Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot

Fusaomi Nagata, Keigo Watanabe

Research output: Contribution to journalArticle

9 Citations (Scopus)


Recently, various control methods represented by proportional-integral-derivative (PID) control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.

Original languageEnglish
Pages (from-to)54-61
Number of pages8
JournalInternational Journal of Automation and Computing
Issue number1
Publication statusPublished - Feb 2011



  • adaptive learning
  • large-scale teaching signal
  • Neural networks
  • nonlinear control
  • PUMA560 manipulator
  • servo system
  • sigmoid function
  • trajectory following control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Applied Mathematics
  • Modelling and Simulation

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