol and virtual token medium access control protocol iwth reservation through performance analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.100-104
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• 1998

This paper describes the real-time implementation of an adaptive controller fur the robotic manipulator. Digital signal processors(DSPs) are special purpose micro-processors that are particularly powerful for intensive numerical computations involving sums and products of variables. TMS320C50 chips are used in implementing real time adaptive control algorithms to provide an enhanced motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved Lyapunov second stability analysis based on the direct adaptive control theory. The adaptive controller consists of an adaptive feedforward controller and feedback controller. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a assembling robot.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.181-190
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• 2002

IEEE1394 is a de facto standard for the home network interfaces of digital multimedia home devices including digital A/V systems, digital camcorders, and PCs. Recently, it has been used in applications to guarantee the real-time characteristics such as home automation system and IICP (Instrument and Industrial Control Protocol). In order to guarantee real-time requirements in these IEEE1394-based real-time applications, this thesis proposes the software architecture of an IEEE1394 based home network that supports the guarantee for service's react-time characteristics. The proposed architecture has a real-time IEEE1394 device driver and event service architecture for guarantee real-time characteristics. The real-time device driver supports priority-based queueing of packets and mechanism to reduce the interrupt latency time in ISR. The event service architecture supports a real-time events delivery based on home network service using real-time event channel. This architecture can accommodate the real-time requirements of various applications and services such as digital multimedia services with QoS guarantees. home automation system required real-tine characteristics.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.759-763
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.