• International Journal of Precision Engineering and Manufacturing
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• 제3권3호
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• pp.12-20
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• 2002

As numerous sensors and actuators are used in many automated systems, various industrial networks are adopted for real-time distributed control. In order to take advantages of the networking, however, the network implementation should be carefully designed to satisfy real-time requirements considering network induced delays. This paper presents an implementation scheme of a networked control system via Profibus-DP network fur real-time distributed control. More specifically, the effect of the network induced delay on the control performance is evaluated on a Profibus-DP testbed. Also, two conventional PID gain tuning methods are slightly modified fur fouling controllers fur the networked control system. With appropriate choices for gains, it is shown that the networked control system can perform almost as well as the traditional control system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.134-134
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• 2000

A switching control using multiple gains in the fuzzy rule is newly proposed for an abruptly changing hydraulic servo system. The proposed scheme employs fuzzy PID control, where modified input parameters are used, and LVQNN(Learning Vector Quantization Neural Network) as a switching controller (supervisor). Simulation and experimental studies have been carried out to validate and illustrate the proposed controller.

• 음성과학
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• 제11권4호
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• pp.53-66
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• 2004

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech. we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results [6] with slightly increased complexity.

• 제어로봇시스템학회논문지
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• 제7권11호
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• pp.918-926
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• 2001

In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

• 제어로봇시스템학회논문지
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• 제11권11호
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• pp.930-935
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• 2005

In this paper, position tracking control of an autonomous helicopter is presented. Combining an LQR method and a proportional control forms a simple PD control. Since LQR control gains are set for the velocity control of the helicopter, a position tracking error occurs. To minimize a position tracking error, neural network is introduced. Specially, in the frame of the reference compensation technique for teaming neural network compensator, a position tracking error of an autonomous helicopter can be compensated by neural network installed in the remotely located ground station. Considering time delay between an auto-helicopter and the ground station, simulation studies have been conducted. Simulation results show that the LQR with neural network performs better than that of LQR itself.

• 한국통신학회논문지
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• 제21권11호
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• pp.2836-2849
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• 1996

Over ATM network, variable bit rate(or VBR) traffic is allowed. Control of VBR traffic is allowed. Control of VBR traffic becomes difficult if it is bursty. VBR video traffic becomes so much bursty during intra frame period that much cell loss would occur when satistical multiplexed in ATM swich. To aviod cellloss, extra communication resources should be allocated, which reduces the capability of an ATM channel. In this paper, we propose two methods which enable a channel limited in resources to serve more VBR MPEG video bit streams. Firstly, we could redue the bitrate fluction of a statiscally multiplexed bundle of VBR video bit streams by reducing the number of intra frames overlapped at the same frame period. This method can be used in ATM switch which controls multiple video sources. Secondary, in two layer enoding, statistical multiplexing gains can be icreased by letting peak bit rate durations of both layers not be overlapped. This results in more smooth traffic. The performance of proposed methods are demonstrated by a proposed calculation method of statistical multiplexing gains(or SMGs.) The proposed SMG is based on both delay and cell loss QoS requirements at the same time.

• 융합신호처리학회논문지
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• 제3권1호
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• pp.74-79
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• 2002

많은 제어기법들이 이산시간영역제어계에서 제어성능을 개선하기 위해서 제안되고 있다. 이 제어기법들을 이용한 제어계에서 계의 응답특성은 제어기의 이득에 관계한다. 특히 외란이나 부하변동에 의해서 계의 응답이 변할 때 제어기의 이득을 재조정할 필요가 있다. 본 논문에서는 다층 신경망으로 I-PD제어기와 전치보상기를 설계하였다. I-PD제어기와 전치보상기의 이득이 자동적으로 역전파 알고리즘에 의해서 조정되도록 하였다. 제어계의 응답이 어떤 조건에 의해서 변할 때 I-PD제어기와 전치보상기의 이득들이 역전파 알고리즘에 의해서 자동적으로 조정되게 하였다. 이 I-PD제어기법을 직류 서보 전동기를 구동원으로 하는 위치제어계에 적용하여 제어기의 제어성능을 실험 결과로 타당성을 확인하였다.

• 제어로봇시스템학회논문지
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• 제5권1호
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2000년도 전력전자학술대회 논문집
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• pp.620-623
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• 2000

A very simple control approach using neural network for the robust position control of a Permanent Magnet Synchronous Motor(PMSM) is presented The linear quadratic controller plus feedforward neural network is employed to obtain the robust PMSM system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a fedforward recall and error back-propagation training. Since the total number of nodes are only eight this system can be easily realized by the general microprocessor. During the normal operation the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. IN addition the robustness is also obtained without affecting overall system response. This method is realized by a floating-point Digital Singal Processor DS1102 Board (TMS320C31) The basic DSP software is used to write C program which is compiled by using ANSI-C style function prototypes.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.