堆垛机英文资料翻译

.

翻

译

英文原文

Realization of Neural Network Inverse System with PLC in Variable

Frequency Speed-Regulating System

Abstract. The variable frequency speed-regulating system which

consists

of

an

induction

motor

and

a

general

inverter,

and

controlled

by

PLC

is

widely

used

in

industrial

field.

.However,

for

the

multivariable,

nonlinear and strongly coupled induction motor, the control performance

is

not

good

enough

to

meet

the

needs

of

speed-regulating.

The mathematic

model

of

the

variable

frequency

speed-regulating

system

in

vector

control

mode

is

presented

and

its

reversibility

has

been

proved.

By

constructing

a neural network inverse system and combining it with the variable

frequency

speed- regulating

system,

a

pseudo-linear

system

is

completed,

and

then

a

linear

close-loop

adjustor

is

designed

to

get

high

performance.

Using PLC, a neural network inverse system can be realized in actural

system. The results of experiments have shown that the performances of

variable frequency speed-regulating system can be improved greatly and

the practicability of neural network inverse control was testified.

uction

In recent years, with power electronic technology, microelectronic

technology and modern control theory infiltrating into AC electric

driving system, inverters have been widely used in speed- regulating of

AC motor. The variable frequency speed-regulating system which consists

of an induction motor and a general inverter is used to take the place

of

DC

speed-regulating

system.

Because

of

terrible

environment

and

severe

disturbance

in

industrial

field,

the

choice

of

controller

is

an

important

problem. In reference [1][2][3], Neural network inverse control was

realized by using industrial control computer and several data

acquisition

cards.

The

advantages

of

industrial

control

computer

are

high

computation speed, great memory capacity and good compatibility with

;.

.

other software etc. But industrial control computer also has some

disadvantages in industrial application such as instability and

fallibility and worse communication ability. PLC control system is

special designed for industrial environment application, and its

stability and reliability are good. PLC control system can be easily

integrated into field bus control system with the high ability of

communication configuration, so it is wildly used in recent years, and

deeply welcomed. Since the system composed of normal inverter and

induction motor is a complicated nonlinear system, traditional PID

control strategy could not meet the requirement for further control.

Therefore, how to enhance control performance of this system is very

urgent.

The neural network inverse system [4][5] is a novel control method

in recent years. The basic idea is that: for a given system, an inverse

system

of

the

original

system

is

created

by

a

dynamic

neural

network,

and

the combination system of inverse and object is transformed into a kind

of

decoupling

standardized

system

with

linear

relationship.

Subsequently,

a linear close-loop regulator can be designed to achieve high control

performance. The advantage of this method is easily to be realized in

engineering.

The

linearization

and

decoupling

control

of

normal

nonlinear

system can realize using this method.

Combining

the

neural

network

inverse

into

PLC

can

easily

make

up

the

insufficiency of solving the problems of nonlinear and coupling in PLC

control system. This combination can promote the application of neural

network inverse into practice to achieve its full economic and social

benefits.

In this paper, firstly the neural network inverse system method is

introduced, and mathematic model of the variable frequency

speed-regulating system in vector control mode is presented. Then a

reversible

analysis

of

the

system

is

performed,

and

the

methods

and

steps

are given in constructing NN-inverse system with PLC control system.

;.

.

Finally, the method is verified in experiments, and compared with

traditional PI control and NN-inverse control.

Network Inverse System Control Method

The basic idea of inverse control method [6] is that: for a given

system,

an

α

-th

integral

inverse

system

of

the

original

system

is

created

by

feedback

method,

and

combining

the

inverse

system

with

original

system,

a kind of decoupling standardized system with linear relationship is

obtained, which is named as a pseudo linear system as shown in Fig.1.

Subsequently, a linear close-loop regulator will be designed to achieve

high control performance.

Inverse

system

control

method

with

the

features

of

direct,

simple

and

easy

to

understand

does

not

like

differential

geometry

method

[7],

which

is

discusses

the

problems

in

domain

main

problem

is

the

acquisition of the inverse model in the applications. Since non-linear

system

is

a

complex

system,

and

desired

strict

analytical

inverse

is

very

difficult to obtain, even impossible. The engineering application of

inverse

system

control

doesn’t

meet

the

expectations.

As

neural

network

has

non-linear

approximate

ability,

especially

for

nonlinear complexity

system,

it

becomes

the

powerful

tool

to

solve

the

problem.a

?

th

NN

inverse

system integrated inverse system with non-linear ability of the neural

network can avoid the troubles of inverse system method. Then it is

possible to apply inverse control method to a complicated non-linear

system.

a

?

th

NN

inverse

system

method

needs

less

system

information

such

as

the

relative

order

of

system,

and

it

is

easy

to

obtain

the

inverse

model

by neural network training. Cascading the NN inverse system with the

original system, a pseudo-linear system is completed. Subsequently, a

linear close-loop regulator will be designed.

