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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 28 (2000), S. 231-258 
    ISSN: 1573-0409
    Keywords: kinematically redundant manipulators ; torque-optimality ; singularity-robustness ; dynamic control equation ; weighted generalized inverses ; Jacobian-inertia product ; damped least-squares inverses ; generalized dynamic manipulability measure
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A new control method for kinematically redundant manipulators having the properties of torque-optimality and singularity-robustness is developed. A dynamic control equation, an equation of joint torques that should be satisfied to get the desired dynamic behavior of the end-effector, is formulated using the feedback linearization theory. The optimal control law is determined by locally optimizing an appropriate norm of joint torques using the weighted generalized inverses of the manipulator Jacobian-inertia product. In addition, the optimal control law is augmented with fictitious joint damping forces to stabilize the uncontrolled dynamics acting in the null-space of the Jacobian-inertia product. This paper also presents a new method for the robust handling of robot kinematic singularities in the context of joint torque optimization. Control of the end-effector motions in the neighborhood of a singular configuration is based on the use of the damped least-squares inverse of the Jacobian-inertia product. A damping factor as a function of the generalized dynamic manipulability measure is introduced to reduce the end-effector acceleration error caused by the damping. The proposed control method is applied to the numerical model of SNU-ERC 3-DOF planar direct-drive manipulator.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Neural computing & applications 1 (1993), S. 215-223 
    ISSN: 1433-3058
    Keywords: Expert system ; Knowledge-based system ; Neural networks ; Machine learning ; Induction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Neural network technology is experiencing rapid growth and is receiving considerable attention from almost every field of science and engineering. The attraction is due to the successful application of neural network techniques to several real world problems. Neural networks have not yet found widespread application in weather forecasting. The reason for this has been the difficulty in obtaining suitable weather forecasting data sets. In this paper we describe our experience in applying neural network techniques for acquiring the necessary knowledge to predict the weather conditions of Melbourne City and its suburbs in Australia during a 24 hour period beginning at 9 am local time. The accuracy of forecasts produced by a given forecasting procedure typically varies with factors such as geographical location, season, categories of weather, quality of input data, lead time and validity time. Two types of weather data sets assembled from the archives of the Australian Commonwealth Bureau of Meteorology are used for training the neural network. The results of the experiments are competitive and are discussed.
    Type of Medium: Electronic Resource
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