Control of Mechatronic Systems E-Book

Patrick O. J. Kaltjob

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 726

Veröffentlichungsjahr: 2020

Beschreibung

A practical methodology for designing integrated automation control for systems and processes Implementing digital control within mechanical-electronic (mechatronic) systems is essential to respond to the growing demand for high-efficiency machines and processes. In practice, the most efficient digital control often integrates time-driven and event-driven characteristics within a single control scheme. However, most of the current engineering literature on the design of digital control systems presents discrete-time systems and discrete-event systems separately. Control Of Mechatronic Systems: Model-Driven Design And Implementation Guidelines unites the two systems, revisiting the concept of automated control by presenting a unique practical methodology for whole-system integration. With its innovative hybrid approach to the modeling, analysis, and design of control systems, this text provides material for mechatronic engineering and process automation courses, as well as for self-study across engineering disciplines. Real-life design problems and automation case studies help readers transfer theory to practice, whether they are building single machines or large-scale industrial systems. * Presents a novel approach to the integration of discrete-time and discrete-event systems within mechatronic systems and industrial processes * Offers user-friendly self-study units, with worked examples and numerous real-world exercises in each chapter * Covers a range of engineering disciplines and applies to small- and large-scale systems, for broad appeal in research and practice * Provides a firm theoretical foundation allowing readers to comprehend the underlying technologies of mechatronic systems and processes Control Of Mechatronic Systems is an important text for advanced students and professionals of all levels engaged in a broad range of engineering disciplines.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Dedication

