1. Case Study Container Crane Control

2. Objectives of Ports For delivery of goods through containers transported by cargo ships. Example is PTP in Johore, Wesport in Klang and of course Singapore.

3. Crane Productivity Crane productivity is measured by how fast the Port Authority can move the cranes. Singapore = 25 moves/hour Jakarta = 17 moves/hour Malaysia Westport=22 moves/hour

4. Crane Productivity in Westport, Port Klang

5. Container Crane Simulator

6. Container Crane Control Loading and unloading of containers are done in harbors in every country around the world. For transportation of manufactured goods, food, etc. Container cranes are used for such purpose.

7. Operations and Problems When a container is picked up and the crane head starts to move, the container begins to sway. Swaying of the container is not a problem during transportation but a swaying container cannot be released.

8. Container Crane Control Two ways to solve this problem: 1.To position the crane head exactly over the target position, and then just wait until the sway dampens to an acceptable level. 2.To pick up the container and just move slowly that no sway ever occurs. Both ways would be alright on a non-windy day but it takes too much time. An alternative is to build container cranes where additional cables fix the position of the container during operation- but this would be too expensive.

9. For these reasons, most container cranes use continuous speed control of the crane motor- a human operator then control the speed of the motor. The operator has to simultaneously compensate for the sway and make sure the target position is reached in time. This is not an easy and would need very skilled operators.

10. 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 when the container is closed to the target where PID is insufficient due 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 linear than assumed in the model. The crane head only moves with friction. Disturbances such as wind cannot be modelled easily.

11. A Linguistic Control Strategy A skilled operator is capable to control the crane. He does not even need to use differential equations or a cable- length sensor which many control techniques would require. So how does he do it?

12. Once he has picked the container, he starts the crane with medium power to see how the container sways. Depending on the reaction, he adjusts motor power to get the container a little behind the crane head. In this position, maximum speed can be reached with minimum sway. 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 the container 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

13. 1.Start with medium power. 2.If you get started and still far away from the target, adjust the motor power so the container gets a little behind the crane head. 3.If you are closer to the target, reduce motor speed so the container gets a little ahead of the crane head. 4.When the container is very close to the target position, power up the motor. 5.When the container is over the target and sway is zero, stop the motor. Analysis of Operator’s actions

14. 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?

15. The Control Strategy 1.IF Distance = far AND Angle = zero THEN power = pos_medium 2.IF Distance = far AND Angle = neg_small THEN power = pos_big 3.IF Distance = far AND Angle = neg_big THEN power = pos_medium 4.IF Distance = medium AND Angle = neg_small THEN power = neg_medium 5.IF Distance = close AND Angle = pos_small THEN power = pos_medium 6.IF Distance = zero AND Angle = zero THEN power = zero

16. Fuzzy Controller Design From what you have studied thus far, let’s design our Fuzzy Controller to solve this problem. What next?

17. Conventional Fuzzy Control Fuzzification InferenceDefuzzfication Antecedents Consequent

18. Antecedents Partition or break your antecedents into several fuzzy sets that can reflect the system

19. For each antecedent, identify the range for the universe of discourse. Distance  Metres or Yards Angle  From -90 o to +90 o Break up each antecedent 5 fuzzy sets each and provide the appropriate label that reflect the variables Distance Angle too far zero close medium far neg_big neg_ small zero pos_ small pos_ big

20. Distance Angle Next design appropriate membership functions for each fuzzy set and set them on the universe of each antecedent Typical design would be as follows:

21. Similarly for the consequent Identify the motor power range Break up into 5 fuzzy sets Power neg_high neg_medium zero pos_ medium pos_high

22. Membership functions of the Consequent Motor Power

23. Next develop the rules use matrix form How many rules maximum? Distance Angle NB NS ZE PS PB Too far zero close med far

24. Rules proposed by Fuzzy Tech software

25. Inference procedure? Max-min or (min/max as described in FuzzyTech) Max-dot Etc.

26. Defuzzification Centroid Mean of max

27. Try out the simulation exercise