Hybrid computed torque controller using fuzzy neural network for motor-toggle servomechanism

Faa Jeng Lin, Rong Jong Wai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The dynamic response of a hybrid computed torque controlled toggle mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is studied in this paper. First, based on the principle of computed torque control, a position controller is developed for the motor-toggle servomechanism. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a fuzzy neural network (FNN) uncertainty observer is utilized to adapt the lumped uncertainty on line. Furthermore, based on the Lyapunov stability a hybrid control system, which combines the computed torque controller, the FNN uncertainty observer and a compensated controller, is proposed to control the position of a slider of the motor-toggle servomechanism. The computed torque controller with FNN uncertainty observer is the main tracking controller, and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the FNN. Finally, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed hybrid control system are robust with regard to parametric variations and external disturbances.

Original languageEnglish
Title of host publicationIECON Proceedings (Industrial Electronics Conference)
PublisherIEEE Computer Society
Number of pages6
StatePublished - 2000

Publication series

NameIECON Proceedings (Industrial Electronics Conference)


  • Approximation error
  • Computer networks
  • Control systems
  • Fuzzy control
  • Fuzzy neural networks
  • Lyapunov method
  • Permanent magnets
  • Servomechanisms
  • Torque control
  • Uncertainty


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