Preface

Acknowledgment

About the Companion Website

1 Introduction to the Control of Mechatronic Systems

1.1 Introduction

1.2 Description of Mechatronic Systems

1.3 Generic Controlled Mechatronic System and Instrumentation Components

1.4 Functions and Examples of Controlled Mechatronic Systems and Processes

1.5 Controller Design Integration Steps and Implementation Strategies

Bibliography

2 Physics-Based Systems and Processes: Dynamics Modeling

2.1 Introduction

2.2 Generic Dynamic Modeling Methodology

2.3 Transportation Systems and Processes

2.4 Biomedical Systems and Processes

2.5 Fluidic and Thermal Systems and Processes

2.6 Chemical Processes

2.7 Production Systems and Processes

Bibliography

3 Discrete-Time Modeling and Conversion Methods

3.1 Introduction

3.2 Digital Signal Processing Preliminaries

3.3 Signal Conditioning

3.4 Signal Conversion Technology

3.5 Data Logging and Processing

3.6 Computer Interface and Data Sampling Issues

Bibliography

4 Discrete-Time Analysis Methods

4.1 Introduction

4.2 Analysis Tools of Discrete-Time Systems and Processes

4.3 Discrete-Time Controller Specifications

4.4 Discrete-Time Steady-State Error Analysis

4.5 Stability Test for Discrete-Time Systems

4.6 Performance Indices and System Dynamical Analysis

Bibliography

5 Continuous Digital Controller Design

5.1 Introduction

5.2 Design of Control Algorithms for Continuous Systems and Processes

5.3 Modern Control Topologies

5.4 Induction Motor Controller Design

Bibliography

6 Boolean-Based Modeling and Logic Controller Design

6.1 Introduction

6.2 Generic Boolean-Based Modeling Methodology

6.3 Production Systems

6.4 Biomedical Systems

6.5 Transportation Systems

6.6 Fail-Safe Design and Interlock Issues

Bibliography

7 Hybrid Controller Design

7.1 Introduction

7.2 Requirements for Monitoring and Control of Hybrid Systems

7.3 Design Methodology for Monitoring and Control Systems

7.4 Examples of Hybrid Control and Case Studies

Bibliography

8 Mechatronics Instrumentation: Actuators and Sensors

8.1 Introduction

8.2 Actuators in Mechatronics

8.3 Electromechanical Actuating Systems

8.4 Electro-Fluidic Actuating Systems

8.5 Electrothermal Actuating Systems

8.6 Sensors in Mechatronics

Bibliography

A Stochastic Modeling

A.1 Discrete Process Model State-Space Form

A.2 Auto-Regressive Model with an eXogenous Input: ARX Model Structure

A.3 The Auto-Regressive Model – AR Model Structure

A.4 The Moving Average Model – MA Model Structure

A.5 The Auto-Regressive Moving Average Model – ARMA Model Structure

A.6 The Auto-Regressive Moving Average with eXogenous Input Model – ARMAX Model Structure

A.7 Selection of Model Order and Delay

A.8 Parameter Estimation Methods

A.9 LS Estimation Methods

A.10 RLS Estimation Methods

A.11 Model Validation

A.12 Prediction Error Analysis Methods

A.13 Estimation of Confidence Intervals for Parameters

A.14 Checking for I/O Consistency for Different Models

B Step Response Modeling

C Z-Transform Tables

D Boolean Algebra, Bus Drivers, and Logic Gates

D.1 Some Logic Gates, Flip-Flops, and Drivers

D.2 Other Logic Devices: Drivers and Bus Drivers

D.3 Gated Latch

D.4 D-Type (Delay-Flip-Flop)

D.5 Register or Buffer

D.6 Adder

E Solid-State Devices and Power Electronics

E.1 Power Diodes

E.2 Diode–Transistor Logic (DTL)

E.3 Power Transistors

E.4 Resistor–Transistor Logic (RTL)

E.5 Transistor–Transistor Logic (TTL)

E.6 Metal Oxide Semiconductor FET (MOSFET)

E.7 Thyristors

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Functions and implementation strategies for controlling mechatronic...

Chapter 2

Table 2.1 Generic dynamic modeling procedure of system or process operations.

Table 2.2 Crane gantry system parameters and variables.

Table 2.3 Gantry cranes speed and acceleration operating conditions.

Table 2.4 Some elevator system parameters and model variables.

Table 2.5 HEV system parameters and model variables.

Table 2.6 Some key variables and parameters for the car longitudinal dynamics...

Table 2.7 Some Segway system variables and parameters.

Table 2.8 Some parameters and variables of infant incubator system.

Table 2.9 Some key parameters and variables of glucose and insulin metabolism...

Table 2.10 Typical patient information.

Table 2.11 Patient data.

Table 2.12 Model parameter estimates.

Table 2.13 Mixing tank process variables and parameters.

Table 2.14 Water treatment process variables and parameters.

Table 2.15 Conveyor oven system parameters and variables.

Table 2.16 Some key parameters and variables of the poultry processing system...

Table 2.17 Some key variables and parameters of the distillation process.

Table 2.18 Average crude oil fractioning temperature.

Table 2.19 Some key parameters and variables of the fermentation process.

Table 2.20 Fermentation temperature profile.

Table 2.21 Some key variables and parameters of the drilling machine.

Table 2.22 Some keys variables and parameters for the translation and scrapin...

Table 2.23 Some key parameters and variables of the wind turbine generator sy...

Table 2.24 Variables and parameters of the elevator driven by a permanent mag...

Table 2.25 Some key variables and parameters of the crude oil preheating proc...

Table 2.26 Solar heating process variables and parameters.

Table 2.27 Typical robot handling system parameters and variables.

Table 2.28 Typical operating conditions of a solar-based heating process.

Chapter 3

Table 3.1 Examples of signals equivalence between continuous and discrete dom...

Table 3.2 Table of equivalence based on numerical approximation techniques.

Table 3.3 DC motor speed values for different times.

Table 3.4 General steps for developing discrete-time model of linear time inv...

Table 3.5 Some key variables and parameters values of DC motor.

Table 3.6 Table of parameters used to build various process command input seq...

Table 3.7 Types of transducers and associated signal-conditioning functions r...