3. Mathematic Model of Induction Motor Variable Frequency

Speed-Regulating System and Its Reversibility

;.

.

Induction

motor

variable

frequency

speed-regulating

system

supplied

by the inverter of tracking current SPWM can be expressed by 5-th order

nonlinear model in d-q two-phase rotating coordinate. The model was

simplified as a 3-order nonlinear model. If the delay of inverter is

neglected,

the model is expressed as follows:

(1)

where

denotes

synchronous

angle

frequency,

and

is

rotate

speed.

are stator’s current, and

(d,q)axis.

is number of poles.

are rotor’s flux linkage in

is mutual inductance, and

is

rotor’s

inductance.

J

is

moment

of

inertia.

and

is load torque.

In vector mode, then

is

rotor’s

time

constant,

Substituted it into formula (1), then

(2)

Taking reversibility analyses of forum (2), then

;.

.

The state variables are chosen as follows

Input variables are

Taking the derivative on output in formula(4), then

(5)

(6)

Then the Jacobi matrix is Realization of Neural Network Inverse System

with PLC

(7)

(8)

As

so

and system is

reversible. Relative-order of system is

When

the

inverter

is

running

in

vector

mode,

the

variability

of

flux

linkage can be neglected (considering the flux linkage to be

invariableness and equal to the rating). The original system was

simplified as an input and an output system concluded by forum (2).

According to implicit function ontology theorem, inverse system of

formula (3)

;.

.

can be expressed as

(9)

When

the

inverse

system

is

connected

to

the

original

system

in

series,

the pseudo linear compound system can be built as the type of

4. Realization Steps of Neural Network Inverse System

4.1 Acquisition of the Input and Output Training Samples

Training samples are extremely important in the reconstruction of

neural network

inverse system. It

is not only need to

obtain the dynamic

data of the original system, but also need to obtain the static date.

Reference signal should include all the work region of original system,

which

can

be

ensure

the

approximate

ability.

Firstly

the

step

of

actuating

signal is given corresponding every 10 HZ form 0HZ to 50HZ, and the

responses

of

open

loop

are

obtain.

Secondly

a

random

tangle

signal

is

input,

which is a random

signal cascading

on

the step of actuating

signal every

10

seconds,

and

the

close

loop

responses

is

obtained.

Based

on

these

inputs,

1600 groups

training samples are gotten.

4.2 The Construction of Neural Network

A static neural network and a dynamic neural network composed of

integral

is

used

to

construct

the

inverse

system.

The

structure

of

static

neural network is 2 neurons in input layer, 3 neurons in output layer,

and

12

neurons

in

hidden

layer.

The

excitation

function

of

hidden

neuron

is monotonic smooth hyperbolic tangent function. The output layer is

composed of neuron with linear threshold excitation function. The

training datum are the corresponding speed of open-loop, close-loop,

first order

derivative of these speed, and setting reference speed. After 50 times

;.

.

training, the training error of neural network achieves to 0.001. The

weight and threshold of the neural network are saved. The inverse model

of original system is obtained.

5 .Experiments and Results

5.1 Hardware of the System

The

hardware

of

the

experiment

system

is

shown

in

Fig

5.

The

hardware

system includes upper computer installed with Supervisory & Control

configuration

software

WinCC6.0

[8],

and

S7-300

PLC

of

SIEMENS,

inverter,

induction motor and photoelectric coder.

PLC

controller

chooses

S7-315-2DP,

which

has

a

PROFIBUS-DP

interface

and a MPI interface. Speed acquisition module is FM350-1. WinCC is

connected with S7-300 by CP5611 using MPI protocol.

The

type

of

inverter

is

MMV

of

SIEMENS.

It

can

communicate

with

SIEMENS

PLC by USS protocol. ACB15 module is added on the

inverter in this system.

5.2 Software Program

5.2.1 Communication Introduction

MPI

(MultiPoint

Interface)

is

a

simple

and

inexpensive

communication

strategy

using

in

slowly

and

non- large

data

transforming

field.

The

data

transforming between WinCC and PLC is not large, so the MPI protocol is

chosen.

The MMV inverter is connected to the PROFIBUS network as a slave

station, which is mounted with CB15 PROFIBUS module. PPO1 or PPO3 data

type can be chosen. It permits to send the control data directly to the

inverter addresses, or to use the system function blocks of STEP7V5.2

SFC14/15.

OPC can efficiently provide data integral and intercommunication.

Different

type

servers

and

clients

can

access

data

sources

of

each

other.

Comparing

with

the

traditional

mode

of

software

and

hardware

development,

;.