Robust fuzzy neural network control for linear ceramic motor drive via backstepping design technique

Rong Jong Wai, Faa Jeng Lin, Rou Yong Duan, Kuan Yun Hsieh, Jeng Dao Lee

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


This study presents a robust fuzzy-neural-network (RFNN) control system for a linear ceramic motor (LCM) that is driven by an unipolar switching full-bridge voltage source inverter using LC resonant technique. The structure and operating principle of the LCM are introduced. Since the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a RFNN control system is designed based on the hypothetical dynamic model to achieve high-precision position control via the backstepping design technique. In the RFNN control system a fuzzy neural network (FNN) controller is used to learn an ideal feedback linearization control law, and a robust controller is designed to compensate the shortcoming of the FNN controller. All adaptive learning algorithms in the RFNN control system are derived from the sense of Lyapunov stability analysis, so thai system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed RFNN control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.

Original languageEnglish
Pages (from-to)102-112
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Issue number1
StatePublished - Feb 2002


  • Backstepping technique
  • Fuzzy neural network
  • Linear ceramic motor
  • Robust control


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