Abstract
A robust fuzzy neural network (RFNN) control system is proposed to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories in this study. In the proposed RFNN control system, a FNN controller is the main tracking controller, which is used to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. The ideal feedback linearization control law is designed based on the backstepping technique. Moreover, to relax the requirement for the bound of uncertainty, which comprises a minimum approximation error, optimal parameter vectors and higher-order terms in Taylor series, a RFNN control system with adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.
Original language | English |
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Pages | 4386-4390 |
Number of pages | 5 |
State | Published - 2004 |
Event | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004 |
Conference
Conference | WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings |
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Country/Territory | China |
City | Hangzhou |
Period | 15/06/04 → 19/06/04 |
Keywords
- Adaptive bound estimation
- Fuzzy neural network
- Permanent magnet linear synchronous motor
- Taylor series