Industrial Automation course

Transcription

1 Industrial Automation course Lesson 1 Introduction Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 1

2 What do we do Industrial Automation Course for the Monterrey PoliMi Theory + Exercises (if someone isn t in the right classroom, he can leave now) Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 2

3 Who am I Alberto Luigi Cologni Assistant UniBG (Control group) Tel alberto.cologni@unibg.it Theory Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 3

4 Who will help me Paolo Sangregorio PhD UniBG (Control group) Tel paolo.sangregorio@unibg.it Lab Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 4

5 Course organization Day Morning (8:30 12:30) Room Lecturer Afternoon (13:30 15:30) Room Lecturer July, 6 th 4 PT 1 Cologni 2 PT 1 Cologni July, 7 th 4 PT 1 Cologni 2 PT 1 Cologni July, 8 th 4 Seminari Cologni 2 Seminari Cologni July, 9 th 4 Seminari Cologni 2 Seminari Cologni July, 13 th 4 Conferenze Sangregorio 2 Conferenze Sangregorio July, 14 th 4 Seminari Sangregorio 2 Seminari Sangregorio July, 15 th 4 Seminari Cologni 2 Seminari Cologni Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 5

6 Assessment Written test, joined with Prof. Corno s course Topics: all the things that we see during the lessons Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 6

7 Topics Introduction to the Industrial Automation PLC Introduction IEC programming languages Ladder SFC ST Event driven modeling Introduction Petri nets Introduction Properties Examples / exercises Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 7

8 Introduction to the Industrial Automation Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 8

9 What is «Automation» (Garzanti Translated from Italian) The introduction of mechanic production processes, especially driven by electronic systems, in which the human operations are reduced to the minimum (Wiki) Use of control systems and information technologies to reduce the need for human work in the production of goods and services. In the scope of industrialization, automation is a step beyond mechanization Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 9

10 Why Automate allows to: Reduce the times / costs Increase the production volumes Increase the product quality (or, at least, standardize it) Increase the plants flexibility Produce with the JIT philosophy Improve the work condition (at the expense of the number of employed) Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 10

11 A video is worth a thousand words Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 11

12 A video is worth a thousand words Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 12

13 A video is worth a thousand words Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 13

14 A video is worth a thousand words Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 14

15 A video is worth a thousand words Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 15

16 Conceptual scheme Machine Software Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 16

17 Story of the Industrial Automation First generation of controllers (1950) In fact built on wired logic (relay, coils, timers, etc ) Very slow in the elaboration Absolutely not flexible Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 17

18 Story of the Industrial Automation Second generation of controllers (1960) Passage to the semi-conductors Increase of the performances Increase of the costs Flexibility still reduced (not programmability) Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 18

19 Story of the Industrial Automation Third generation of controllers (1960) Microprocessor systems Programmability Birth of the PLC (Allen Bradley ) Industrial standard from the mid-70s Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 19

20 Story of the Industrial Automation PLC for everything Centralized control (until the end of the 80s) PLC Output PLC Input PLC Plant Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 20

21 Story of the Industrial Automation First scalable solutions Proprietary real-time networks: field-bus (until the end of the 90s) PLC Output PLC Input PLC Sensors Plant Actuators Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 21

22 Story of the Industrial Automation Integration with the upper level (supervising, ) Real-Time networks based on Ethernet (since 2000) Factory PLC Ethernet Ethernet RT protocol Sensors Plant Actuators Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 22

23 Example of automation architecture (B&R) Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 23

24 Example of automation architecture (Siemens) Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 24

25 Automation Management interaction Plant 1 Plant 2 Ethernet PLC 1 PLC 2 Machine control Factory automation ERP MES SCADA Enterprise Resource Planning Manufacturing Execution System Supervisory Control And Data Acquisition Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 25

26 Automation Management interaction Enterprise Resource Planning It s the corporate information system, it includes: Accounting Management review Management of Human resources Purchases Warehouses Production Distribution / Sales Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 26

27 Automation Management interaction Manufacturing Execution It s a software that allows to: Monitoring Production Orders progress Production times Warehouse inserts Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 27

28 Automation Management interaction SCADA It s the control and monitoring system of the production line (usually called Human Machine Interface). It composes of: Monitoring Line commands Data acquisition Data analysis Alarms management For more info Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 28

29 Automation Plant interaction Conceptual scheme Logic control PLC Programmable Logic Controller Motion planning CNC Computer Numerical Control Modulating control Inverter / Actuators/ Plant Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 29

