Design of adaptive robot control system using recurrent neural network

Yildirim S.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, cilt.44, sa.3, ss.247-261, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44 Sayı: 3
  • Basım Tarihi: 2005
  • Doi Numarası: 10.1007/s10846-005-9012-6
  • Dergi Adı: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.247-261
  • Erciyes Üniversitesi Adresli: Evet

Özet

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.