By Topic

A Neural-Network-Identifier and Fuzzy-Controller-Based Algorithm for Dynamic Decoupling Control of Permanent-Magnet Spherical Motor

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Changliang Xia ; School of Electrical Engineering and Automation, Tianjin University, Tianjin , China ; Chen Guo ; Tingna Shi

This paper proposes a dynamic model of permanent-magnet spherical motor (PMSM) and puts forward a dynamic decoupling control algorithm of the motor, using fuzzy controllers (FCs) and a neural network identifier (NNI). PMSM is a multivariable nonlinear system with strong interaxis couplings. A computed torque method structure is applied to PMSM. There are such uncertainties as estimated errors of the model and external perturbations, which may influence the precision of the control system. A back-propagation algorithm with additional momentum term and self-adaptive learning rate applied to feed-forward neural network can approach nonlinear functions with a learning rate adjusted online, which helps to improve training speed. In this paper, an NNI is applied to identify the uncertainties online. An adaptive-neuro-fuzzy-inference-system-based FC is applied, which has self-adaptive ability and strong robustness. Simulation results preliminarily validate that the algorithm proposed in this paper can eliminate the influences of interaxis nonlinear couplings effectively to actualize dynamic decoupling control. Furthermore, the static and dynamic performances of the control system have been improved greatly with strong robustness to uncertainties. A hypothetical microprocessor system is proposed, and simple experiments of spinning operation are carried out as a foundation for further study.

Published in:

IEEE Transactions on Industrial Electronics  (Volume:57 ,  Issue: 8 )