Table 3.8 Logic level signals of I

/

O data bus.

Table 3.9 Sampled response in

z

-transform for a first-order process.

Chapter 4

Table 4.1 Generic rules for manually sketching the root locus in the

z

-plane....

Table 4.2 Frequency response data.

Table 4.3 Derived frequency response data.

Table 4.4 Error constants and steady-state errors for various system types an...

Table 4.5 Stability conditions and corresponding root values.

Chapter 5

Table 5.1 Table of discrete values of input-output at different sample period...

Table 5.2 Effects of PID controller components on process dynamics characteri...

Table 5.3 Derived sequence values.

Table 5.4 Sequence values of system with input feedforward.

Table 5.5 Sequence values of system with adjusted input feedforward.

Table 5.6 Typical recorded motion data of a multi-axis robot.

Table 5.7 Some motor drives and their corresponding electric motors.

Table 5.8 PID compensation effects on system response characteristics.

Table 5.9 DC motor parameters.

Table 5.10 Some key parameters and variables of a blood treatment system.

Table 5.11 Sequence values for the PD-based width cutting control of lathe ma...

Table 5.12 With A = 0.0001111

2

steps per adds

Table 5.13 Velocity and position profiles data

Chapter 6

Table 6.1 Step-by-step logic controller design methodology.

Table 6.2 Module component from FT using SADT method.

Table 6.3 Truth table for three inputs and one output.

Table 6.4 Corresponding K-map for three inputs.

Table 6.5 State table based on a Mealy machine.

Table 6.6 Truth table of a starter motor.

Table 6.7 State transition table of a starter motor.

Table 6.8 K-maps of starter motor.

Table 6.9 State table based on the Mealy machine.

Table 6.10 State table based on the Moore machine.

Table 6.11 Equipment involved in the cement pozzolana scratching process.

Table 6.12 Sequence table for the cement pozzolana scratching process.

Table 6.13 Equipment involved in the biopsy operation process.

Table 6.14 Sequence table analysis for the drilling machine process using the...

Table 6.15 Equipment involved in a toxic liquid treatment tank.

Table 6.16 Sequence table analysis for a toxic liquid treatment tank.

Table 6.17 Laser surgery process state transition table.

Table 6.18 Corresponding K-maps of the laser surgery process.

Table 6.19 Laser surgery process state transition table.

Table 6.20 Corresponding K-maps.

Table 6.21 Equipment involved in a three-floor elevator system.

Table 6.22 Sequence table analysis of a two-floor elevator system using switc...

Table 6.23 Listing of input and output devices involved in an automated fruit...

Table 6.24 Binary coding of input transition conditions and fruit-picker syst...

Table 6.25

D

1

Karnaugh table.

Table 6.26

D

0

Karnaugh table.

Table 6.27

Open

output Karnaugh table.

Table 6.28 State transition table with a D-flip-flop gate.

Table 6.29 Truth table when two out of three detectors are checked.

Table 6.30 Truth table when using a virtual disagreement detector.

Table 6.31 Truth table of process outputs.

Table 6.32 K-maps of a motor starter system.

Table 6.33 Truth table of a belt fastening process.

Table 6.34 Truth table of outputs

LAMP

1

,

LAMP

2

, and

LAMP

3

.

Table 6.35 K-maps of a starter motor system.

Table 6.36 Equipment involved in an electric driverless car motion control sy...

Table 6.37 Sequence table of the laser-based cutting process.

Table 6.38 I/O interface addresses for a car engine safety program.

Table 6.39 Sequence state table for a car engine starter.

Table 6.40 Some input and output variables involved in an automated drug cond...

Chapter 7

Table 7.1 Commonly used variables for monitoring software design.

Table 7.2 Listing of typical FAST expected results.

Table 7.3 Process equipment listing.

Table 7.4 System performance audit and specifications of the cement-drying pr...

Table 7.5 Start and stop operating mode of cement-drying process.

