Adaptive multi-model CMAC-based supervisory control for uncertain MIMO systems

Sadati, N ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1109/ICTAI.2005.24
  3. Publisher: 2005
  4. Abstract:
  5. In this paper, an adaptive multi-model CMAC-based controller (AMCBC) in conjunction with a supervisory controller is developed for uncertain nonlinear MIMO systems. AMCBC is a kind of adaptive feedback linearizing controller where nonlinearity terms are approximated with multiple CMAC neural networks With the help of a supervisory controller, the resulting close-loop system is globally stable. The proposed control system is applied to control a robotic manipulators, 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 CMAC neural network. Simulation results demonstrate the effectiveness of the proposed control scheme for the robotic manipulators. © 2005 IEEE
  6. Keywords:
  7. Communication channels (information theory) ; Feedback control ; Manipulators ; Neural networks ; Robotics ; Uncertain systems ; Adaptive multi model ; MIMO systems ; Robotic manipulators ; Adaptive systems
  8. Source: ICTAI 2005: 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'05, Hong Kong, 14 November 2005 through 16 November 2005 ; Volume 2005 , 2005 , Pages 457-461 ; 10823409 (ISSN); 0769524885 (ISBN); 9780769524887 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/1562978