Sliding-mode-controlled slider-crank mechanism with fuzzy neural network

Faa Jeng Lin, Rong Jong Wai

Research output: Contribution to journalArticlepeer-review

58 Scopus citations


The dynamic response of a sliding-mode-controlled slider-crank mechanism, which is driven by a permanent-magnet (PM) synchronous servo motor, is studied in this paper. First, a position controller is developed based on the principles of sliding-mode control. Moreover, to relax the requirement of the bound of uncertainties in the design of a sliding-mode controller, a fuzzy neural network (FNN) sliding-mode controller is investigated, in which an FNN is adopted to adjust the control gain in a switching control law on line to satisfy the sliding mode condition. In addition, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the FNN. Numerical and experimental results show that the dynamic behaviors of the proposed controller-motor-mechanism system are robust with regard to parametric variations and external disturbances. Furthermore, compared with the sliding-mode controller, smaller control effort results and the chattering phenomenon is much reduced by the proposed FNN sliding-mode controller.

Original languageEnglish
Pages (from-to)60-70
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Issue number1
StatePublished - Feb 2001


  • Fuzzy neural network
  • Permanent-magnet synchronous servo motor
  • Slider-crank mechanism
  • Sliding-mode control
  • Varied learning rates


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