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Adaptive multi-model sliding mode control of robotic manipulators using soft computing

Sadati, N ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2007.06.019
  3. Publisher: Elsevier , 2008
  4. Abstract:
  5. In this paper, an adaptive multi-model sliding mode controller for robotic manipulators is presented. By using the multiple models technique, the nominal part of the control signal is constructed according to the most appropriate model at different environments. Adaptive single-input-single-output (SISO) fuzzy systems or radial basis function (RBF) neural networks, regarding their functional equivalence property, are used to approximate the discontinuous part of control signal; control gain, in a classical sliding mode controller. The key feature of this scheme is that prior knowledge of the system uncertainties is not required to guarantee the stability. Also the chattering phenomenon in sliding mode control and the steady-state tracking error are eliminated. Moreover, a theoretical proof of the stability and convergence of the proposed scheme using the Lyapunov method is presented. © 2008 Elsevier B.V. All rights reserved
  6. Keywords:
  7. Controllers ; Flexible manipulators ; Functions ; Fuzzy inference ; Fuzzy neural networks ; Fuzzy systems ; Lyapunov methods ; Manipulators ; Nonlinear control systems ; Radial basis function networks ; Robotics ; Sliding mode control ; Soft computing ; System stability ; Adaptive Control ; Lyapunov stability ; Multiple models control ; RBF Neural Network ; Robotic manipulators ; Adaptive control systems ; Artificial neural network ; Conference paper ; Fuzzy system ; Mathematical computing ; Mathematical model ; Priority journal ; Radial based function ; Robotics ; Simulation ; Theoretical study
  8. Source: Neurocomputing ; Volume 71, Issue 13-15 , 2008 , Pages 2702-2710 ; 09252312 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0925231208002336