By Topic

Adaptive inverse microstep tracking control of a hybrid stepper motor using RBF and MLP neural networks

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

2 Author(s)
Y. Bastani ; Dept. of Mech. Eng., Sharif Univ. of Technol., Tehran, Iran ; G. R. Vossoughi

In this paper, a radial basis function neural network-based (RBFNN) adaptive inverse controller for real-time position tracking control of a two phase hybrid stepper motor in microstep mode is presented. To ensure system's robustness and closed loop stability and to improve the performance of the controller, the RBF based adaptive inverse control is combined with a closed loop PID controller. Experimental results are provided for various types of trajectories, comparing the performance of the proposed controller to the same neuro-controller using feed forward back-propagation neural networks (FFBP) proposed previously by the authors. The results show the superior performance of RBFNN over FFBP neural networks. Also the comparison of neuro controllers with a conventional fixed gain stand-alone PID controller show the improvement of the controller performance from 80% up to 99.7% decrease in the mean squared error (MSE) for different trajectories. Trajectories were tracked with the maximum error of 0.02 up to 0.06 degrees. Also the robustness of the method is confirmed through experimental results comparing neuro-controllers and the conventional PID controller by varying the load's inertia and disturbance torques. For this purpose two methods were examined. First using the same neuro-controllers trained by the initial training data and in the second method, neuro-controllers were adapted by new training data according to the new working conditions.

Published in:

IEEE International Conference Mechatronics and Automation, 2005  (Volume:3 )

Date of Conference: