Neurocomputing 50 (2003) 365–390 www.elsevier.com/locate/neucom Robust recurrent fuzzy neural network control for linear synchronous motor drive system Faa-Jeng Lin a , Rong-Jong Wai b; ∗ a Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan, ROC b Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan, ROC Received 31 March 2001; accepted 17 February 2002 Abstract A robust recurrent fuzzy neural network control (RFNNC) system is proposed to control the position of the mover of a permanent magnet linear synchronous motor drive system in this study. In the proposed RFNNC system, a RFNN controller is the main tracking controller, that is used to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the RFNN controller. Moreover, to relax the requirement for the bound of lumped uncertainty, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, a RFNNC system with adaptive bound estimation is investigated. In the control system a simple adaptive algorithm is utilized to estimate the bound of lumped uncertainty. In addition, simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties. c  2002 Elsevier Science B.V. All rights reserved. Keywords: Recurrent fuzzy neural network; Taylor series; Adaptive bound estimation; Permanent magnet linear synchronous motor servo drive 1. Introduction The direct drive design of mechanical applications based on Permanent magnetic linear synchronous motor (PMLSM) is a viable candidate to meet the increasing de- mands for higher contouring accuracy at high machine speeds. The direct drive design based on PMLSM has the following advantages over its indirect counterpart: (1) no backlash and less friction; (2) high speed and high precision in long distance location; * Corresponding author. Tel.: +886-3-463-8800=429; fax: +886-3-463-9355. E-mail address: rjwai@saturn.yzu.edu.tw (R.-J. Wai). 0925-2312/03/$ - see front matter c  2002 Elsevier Science B.V. All rights reserved. PII:S0925-2312(02)00572-6