Brake Motor with Neural Network

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

The 2013 International Conference on Control Engineering and Communication Technology

Power Matters.

CONFIDENTIAL FOR CAVIU

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Youngest University in Oldest City

10000000 Years Human Residency Most privatized city in China, even the university!

2The 2013 International Conference on Control Engineering and Communication Technology

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About the Authors

Jun Steed Huang, Professor SuqianCollege Jiangsu, China [email protected]

Jing Wen Zhu, Master University of Southern California Los Angeles, California [email protected]

Mary Opokua Ansong, Ph.D Jiangsu University Zhenjiang, China [email protected]


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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?

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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

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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

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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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Particle Swarm Optimized Neural Network

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Neural network maps current to speed and speed to frictional force by learning from test data using mutated algorithm


1−z )(ˆ kF

)2(1 −kisα1−z

1−z

1−z

)1(1 −kisβ

)(1 kisα)1(1 −kisα

)2(2 −kisα1−z

1−z

)2(2 −kisβ1−z

1−z

)(1 kisβ

)2(1 −kisβ)(2 kisα)1(2 −kisα

)(2 kisβ)1(2 −kisβ

Tensi onCont r olModel

NN

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Oligodendrocytes for splenium


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
