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Reduction of Electrical Energy Consumed by Feed-Drive Systems Using Sliding-Mode Control With a Nonlinear Sliding Surface

Research Authors
Abd El Khalick Mohammad, Naoki Uchiyama and Shigenori Sano
Research Year
2014
Research Journal
EEE Transaction on Industrial Electronics
Research Publisher
NULL
Research Vol
Vol. 61 - No. 6
Research Rank
1
Research_Pages
pp. 2875 - 2882
Research Website
NULL
Research Abstract

The large amount of energy consumed in machining
by feed-drive systems has become an important issue in recent
years because machine tools are extensively used worldwide. This
paper presents a novel sliding-mode controller with a nonlinear
sliding surface (NLSS) to improve the machining accuracy of
ball-screw feed-drive systems. Unlike the conventional slidingmode
control design, the proposed NLSS varies according to the
output (controlled variable) so that the damping ratio of the
system changes from its initial low value to its final high value as
the output changes from its initial value to the reference point.
Hence, the proposed algorithm allows a closed-loop system to
simultaneously achieve low overshoot and a small settling time,
resulting in a smaller error. Experiments to verify the effectiveness
of the proposed approach are carried out for a ball-screw
feed-drive system for two perspectives. The first perspective is
to show the effectiveness of the proposed NLSS in reducing the
tracking error, while the second one is to verify the ability of the
proposed approach to reduce the consumed energy and control
input variation. For the first case, sliding-mode controllers with
and without the nonlinear term are compared under the same
parameters. The mean of the tracking error magnitude was reduced
by 35% without additional electrical energy or control input
variation in the controller with NLSS. In the second case, the
linear gain is increased for the controller with the linear surface
to obtain similar tracking performance. By using the controller
with an NLSS, the consumed energy and control input variation
were reduced by about 12.9% and 19.1%, respectively.