Table 7.6 Process equipment listing.

Table 7.7 Process equipment listing.

Table 7.8 Process operating modes and sequences.

Table 7.9 Pasteurization process operating sequence.

Table 7.10 Fermentation process operating sequence.

Chapter 8

Table 8.1 Typical electrical-driven actuating systems.

Table 8.2 Control variables and system parameters of a DC motor.

Table 8.3 Coil location per number of stator phases.

Table 8.4 Dynamical equations for the motion and torque of some mechanical tr...

Table 8.5 Parameters and variables used.

Table 8.6 Dynamics equations governing heat transfer.

Table 8.7 Typical variables and associated sensing methods.

Table 8.8 Some commonly encountered sensors.

Table 8.9 Distance formula using a TOF technique.

Table 8.10 Example LMP sensor characteristics.

Table 8.11 Example of binary codes generated by absolute rotary encoder.

Table 8.12 RFID signal properties and characteristics.

Appendix A

Table A.1 Typical model structure encountered for a system model.

Table A.2 Commonly used statistical criteria for model order validation.

Appendix B

Table B.1 Generic step response model types and corresponding transfer functi...

Appendix C

Table C.1

Z

-transform table.

Table C.2 Delay-included

z

-transform table.

List of Illustrations

Chapter 1

Figure 1.1 Customized 3D food printer.

Figure 1.2 Steam-based power generation technical process schematic.

Figure 1.3 Generic controlled mechatronic systems and instrumentation block ...

Figure 1.4 Relationship between technical process and machine control system...

Figure 1.5 Generic control systems and instrumentation block diagram.

Figure 1.6 Image-guided tele-assisted robot intravascular surgery.

Figure 1.7 (a) Chassis of a driverless vehicle. Source: Based on Kaltjob P. ...

Figure 1.8 Crane-based vertical motion control system schematic.

Figure 1.9 (a) Block diagram of the crane motion feedback control system. (b...

Figure 1.10 Milk-based beverage processing factory schematic.

Figure 1.11 Block diagram with SCADA components for a milk-based beverage pr...

Figure 1.12 Generic controller design steps and dependencies.

Figure 1.13 Overview of activities related to control project management....

Figure 1.14 Egg incubator schematic.

Figure 1.15 Dialysis blood processing system.

Figure 1.16 Guided missile trajectory.

Figure 1.17 Schematic of a crude oil distillation process with its boiler te...

Figure 1.18 Cake conveyor oven.

Figure 1.19 Helicopter schematic.

Figure 1.20 Electric-driven car moving up the hill.

Figure 1.21 Automatic fruit harvesting robot.

Figure 1.22 Schematic of nuclear plant.

Figure 1.23 Incomplete block diagram of a nuclear plant continuous control s...

Chapter 2

Figure 2.1 (a) Schematic of the sea port gantry crane and its components. (b...

Figure 2.2 Typical variation of swaying angle and spreader speed over time....

Figure 2.3 Classical elevator system schematic and its components.

Figure 2.4 Parallel configuration of a hybrid electric vehicle.

Figure 2.5 Vehicle forces schematic.

Figure 2.6 Schematic of a Segway transportation system.

Figure 2.7 Neonatal incubator and its typical components.

Figure 2.8 Glucose-insulin metabolism under a model prediction control parad...

Figure 2.9 Mixing tank system.

Figure 2.10 Water treatment and distribution process schematic.

Figure 2.11 Cake conveyor-oven system.

Figure 2.12 Poultry processing system with ammoniac flow.

Figure 2.13 Distillation tower schematic.

Figure 2.14 Distillation tower.

Figure 2.15 Fermentation tank schematic.

Figure 2.16 Drilling machine system.

Figure 2.17 (a) Pozzolana portal scraper schematic and components. Source: A...

Figure 2.18 Subprocess function block diagrams for the pozzolana portal scra...

