• 제목/요약/키워드: Geometric Adaptive Control

검색결과 44건 처리시간 0.036초

축교정을 위한 기하학적 진직도 적응제어기 설계 (Design of a Geometric Adaptive Straightness Controller for Shaft Straightening Process)

  • 김승철;정성종
    • 대한기계학회논문집A
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    • 제24권10호
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    • pp.2451-2460
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    • 2000
  • In order to minimize straightness error of deflected shaft, a geometric adaptive straightness controller system is studied. A multi-step straightening and a three-point bending process have been developed for the geometric adaptive straightness controller. Load-deflection relationship, on-line identification of variations of material properties, on-line springback prediction, and real-time hydraulic control methodology are studied for the three-point bending process. By deflection pattern analysis and fuzzy self-learning method in the multi-step straightening process, a straightening point and direction, desired permanent deflection and supporting condition are determined. An automatic straightening machine has been fabricated for rack bars by using the developed ideas. Validity of the proposed system is verified through experiments.

머시닝센터에서 고정밀 가공을 위한 NC 기술 (NC Technology for High-Precision Machining in Machining Centers)

  • 정성종
    • 한국정밀공학회:학술대회논문집
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    • 한국정밀공학회 1994년도 추계학술대회 논문집
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    • pp.748-754
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    • 1994
  • This paper deals with a geometric error simulator, measurement and inspection of workpiece errors on the machine tools, and identification and compensation methodology of thermal errors in machining centers. In order to raise the machining accuracy of workpieces a measurement and inspection system on the machine tool is developed. By using MPPGT module Manual and CNC type CMMs are realized on the machining centers. To compensate for geometric and thermal deformation errors of machining centers, a real time and an off line geometric adaptive control system were developed on the machining centers. A vertical and a horizontal machining center equipped with FANUC 0MC were used for experiments. Performance of the systems were confirmed with a large amount of experiment.

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FXLMS 알고리즘 수렴성의 기하학적 해석 (Geometric Analysis of Convergence of FXLMS Algorithm)

  • 강민식
    • 대한전기학회논문지:시스템및제어부문D
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    • 제54권1호
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    • pp.40-47
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    • 2005
  • This paper concerns on Filtered-x least mean square (FXLMS) algorithm for adaptive estimation of feedforward control parameters. The conditions for convergence in ensemble mean of the FXLMS algorithm are derived and the directional convergence properties are discussed from a new geometric vector analysis. The convergence and its directionality are verified along with some computer simulations.

굴삭기 작업장치부의 기하학적 동역학 모델링 및 궤적 제어에 관한 연구 (Geometric Modeling and Trajectory Control Design for an Excavator Mechanism)

  • 김성호;유승진;이교일
    • 유공압시스템학회논문집
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    • 제4권2호
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    • pp.1-6
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    • 2007
  • During the last few decades, excavation automation has been investigated to protect the operator from the hazardous working environment and to relieve the cost of the skilled operator. Therefore, a number of modelling and controller design methods of the hydraulic excavator are proposed in many literatures to realize the excavation automation. In this article, a geometric approach far the multi-body system modeling is adopted to develop the excavator mechanism model that contains 4 kinematic loops and 12 links. Considering a simple soil mechanism model with a number of uncertain soil parameters, an adaptive trajectory tracking control strategy based on the developed excavator model is proposed. The improved performance of the designed controller over the simple PID controller is validated via the simulation study.

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기하학적 적응제어에 의한 엔드밀링머시인의 안내면 오차 규명 (Identification of guideway errors in the end milling machine using geometric adaptive control algorithm)

  • 정성종;이종원
    • 대한기계학회논문집
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    • 제12권1호
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    • pp.163-172
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    • 1988
  • 본 논문에서는 GAC방법을 이용하여 공작기계의 안내면오차를 수치제어 공작기계가 가지고 있는 가공조건의 조절 능력을 이용하여 가공오차를 보상제어 함으로써 규명(identification)할 수 있는 방법을 제시한다.

