Loading...

Adaptive multi-model controller for robotic manipulators based on CMAC neural networks

Sadati, N ; Sharif University of Technology | 2005

282 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICIT.2005.1600784
  3. Publisher: 2005
  4. Abstract:
  5. In this paper, an adaptive multi-model controller based on CMAC neural networks (AMNNC) is developed for uncertain nonlinear MIMO systems. AMNNC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple neural networks. The weighted sum of the multiple neural networks is used to approximate the system nonlinearity for a given task. The proposed control scheme is applied to control a robotic manipulator, where some varying tasks are repeated but information on the load is not defined; it is unknown and varying. It is shown how the proposed controller is effective because of its capability to memorize the control skill for each task using neural networks. Simulation results demonstrate the effectiveness of the proposed control scheme for the robotic manipulator, in comparison with the conventional adaptive neural network controllers (ANNC). © 2005 IEEE
  6. Keywords:
  7. Computer simulation ; Feedback control ; Manipulators ; Neural networks ; Nonlinear control systems ; Adaptive feedback linearizing controllers ; Adaptive multi model controllers ; CMAC neural networks ; System nonlinearity ; Adaptive control systems
  8. Source: 2005 IEEE International Conference on Industrial Technology, ICIT 2005, Hong Kong, 14 December 2005 through 17 December 2005 ; Volume 2005 , 2005 , Pages 1012-1017 ; 0780394844 (ISBN); 9780780394841 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/1600784