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Romansy 14 pp 173-180 | Cite as

Advanced Control of Robot Compliance Tasks Using Hybrid Intelligent Paradigms

  • Duško Katić
  • Miomir Vukobratović
Part of the International Centre for Mechanical Sciences book series (CISM, volume 438)

Abstract

In this paper, a new comprehensive intelligent control strategy based on connectionist learning is presented, effectively combining genetic algorithms(GA) with neural and wavelet classification. The proposed neural network classifies characteristics of environment, determines the control parameters for compliance control, and in coordination with basic learning compliance control algorithm, reduces the influence of robot dynamic model uncertainties. The effectiveness of the approach is shown by using a simple and efficient GA optimization procedures to tune and optimize the performance of a neural classifier and controller. Some compliant motion simulation experiments with robotic arm placed in contact with dynamic environment have been performed.

Keywords

Multilayer Perceptron Wavelet Neural Network Wavelet Network Neural Classifier Connectionist Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Katié, D., and Vukobratovic, M. (1998). A neural network-based classification of environment dynamic models for compliant control of manipulation robots. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 28 (1): 58–69.Google Scholar
  2. Vukobratovic, M., and Ekalo, Y. (1996). New approach to the control of manipulation robots interacting with dynamic environment. Robotica 14: 31–39.CrossRefGoogle Scholar
  3. Vukobratovic, M., and Katié, D. (1994). Robot control structures for high-quality learning in flexible manufacturing tasks. In V.L.Plantamura, B.Soucek, and G.Visaggio., eds., Frontier Decision Support Concepts: Help Desk, Learning, Fuzzy Diagnoses, Quality Evaluation, Prediction, Evolution. New York: John Wiley & Sons,Inc,. 319–355.Google Scholar

Copyright information

© Springer-Verlag Wien 2002

Authors and Affiliations

  • Duško Katić
    • 1
  • Miomir Vukobratović
    • 1
  1. 1.Robotics LaboratoryMihailo Pupin InstituteBelgradeYugoslavia

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