Fuzzytech Crane Simulation

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Case Study

Container Crane Control


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Objectives of Ports

For delivery of goods through containerstransported by cargo ships.

Example is PTP in Johore, Wesport in

Klang and of course Singapore.


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Crane Productivity

Crane productivity ismeasured by how fast thePort Authority can movethe cranes.

Singapore = 25moves/hour

Jakarta = 17 moves/hour

Malaysia Westport=22moves/hour


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Crane Productivity in Westport,

Port Klang


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Container Crane Simulator


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Loading and unloading ofcontainers are done inharbors in every countryaround the world.

For transportation ofmanufactured goods,food, etc.

Container cranes areused for such purpose.


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Operations and Problems

When a container is picked up and the cranehead starts to move, the container begins tosway.

Swaying of the container is not a problem duringtransportation but a swaying container cannot bereleased.


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Two ways to solve this problem:

1.To position the crane head exactly over the targetposition, and then just wait until the sway dampens to

an acceptable level.

2.To pick up the container and just move slowly that nosway ever occurs.

Both ways would be alright on a non-windy day but ittakes too much time.

An alternative is to build container cranes whereadditional cables fix the position of the containerduring operation- but this would be too expensive.


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For these reasons, most container cranesuse continuous speed control of the cranemotor- a human operator then control thespeed of the motor.

The operator has to simultaneouslycompensate for the sway and make surethe target position is reached in time.

This is not an easy and would need veryskilled operators.


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Several Control Modes Many engineers have tried to automate this

control task of controlling the crane by using: Conventional PID Control

Model-based control

Fuzzy logic control

Problems with PID This is a nonlinear problem.

Minimizing the swaying of the container is important whenthe container is closed to the target where PID is insufficientdue to high nonlinearity.

Problems with Model-based control Usually math-models tend to be an assumption (reduced-

order model) and the crane motor behavior is far less linearthan assumed in the model.

The crane head only moves with friction. Disturbances such as wind cannot be modelled easily.


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A Linguistic Control Strategy

A skilled operator is capable tocontrol the crane.

He does not even need to usedifferential equations or a cable-length sensor which manycontrol techniques would

require.

So how does he do it?


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Once he has picked the container, he starts the crane with mediumpower to see how the container sways.

Depending on the reaction, he adjusts motor power to get thecontainer a little behind the crane head.

In this position, maximum speed can be reached with minimumsway.

Getting closer to the target position, the operator reduces motor

power or might even apply negative brake.

With that the container gets a little ahead of the crane head until thecontainer reaches the target position.

Then motor power is increased so that the crane head is over the

target position and sway is zero.

Human-operated

Crane System


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1. Start with medium power.

2. If you get started and still far away from thetarget, adjust the motor power so the containergets a little behind the crane head.

3. If you are closer to the target, reduce motorspeed so the container gets a little ahead of thecrane head.

4. When the container is very close to the targetposition, power up the motor.

5. When the container is over the target and swayis zero, stop the motor.

Analysis ofOperators actions


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See if you can write the rules to

control this container crane system

First identify the antecedent variables

Next the consequent variable

Then write the rules according to the

analysis of the operators action in the

previous page.

6 rules can be written- Try?


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The Control Strategy

1. IF Distance = far AND Angle = zeroTHEN power = pos_medium

2. IF Distance = far AND Angle = neg_smallTHEN power = pos_big

3. IF Distance = far AND Angle = neg_bigTHEN power = pos_medium

4. IF Distance = medium AND Angle = neg_smallTHEN power = neg_medium

5. IF Distance = close AND Angle = pos_smallTHEN power = pos_medium

6. IF Distance = zero AND Angle = zeroTHEN power = zero


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Fuzzy Controller Design

From what you have studied thus far, lets

design our Fuzzy Controller to solve thisproblem.

What next?


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Conventional Fuzzy Control

Fuzzification

Inference

Defuzzficatio

n

Anteceden

ts

Consequ

ent


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Antecedents

Partition or break your antecedents into

several fuzzy sets that can reflect the system


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For each antecedent, identify the range for the universeof discourse.

Distance Metres or Yards

Angle From -90o to +90o

Break up each antecedent 5 fuzzy sets each and providethe appropriate label that reflect the variables

Distance Angle

too far

zero

closemedium

farneg_big

neg_

small

zeropos_

small

pos_

big


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Distance

A

ngle

Next design appropriate membership

functions for each fuzzy set and setthem on the universe of each

antecedent

Typical design would be as follows:


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Similarly for the consequent

Identify the motor power range

Break up into 5 fuzzy sets

Power

neg_high

neg_medium

zeropos_

medium

pos_high


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Membership functions of the Consequent

Motor Power


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Next develop the rules

use matrix form

How many rules maximum?

Distance

Ang

le

NB

NS

ZE

PS

PB

Too far zero close med far


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Rules proposed by

Fuzzy Tech software


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Inference procedure?

Max-min or (min/max as

described in FuzzyTech)

Max-dot

Etc.


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Defuzzification

Centroid

Mean of max


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Try out the simulation exercise

Documents

Fuzzytech Crane Simulation