Calls for papers


International Journal of Advanced Mechatronic Systems
International Journal of Advanced Mechatronic Systems


Special Issue on: "Fuzzy Neural Control for Mechatronic Systems"

Guest Editors:
Dr. Wen Yu, CINVESTAV-IPN, Mexico
Dr. Jin-Hua She, Tokyo University of Technology, Japan

Both neural networks (NN) and fuzzy logic systems (FLS) are universal estimators. Resent results show that the fusion procedure of these two different technologies has significant advantages over standard feedback controllers for unknown nonlinear systems. Mostly, a neural network or a fuzzy logic system is used to approximate the nonlinearity of the system to be controlled and a controller is synthesised based on universal function approximators (indirect control), or a control law is directly designed using NN, or FLS based on stability theories. Another approach to feedback control design relies on using fuzzy neural networks to approximately solve various nonlinear controller design equations.

In addition to the classical feedback control theory, adaptive control and robust control are effective techniques to treat system uncertainties but generally suffer from the disadvantage of being able to achieve asymptotical convergence of the tracking error; in addition, the on-line computation load is usual heavy. In robust control designs, a fixed control law based on a priori information on the uncertainties (usually bounds on these uncertainties) is designed to compensate their effects, and exponential convergence of the tracking error to a (small) ball centered at the origin is obtained.

There is a gap between control system community and computational intelligence (e.g. neural networks and fuzzy systems) community. The purpose of this special issue is to bring together fuzzy neural networks and feedback control design techniques.

Subject Coverage
Relevant topics include, but are not limited to, the following:
  1. Feedback control using neural networks, fuzzy logic and fuzzy neural networks
  2. Robust neural (fuzzy) control
  3. Compensation of nonlinearities with (fuzzy) neural networks for mechatronic systems
  4. Identification and observers via (fuzzy) neural networks for mechatronic systems
  5. Applications of neural (fuzzy) control in mechatronic systems

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere

All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page

Important Dates

Submission deadline: 15 May, 2009