160 research outputs found

    Foreword

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    Time and frequency domain convergence properties of causal and Current Iteration Tracking Error (CITE) discrete time Iterative Learning Control (ILC) algorithms are discussed. Considering necessary and sufcient convergence conditions basic matrix properties can be utilized to show that causal as well as CITE ILC algorithms converge to zero error in only very restrictive special cases. The frequency domain convergence conditions, sucient for monotone convergence, are studied using a discrete-time version of Bode's integral theorem. The result is that causal and CITE ILC algorithms will not satisfy the frequency domain conditions except if the system has relative degree zero or it is accepted that the algorithms do not converge to zero error

    Some Issues Raised By Alaska’s Recording Act

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    A novel method to find the orientation and position of a triaxial accelerometer mounted on a six degrees-of-freedom industrial robot is proposed and evaluated on experimental data. The method consists of two consecutive steps, where the first is to estimate the orientation of the accelerometer from static experiments. In the second step the accelerometer position relative to the robot base is identified using accelerometer readings when the accelerometer moves in a circular path and where the accelerometer orientation is kept constant in a path fixed coordinate system. Once the accelerometer position and orientation are identified it is possible to use the accelerometer in robot model parameter identification and in advanced control solutions. Compared to previous methods, the accelerometer position estimation is completely new, whereas the orientation is found using an analytical solution to the optimisation problem. Previous methods use a parameterisation where the optimisation uses an iterative solver.LINK-SI

    The Supreme Court As National School Board

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    A modern industrial robot control system is often only based upon measurements from the motors of the manipulator. To perform good tra-ectory tracking on the arm side of the robot a very accurate description of the system must therefore be used. In the paper a sensor fusion technique is presented to achieve good estimates of the position of the robotusing a very simple model. By using information from an accelerometer at the tool of the robot the effect of unmodelled dynamics can be measured. The estimate of the tool position can be improved to enhance accuracy. We formulate the computation of the position as a Bayesian estimation problem and propose two solutions. The first solution uses the extended Kalman fillter (EKF) as a fast but linearized estimator. The second uses the particle fillter which can solve the Bayesian estimation problem without linearizations or any Gaussian noise assumptions. Since the aim is to use the positions estimates to improve position with an iterative learning control method, no computational constraints arise. The methods are applied to experimental data from an ABB IRB1400 commercial industrialrobot and to data from a simulation of a realistic flexible robot model, showing a significant improvement in position accuracy

    Theory of the Arbitration Process

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    A sensor fusion method for state estimation of a flexible industrial robot is developed. By measuring the acceleration at the end-effector, the accuracy of the arm angular position, as well as the estimated position of the end-effector are improved. The problem is formulated in a Bayesian estimation framework and two solutions are proposed; the extended Kalman filter and the particle filter. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The technique is also verified in experiments on an ABB robot, where the dynamic performance of the position for the end-effector is significantly improved.Vinnova Excellence Center LINK-SICSSF project Collaborative Localizatio

    Foreword

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    This users manual introduces the basic ideas of the PGT - Path Generation Toolbox for Matlab. The main features of the toolbox is to givethe user the possibility to 1) build paths in Cartesian (3DOF) space, 2) transform the Cartesian paths into jointspace. All the most important functions are explained and examples of how to use them are also included

    A Few Questions About the Social-Obligation Norm

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    Reponse to an article by Gregory S. Alexander, \u27The Social-obligation Norm in American Property Law,\u27 in a Special Issue of the Journal on Property Obligation

    L'esprit des lois ?:L'étude des modes de scrutin un demi-siècle après Les partis politiques de Maurice Duverger

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    Peu d’oeuvres peuvent aujourd’hui prétendre à une postérité aussi marquante que l’analyse faite par Maurice Duverger de l’effet des modes de scrutin. Cette analyse aura non seulement marqué son temps en structurant les débats, mais peu d’auteurs, et notamment d’auteurs français, voient encore aujourd’hui leur nom aussi souvent cité. Cette postérité est assurément liée à la formulation des conclusions sous forme de loi, les fameuses trois lois suivant lesquelles les systèmes partisans tendent (le déterminisme reproché à M. Duverger doit de ce point de vue être relativisé) à être bipartisan, multipartite ou multipartite, mais avec coalitions électorales stables, suivant que le mode de scrutin est majoritaire à un tour, proportionnel ou bien majoritaire à deux tours. Mais ces lois sont également soutenues par une théorie explicative du rôle structurant des modes de scrutin par la distinction d’effets mécaniques (ceux liés à la transformation des voix en sièges par l’arithmétique de la loi électorale) d’effets psychologiques, résultant de la sophistication des choix d’électeurs anticipant les effets mécaniques du mode de scrutin. [Premier paragraphe

    Application of the Vagueness Doctrine to Statutes Terminating Parental Rights

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    The norm-optimal iterative learning control (ilc) algorithm for linear systems is extended to an estimation-based norm-optimal ilc  algorithm where the controlled variables are not directly available as measurements. A separation lemma is presented, stating that if a stationary Kalman filter is used for linear time-invariant systems then the ilc  design is independent of the dynamics in the Kalman filter. Furthermore, the objective function in the optimisation problem is modified to incorporate the full probability density function of the error. Utilising the Kullback–Leibler divergence leads to an automatic and intuitive way of tuning the ilc  algorithm. Finally, the concept is extended to non-linear state space models using linearisation techniques, where it is assumed that the full state vector is estimated and used in the ilc  algorithm. Stability and convergence properties for the proposed scheme are also derived.Vinnova Excellence Center LINK-SICExcellence Center at Linköping-Lund in Information Technology, ELLIITSSF project Collaborative Localizatio
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