• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.10
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• pp.89-94
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• 2012

This research is to apply the control of neuron networks for the real-time walking control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of walking control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. This paper proposes a new mode to implement a neural network controller by installing a real object for controlling and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The proposed control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.69-72
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• 1999

In this paper, the implementation method for the extending real-time execution in MMS Implementation is proposed. For this, the method of MMS over ATM(Asynchronous Transfer Mode) and IEEE 802.12 network is analyzed. By the analysis of service response time, making the ASIC of encoding and decoding parts are proposed for one of the real time extension in MMS. The main goals of this paper to analyze and propose suitable methods to meet the real time requirements in MMS applied system.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.6
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• pp.711-721
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• 2013

Optimal operation of a combined sewer network with distributed storage facilities aims to use the whole retention capacity of all reservoirs efficiently before overflows take place somewhere in the considered network system. An efficient real-time network control (RTNC) strategy has been emerging as an attractive approach for reducing substantially the overflows from a sewer network compared to the conventional fixed or manually adjusted gate setting method, but the related concrete framework for RTC development has not been throughly introduced so far. The main goal of this study is to give a detailed description of the RTNC systems via reviewing several guidelines published abroad, and finally to suggest methods for the proper application of RTNC on distributed storage facilities. Especially, this study is focused on emphasizing the importance of hierarchical structure of RTNC system that consists of three control layers (management, global control and local control). Further, with regard to the global control layer which is responsible for the central overall network control, the wide-ranging details of two components (adaption and optimization layers) are also presented. This study can provide the valuable basis for the RTNC implementation in the particular sewer network with distributed multiple storage facilities.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.718-725
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• 2004

The real-time industrial network, often referred to as fieldbus, is an important element for intelligent manufacturing systems. Thus, in order to satisfy the real-time requirements of field devices, numerous fieldbus protocols have been announced. But, the application of fieldbus has been limited due to the high cost of hardware and the difficulty in interfacing with multi-vendor products. Therefore, as an alternative to fieldbus, the computer network technology, especially Ethernet (IEEE 802.3), is being adapted to the industrial environment. However, the crucial technical obstacle for Ethernet is its non-deterministic behavior that makes it inadequate for industrial applications where real-time data have to be delivered within a certain time limit. Recently, the development of switched Ethernet shows a very promising prospect for industrial application due to the elimination of uncertainties in the network operation resulting in much improved performance. This paper focuses on the application of the switched Ethernet with cascade structure for industrial communications. More specifically, this paper presents an analytical performance evaluation of switched Ethernet with cascade structure, and a case study about networked control system.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1348-1351
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• 1996

In field bus network, field device are connected with a medium. Because a medium must be shared for transmitting data, there are random delay time when data arrive destination station. It is difficult that all data packets are guaranteed synchronization and real-time restriction. In this paper, we show an algorithm that makes network utilization to maximum, guarantees real-time restriction, calculates sampling time at all control loop.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1840-1845
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• 2005

The SoC and digital technology development recently enabled the emergence of information devices and control devices because the SoC present many advantages such as lower power consumption, greater reliability, and lower cost. It is required to use an embedded operating system for building control systems. So far, the Real-Time operating system is widely used to implement a Real-Time system since it meets developer's requirements. However, Real-Time operating systems reveal a lack of standards, expensive development, and license costs. Embedded Linux is able to overcome these disadvantages. In this paper, the implementation of control system platform using Real-Time Embedded Linux is described. As a control system platform, we use XScale of a Soc and build Real-Time control platform using RTAI and Real-Time device driver. Finally, we address the feasibility study of the Real-Time Embedded Linux as a Real-Time operating system for mobile robots.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.253-257
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• 2004

Neural networks are works are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division(corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.186-193
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• 2008

The Internet's low cost and ubiquity present an attractive option for real-time distributed control of processes on the factory floor. When integrated with the Internet, the LonWorks open control network can give ubiquitous accessibility with the distributed control nature of information on the factory floor. One of the most important points in real-time distributed control of processes is timely response. There are many processes on the factory floor that require timely response. However, the uncertain time delay inherent in the network makes it difficult to guarantee timely response in many cases. Especially, the transmission characteristics of the LonWorks/IP network show a highly stochastic nature. Therefore, the time delay problem has to be resolved to achieve high performance and quality of the real-time distributed control of the process in the LonWorks/IP Virtual Device Network (VDN). It should be properly predicted and compensated. In this paper, a new distributed control scheme that can compensate for the effects of the time delay in the network is proposed. It is based on the PID controller augmented with the Smith predictor and disturbance observer. Designing methods for output feedback filter and disturbance observer are also proposed. Tracking position control experiment of a geared DC Servo motor is performed using the proposed control method. The performance of the proposed controller is compared with that of the Internal Model Controller (IMC) with the Smith predictor. The result shows that the performance is improved and guaranteed by augmenting a PID controller with both the Smith predictor and disturbance observer under the stochastic time delay in the LonWorks/IP VDN.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

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. 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. The TMS32OC31 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.