• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.10 s.181
• /
• pp.2451-2460
• /
• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1994.10a
• /
• pp.748-754
• /
• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.54 no.1
• /
• pp.40-47
• /
• 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.

• Transactions of The Korea Fluid Power Systems Society
• /
• v.4 no.2
• /
• pp.1-6
• /
• 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.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.12 no.1
• /
• pp.163-172
• /
• 1988

An off-line Geometric Adaptive Control Scheme is applied to the milling machine to identify its guideway errors. In the milling process, the workpiece fixed on the bed travels along the guideway while the tool and spindle system is fixed onto the machine. The scheme is based on the exponential smoothing of post-process measurements of relative machining errors due to the tool, workpiece and bed deflections. The guideway error identification system consists of a gap sensor, a, not necessarily accurate, straightedge, and the numerical control unit. Without a priori knowledge of the variations of the cutting parameters, the time-varying parameters are also estimated by an exponentially weighted recursive least squares method. Experimental results show that the guideway error is well identified within the range of RMS values of geometric error changes between machining passes disregarding the machining conditions.

• International Journal of CAD/CAM
• /
• v.6 no.1
• /
• pp.139-148
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.18-18
• /
• 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

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.5 no.3
• /
• pp.22-30
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.1-6
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.18-21
• /
• 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.