Motor Control

Robotic Liquid Tension Identification with Particle Swarm Optimized Neural Network

By

Hong Xuan Qian, Jian Bing Wu, Yu Hui Shi and Jun Steed Huang

23 – 26 September 2012

Bandung, Indonesia

2Power Matters.

About the authors and the university

� The authors:• Hong was among the key engineers who set up the first automation line in China for Purina (now part of Nestle), her major is

industry automation and its applications, she is co-founder of GenieView Inc.

• Dr. Wu is a full time professor at Jiangsu University, who pioneered the research in mathematical models for sensor less motor, she collaborates well with industry partners.

• Prof. Shi is the adjunct professor at Jiangsu University, who co-authored the early books of Computational Intelligence ISBN9781558607590 and Swarm Intelligence ISBN9781558605954.

• Prof. Huang is the distinguished professor at Jiangsu University, who has initiated a number of multi-national industry-academic collaborations, he is co-founder of GenieView Inc.

� The university• Up to 8 digits 3.1415926 made by Wenyuan at AD461 remained the most accurate for following 900+ years, was from Jiangsu

where our University is exactly located!

22012 IEEE Symposium on Industrial Electronics and Applications

祖冲之祖冲之祖冲之祖冲之

3Power Matters.

Agenda

1. INTRODUCTION

2. TENSION MODEL FOR ROBOT MOTOR

3. SWARM NEURON OPTIMIZATION

4. MATLAB SIMULATION

5. CONCLUSION AND FUTURE WORK


4Power Matters.

1. Problem: maintain a constant liquid tension


A robot arm is tasked to apply the liquid glue on an

object with constant tension. There are a number of

ways to convert motor’s torque to linear tension:

�Single Sensor less Motor is simple and cheap, but

the tension may fluctuate.

�Single Motor with Sensors, the tension is under

control by the sensors but could be expensive.

�Dual Motor System without Sensors is relatively

simple, smooth and not too expensive.

Power Matters.

1. Solutions that we tried and failed


a) The direct motor model calculation cannot follow the liquid

variation from the random interference and measurement errors

occurred in our factory;

b) Kalman filter can deal with random noise and measurement

errors, but it can not follow the variation of individual motor

parameter and its related aging factor;

c) The complicated adaptive model method can partially deal with

the individual variation, but it does not treat the random noise

and measurement error, as it may not converge properly due to

real time computation burden.

d) The error Back-Propagation (BP) algorithm is a popular training

method for feed-forward Neural Network (NN), but we found the

parameter trained for one motor does not fit the other.

Power Matters.

2. Liquid tension = Right tension – Left tension


M1M0

kl kr

f1f2

f0

M2

ARM

2r�1r�

r r

lf rf

object

glue

motor inertia is analogous to mass, etc:

Motor belt Mass flow

Inertia Mass

Torque Disturbance

Rotation Line Speed

Tension Force

Power Matters.

2. Nonlinear map: current -> speed -> tension


IsL IeL

“s” means starting, “e” means ending, “L” means left, “R” means right;

we want the liquid tension or tension difference between left belt and

right belt remains constant, from starting to the ending position during

the glue brushing process.

fsL = feL

current arm tension

rotation speed

- fsR - feR

LEFT

RIGHT

IsR IeR

Power Matters.

2. Nonlinear liquid boundary condition


s

“s” means starting, “e” means ending, “o” means object;

we want the liquid tension has minimum variation,

it means optimum brushing speed.

feo

position

linear speed

fso

Object in between

eliquid tension

Power Matters.

3. Neurons to learn nonlinear relationships


1�z )(ˆ kF

)2(1 �kis�1�z

1�z

1�z

)1(1 �kis�

)(1 kis�)1(1 �kis�

)2(2 �ki s�1�z

1�z

)2(2 �ki s�1�z

1�z

)(1 kis�

)2(1 �ki s�)(2 kis�

)1(2 �kis�

)(2 kis�)1(2 �ki s�

Tension

Control

Model

NN

Neural network s-Sigmoid function

Captures nonlinear from both liquid

and motor:

浦东

浦东

Power Matters.

4. PSO is smooth and quick!

1


Merits

Optimized

STD of

Epoch

Average

Epoch

STD of Target

Value

BP 89 71 0.04

PSO 40 41 0.02

Power Matters.

5. Conclusion and future work

� The equation of liquid tension difference for robot motor driven arm system controlled by current is obtained.

� A neural network trained by PSO identifies the liquid tension from dual-motor system using laboratory data.

� The simulation shows the approach is a viable solution for high volume and precise tooling robot arms.

� New algorithm doubles the speed and the smoothness at the same cost.

� The disadvantage is that the amount of statistics needed could be huge to reduce mechanical vibration noise.

1


12Power Matters.

Thank you!

� Questions are welcome:

1


[email protected];

[email protected]

[email protected]

[email protected]

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