• 제어로봇시스템학회논문지
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• 제19권5호
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• pp.481-487
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• 2013

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 1996년도 추계학술발표회 발표논문집
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.237-242
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• 2012

AFC (Adaptive Feedforward Control) is significantly employed for improving control performance of dynamic systems particularly involving periodic disturbance signals in engineering fields. This paper presents a novel hybrid AFC approach for discrete-time systems with multiple disturbances in terms of control input and state variables. The proposed AFC mechanism is hierarchically composed of a conventional feedforward control framework and PID auxiliary control configuration in parallel. The former is generic to decrease periodic disturbance excited to control actuators and the latter is additionally constructed to overcome control deterioration due to time-varying uncertainty under given systems. We carry out numerical simulation to test reliability of our proposed hybrid AFC system and compare its control performance to a well-known conventional AFC method with respect to time and frequency domains for proving of its superiority.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2001년도 The Seoul International Simulation Conference
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• pp.269-273
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• 2001

Semiconductor wafer fabrication is a business of high capital investment and fast changing nature. To be competitive, the production in a fab needs to be effectively planned and scheduled starting from the ramping up phase, so that the business goals such as on-time delivery, high output volume and effective use of capital intensive equipment can be achieved. In this paper, we propose Stand Alone layout and In-Line layout are analyzed and compared while varying number of device variable changes. The comparison is performed through simulation using ProSys; a window 98 based discrete system simulation software, as a tool for comparing performance of two proposed layouts. The comparison demonstrates that when the number of device variable change is small, In-Line layout is more efficient in terms of production quantity. However, as the number of device variable change is more than 14 titles, Stand Alone layout prevails over In-Line layout.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.58-62
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• 1988

Recently many adaptive control schemes for the industrial robot manipulator have been developed. Especially, learning control utilizing the repetitive motion of robot and based on iterative signal synthesis attracts much interests. However, since most of these approaches excludes the boundness of the input torque supplied to the manipulator, its effectiveness may be limited and also the full dynamic capacity of the robot manipulator can not be utilized. To overcome the above-mentioned difficulties and meet the desired performance, we propose an approach which yields the effective learning control schemes in this paper. In this study, some stability conditions derived from applying the Lyapunov theory to the discrete linear time-varying dynamic system are established and also an optimization scheme considering the bounded input torque is introduced. These results are simulated on a digital computer using a three-joint revolute manipulator to show their effectiveness.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 1996년도 추계 학술 발표회 발표논문집
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• pp.217-227
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• 1996

The new filed of learning control develops controllers that learn to improve their performance at executing a given task , based on experience performing this specific task. In a previous work[6], authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controller indecentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an asssembly line. This paper starts with decentralized discrete time systems. and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The resultof the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample tie in the digital learning controller is sufficiently short.

• 대한조선학회논문집
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• 제45권3호
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• pp.269-279
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• 2008

In this paper, an underwater navigation system with adaptive receding horizon Kalman filter (ARHKF) is studied. It is well known that incorrect statistical information and temporal disturbance invoke errors of any navigation systems with Kalman filter, which makes the autonomous navigation difficult in real underwater environment. In this context, two kinds of problems are herein considered. The first one is the development of an algorithm, which estimates the noise covariance of a linear discrete time-varying stochastic system. The second one is the implementation of ARHKF to underwater navigation systems. The performance of the derived estimation algorithm of noise covariance and the ARHKF are verified by simulation and experiment in the towing tank of Seoul National University.

• 한국지반공학회지:지반
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• 제9권3호
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• pp.67-76
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• 1993

우리나라는 많은 산막지역으로이루어져 있으며 우기에 많은산사태의 발생으로 인하여 인명과 재산의 손실을 입고 있다. 따라서, 산사태의 발생에 대한 예측 시스템과 위험도 분석 연구가 필요하며, 본 연구의 목적은 관측된 지하수위의 분석을 통하여 산사태 발생을 예측하는 가능성에 대한 것이다. 이를 위하여 AR 모델을 사용하여 모델계수를 일정하게 하는 경우와 변화시키는 경우로 나누어 분석하였다. AR모델계수를 일정하게 하는 경우에는 AR(1), AR(2), AR(3) 모델을 선택하여 각 각의 모델계수를 구하였고, AR모델계수를 변화시키는 경우에는 변형된 AR(1)과 전형적인 AR (2) 모델을 과정 모델로 이용하여 Kalman Filtering 기법에 의하여 모델계수를 구하였다. 그 결과, 모델계수를 변화시키는 실시간 예측 방법이나 AR모델계수가 일정한 경우 모두 산사면 에서의 지하수위를 잘 예측해주며, 지하수위 뿐만아니라 시간별 강우강도를 고려함으로써 더욱 정 확한 예측을 할 수 있을 것으로 사료된다.

• Journal of Power Electronics
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• 제5권3호
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• pp.173-179
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• 2005

In this paper, a very low cost and robust sensing method for the rotor position of a TSRM(Toroidal Switched Reluctance Motors) is described. Position information of the rotor is essential for SRM drives. The rotor position sensor such as an opto-interrupter or high performance encoder is generally used for the estimation of rotor position. However, these discrete position sensors not only add complexity and cost to the system but also tend to reduce the reliability of the drive system. In order to solve these problems, in the proposed method, rotor position detection is achieved using voltage waveforms induced by the time varying flux linkage in the search coils, and then the appropriate phases are excited to drive the SRM. But the search coil's EMF is generated only when the motor rotates. Therefore the rotor position sensing method using squared Euclidean distance at a standstill is also examined. The simulation and experimental results are presented to verify the performance of the proposed method in this paper.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2006년도 춘계 국제학술대회 논문집
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• pp.211-217
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• 2006

반복학습제어는 특정목적 궤도의 반복작업을 수행하는 정밀도를 개선하는 제어기를 개발하는 기술이다. 기존 연구에서는 수직다물체의 반복정밀도를 개선하기 위하여 누적학습제어와 적응제어 기법을 한 반복영역에서 동시에 실시하는 기법을 개발하였다. 당초 이 기술은 생산조립라인의 산업용 로봇에서 발생하는 반복정밀도를 개선하기 위해 개발하였으며, 특히, 분산학습기법은 산업용 로봇에서 발생하는 실질적 제어 방식에 유효한 기법이다. 본 논문에서 개발한 제어기술은 한 반복영역의 모든 시간대의 입출력 정보를 동시에 학습하기 보다는 매 시간대의 입출력 정보를 각 시간대 마다 충분히 학습하고 다음 시간대의 정보를 학습하는 것이다. 본 논문에서 개발한 기술을 산업용 로봇과 의료기기에 적용하면 수직다물체의 정밀도 품질보증 확보에 큰 기여를 하게 된다.