30 Automation Plant interaction Remarks One PLC can be used for more than one machine and, obviously, more than one PLCs can be used for a machine The CNC functionalities can be integrated in the PLC or in a multi-axes inverter In a lot of cases, the CNC is not used (if the movements don t need to be changed based on the particulat task) It may happen that (particularly in small plants) the modulating control is executed in the PLC Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 30

31 Control types Modulating control Continuous Continuous control actions Continuous dynamic modeling (differential equations, differences) Event driven Logic control Discrete control actions Discrete dynamic modeling (finite state machines, Petri nets) Prof. Corno s course Our course Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 31

32 System example w in t Inputs w in t w out t (not adjustable) A w out t h t Outputs h t Model A dh t dt = w in t w out t Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 32

33 System example Defined h 0 t as the desired height Modulating control Assuming w in t defined in % it is possible to write the equation: w in t = f h t, h 0 t In this case G s = 1 A s, for this reason R s = K p allows to have limited steady-state error Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 33

34 System example Logic control Assuming w in t defined as a two states variable (ON / OFF) it is possible to define two values of h that represent the lower and the upper limits of the water level: h min and h max which the average is h 0 A simple control can be: if (h(t)>=h_max) { w_in(t) = OFF; } if (h(t)<=h_min) { w_in(t) = ON; } Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 34

35 System example Modulating control results 10 w out (t) [m 3 /s] w in (t) [m 3 /s] h(t) [m] Rif. 0 Mis Tempo [s] Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 35

36 System example Logic control results 10 w out (t) [m 3 /s] w in (t) [m 3 /s] h(t) [m] 1 Rif. Mis Tempo [s] Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 36

37 System example Logic control This control can also be represented as a Finite State Machine: h h max 1 w in t = ON 2 w in t = OFF h h min Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 37

38 Finite State Machines A Finite State Machine (with input and output) is a sextuple (U, X, Y, f,, h,, x 0 ), with: U = u 1, u 2, u 3, is the set of the input events X = x 1, x 2, x 3, is the finite state set Y is the finite output set f, : X U X is the transition function h, : X U Y is the output updating function x 0 is the initial state Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 38

39 Finite State Machines Considering the previous example: h h max 1 w in t = ON 2 w in t = OFF h h min U = h h max, h h min X = 1,2 Y = ON, OFF x 0 = 1 Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 39

40 Finite State Machines Considering the previous example: h h max 1 w in t = ON 2 w in t = OFF h h min f, : X U X h h max h h min Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 40

41 Finite State Machines Considering the previous example: h h max 1 w in t = ON 2 w in t = OFF h h min h, : X U Y h h max h h min 1 OFF ON 2 OFF ON Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 41

42 Finite State Machines Remarks It is possible to update the output also during the transition Not all the input allow to evolve the state The tabular and the «graphical» representations are equivalent There are some tools that allow to implement in an easy way the Finite State Machine Simulink Stateflow Scilab Hybrid Automata Module Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 42

43 Finite State Machine example R1 M1 R3 The activities are: Load M1 with R1 Machining Unload M1 with R3 Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 43

44 Finite State Machine example U = Start, R1 finish, M1 finish, R3 finish Y = R1 start, M1 start, R3 start The line starts when Start = 1 and stops the working cycle when R3 finish = 1 Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 44

45 Finite State Machine example 1 Attesa Start == 1 R1 start = 1 2 Caricamento M1 R3 finish == 1 R3 start = 0 R1 finish == 1 M1 start = 1; R1 start = 0 4 Scaricamento M1 M1 finish == 1 R3 start = 1; M1 start = 0 3 Lavorazione Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 45

46 Finite State Machine example Alternative representation Start == 1 R1 finish == 1 M1 finish == 1 R3 finish == 1 Y R1 start = M1 start = 1; R1 start = 0 R3 start = 1; M1 start = R3 start = 0 Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 46

47 Finite State Machine example Remarks In this case h, : X U Y it is not present because the outputs are not changed inside a state (the output is changed only during the transition) We extended f, : X U X as f, : X U X, Y in order to include the output change during the transition N.B.: This discussion is not to be considered rigorous from the mathematical field of view. In our case it is basically a method for the description of how a system works Politecnico di Milano Universidad de Monterrey, July 2015, A. L. Cologni 47