This paper proposes using a hybrid neural network trained by particle swarm optimization to identify tension in a brake-motor lifting system based on stator current data. The neural network uses both radial basis function and sigmoid basis function neurons arranged like the structure of the splenium in the brain. Simulation results show this approach can precisely control the lifting system with low cost and minimize motor current surge. The algorithm is expected to save around 15% of braking energy efficiency.

1. Control of Brake Motor with Nonlinear Hybrid
Neural Network
Prepared for
ICCECT6866
By
Jun Steed Huang, Jing Wen Zhu and Mary Opokua Ansong
Sunday, December 8, 2013, Xiangtan
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Youngest University in Oldest City
 10000000 Years Human Residency
 Most privatized city in China, even the university!
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About the Authors
 Jun Steed Huang, Professor
 SuqianCollege
 Jiangsu, China
 steedhuang@ujs.edu.cn
 Jing Wen Zhu, Master
 University of Southern California
 Los Angeles, California
 zhujingw@usc.edu
 Mary Opokua Ansong, Ph.D
 Jiangsu University
 Zhenjiang, China
 mo.ansong@yahoo.com
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Agenda
 Smart brake control of heavy lifting motor
 Nonlinear soft friction identification
 Novel hybrid of radial and sigmoid neural network
 Mutated particle swarm optimization
 Braking energy efficiency
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Can we coordinate friction with torque?
5
The motor inertias is analogous with the translational mass,
and the line analogous with the translational spring,
while motor torque is corresponding to a disturbance force,
and motor circular speed is like mass linear speed,
finally, the tension inside wire is similar to the force inside the
spring. the soft brake friction is equivalent to additional mass load.
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Previous Solutions
6
Potentiometers detect the position of dancer rolls and
compare with the given position. The errors are sent to
motor controller to keep the tension constant, this entire
process takes feedback time; so does speed sensor.
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PSO HBF NN
7
Smart motor has neither speed sensor, nor tension sensor,
everything is predicted by using a Particle Swarm
Optimization (PSO) trained Hybrid Basis Function (HBF)
neural network on synchronized motors’ currents.
The algorithm learns how to increase the friction (engage
brake) or reduce friction (release brake) without causing
motor current surge (damage).
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+

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Particle Swarm Optimized Neural Network
8
Neural network maps current to speed
and speed to frictional force by learning
from test data using mutated algorithm
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1−
z )(ˆ kF
)2(1 −kisα
1−
z
1−
z
1−
z
)1(1 −kisβ
)(1 kisα
)1(1 −kisα
)2(2 −kisα
1−
z
1−
z
)2(2 −kisβ
1−
z
1−
z
)(1 kisβ
)2(1 −kisβ
)(2 kisα
)1(2 −kisα
)(2 kisβ
)1(2 −kisβ
Tensi on
Cont r ol
Model
NN

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Oligodendrocytes for splenium
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The nervous system of mammals
depends crucially on myelin sheaths,
which reduce ion leakage and
decrease the capacitance of the cell
membrane, thus increases impulse
speed. Impulse speed of myelinated
axons increases linearly with the
axon diameter. The optimal g-ratio of
axon diameter divided by the total
fiber diameter (which includes the
myelin) is 0.55 to 0.72. Here SBF will
take the weight of 0.72 and the RBF
will occupy the rest space, 0.72 is the
g-ratio for our splenium.

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Nano meter scan of splenium for thinking
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Future design example
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Friction identification via SBF Optimization
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Friction identification via RBF Optimization
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Friction identification via HBF Optimization
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Conclusions
 A neural network trained by PSO with Hybrid neuron (splenium type)
identifies the tension from brake-motor lifting system based on laboratory
test data.
 The original non-linear line tension identification model is built up based on
the stator current in axes vector in two-phase stationary coordinated
system.
 The simulation shows that the proposed approach is a viable engineering
solution towards the low cost high volume and precise controlling of the
lifting system.
 New algorithm makes the trained data more consistent with each other; in
other words, it minimizes the manufacture cost of such motors.
 Our simulation indicates that the amount of energy expected to be saved
is around 15%.
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Thank You
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