Inscribed Approximation based Adaptive Tessellation of Catmull-Clark Subdivision Surfaces

  • Lai, Shuhua;Cheng, Fuhua(Frank)
    • International Journal of CAD/CAM
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    • 제6권1호
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    • pp.139-148
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    • 2006
  • Catmull-Clark subdivision scheme provides a powerful method for building smooth and complex surfaces. But the number of faces in the uniformly refined meshes increases exponentially with respect to subdivision depth. Adaptive tessellation reduces the number of faces needed to yield a smooth approximation to the limit surface and, consequently, makes the rendering process more efficient. In this paper, we present a new adaptive tessellation method for general Catmull-Clark subdivision surfaces. Different from previous control mesh refinement based approaches, which generate approximate meshes that usually do not interpolate the limit surface, the new method is based on direct evaluation of the limit surface to generate an inscribed polyhedron of the limit surface. With explicit evaluation of general Catmull-Clark subdivision surfaces becoming available, the new adaptive tessellation method can precisely measure error for every point of the limit surface. Hence, it has complete control of the accuracy of the tessellation result. Cracks are avoided by using a recursive color marking process to ensure that adjacent patches or subpatches use the same limit surface points in the construction of the shared boundary. The new method performs limit surface evaluation only at points that are needed for the final rendering process. Therefore it is very fast and memory efficient. The new method is presented for the general Catmull-Clark subdivision scheme. But it can be used for any subdivision scheme that has an explicit evaluation method for its limit surface.

ADAPTIVE CONTROL USING NEURAL NETWORK FOR MINIMUM-PHASE STOCHASTIC NONLINEAR SYSTEM

  • Seok, Jinwuk
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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    • pp.18-18
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    • 2000
  • In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network are provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shows that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller

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스프링백 관측기를 이용한 축교정기 개발 (Development of Shaft Straightening Machine with Springback Observer)

  • 안중용
    • 한국생산제조학회지
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    • 제5권3호
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    • pp.22-30
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    • 1996
  • In order to compensate for out-of-straightness of shafts, an automatic straightening process composed of an automatic measuring module, an automatic control unit and operating softwares was developed with a hydraulic press. The out-of-sraightness of each shaft was measured automatically in the measuring stage. An optimal pressure point was determined to minimize TIR value of the shaft according to press count of 3-points bending process. In the geometric adaptive control procedure, punch stroke and springback of the shaft were predicted by an observer using on-line measured values of press force and deflection amount I each press count. An automatic straightening machine was realized with the measuring module, the GAC module, PLD, IBM-PC and the operating software on the hydraulic press. the validity of the proposed straightening process was confirmed through a series of experiments with cam shafts.

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Orthogonalization principle for hybrid control of robot arms under geometric constraint

  • Arimoto, Suguru
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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    • pp.1-6
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    • 1992
  • A principle of "orthogonalization" is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric endpoint constraints. The principle realizes the hybrid control in a strict sense by letting position and velocity feedback signals be orthogonal in joint space to the contact force vector whose components are exerted at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a gradient of the equation of the surface in joint coordinates and hence both projected position and velocity feedback signals become perpendicular to the force vector that is normal to the surface at the contact point in joint space. To show the important role of the principle in control of robot manipulators, three basic problems are analyzed, the first is a hybrid trajectory tracking problem by means of a "modified hybrid computed torque method", the second is a model-based adaptive control problem for robot manipulators under geometric endpoint constraints, and the third is an iterative learning control problem. It is shown that the passivity of residual error dynamics of robots follows from the orthogonalization principle and it plays a crucial role in convergence properties of both positional and force error signals.force error signals.

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최소위상 확률 비선형 시스템을 위한 필터링 조건과 신경회로망을 사용한 적응제어 (A FILTERING CONDITION AND STOCHASTIC ADAPTIVE CONTROL USING NEURAL NETWORK FOR MINIMUM-PHASE STOCHASTIC NONLINEAR SYSTEM)

  • 석진욱
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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    • pp.18-21
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    • 2001
  • In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

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