Figure 2.19 Wind turbine generator system.

Figure 2.20 Inclined elevator schematic and its components.

Figure 2.21 Furnace-based crude oil heating system.

Figure 2.22 Crude oil preheating and distillation.

Figure 2.23 Schematic of laser-based surgery on human tissue.

Figure 2.24 Equivalent workpiece cutting process with the clamping system.

Figure 2.25 Cross-sectional view of the cutting process with a machine tool....

Figure 2.26 Detail of the plate collector of solar heating system.

Figure 2.27 Solar-based heating system for a barn with two rooms (zones).

Figure 2.28 Drug extractor double-tank process.

Figure 2.29 Gyroscopic aircraft stabilizing system.

Figure 2.30 Robot handling system for gamma radiation-based food sterilizati...

Figure 2.31 Series hybrid diesel-electric powertrain.

Figure 2.32 Vehicle longitudinal motion dynamics.

Figure 2.33 Indoor lettuce farming.

Figure 2.34 Electrically-driven UV.

Figure 2.35 Roll mill two-stand schematic.

Chapter 3

Figure 3.1 Generic digital processing of continuous process signals.

Figure 3.2 (a) Continuous function

x

(

t

)

and output of a generic sampler

xp*(

...

Figure 3.3 Discrete-time ramp-like signal.

Figure 3.4 Discrete integration using backward, forward, and bilinear transf...

Figure 3.5 Continuous-time and discrete-time equivalent step response.

Figure 3.6 Continuous-time and discrete-time equivalent step response for va...

Figure 3.7 (a) System with two samplers. (b) System with a cascaded continuo...

Figure 3.8 Process control with sampler and hold circuits.

Figure 3.9 Time delay effect on signal processing by zero-order hold element...

Figure 3.10 Step signal conversion of sampling of manipulation input.

Figure 3.11 Generic block diagram of process with sampler and hold equivalen...

Figure 3.12 (a) and (b) Decomposition of the discrete third-order process mo...

Figure 3.13 Time-delay effect of triangle (first-order) hold element.

Figure 3.14 Time-delay of effect on continuous signal discrete approximation...

Figure 3.15 Typical generated curves using linear and cubic interpolations....

Figure 3.16 Signal reconstruction using three-spline functions for synchroni...

Figure 3.17 Linear interpolation of DC motor-driven elevator position profil...

Figure 3.18 Equivalent angular parabolic position motion profile.

Figure 3.19 Generic motion profile.

Figure 3.20 Command input trajectories (position, velocity and acceleration)...

Figure 3.21 Equivalent harmonic command trajectory.

Figure 3.22 Example of an 8-bit resistive ladder DAC.

Figure 3.23 Resistive-based ladder of a DAC.

Figure 3.24 Analog-to-digital conversion technique.

Figure 3.25 Successive approximate A/D conversion circuitry.

Figure 3.26 Dual-slope A/D conversion circuitry.

Figure 3.27 Flash A/D conversion circuitry.

Figure 3.28 Delta-encoded ADC block diagram.

Figure 3.29 Computer data and instruction addressing structure.

Figure 3.30 Input/output field devices interfacing with computing unit.

Figure 3.31 Computing unit interface for the LED activation.

Figure 3.32 Pneumatically-driven process with limit switche I/O interface ci...

Figure 3.33 Double switch position interface with debouncing.

Figure 3.34 Generic digital data acquisition and control processing system....

Figure 3.35 Timing structure for the execution of computer control algorithm...

Figure 3.36 Latch-based analog-to-digital signal encoding technique.

Figure 3.37 Pulse counting principle of thermocouple generated signals.

Figure 3.38 Typical signal-based delay effect with D-latch.

Figure 3.39 Typical input impulse signal holding.

Figure 3.40 Time delay in discrete response of first-order process.

Figure 3.41 DC motor block diagram with current and position cascade feedbac...

Figure 3.42 Typical DC motor current, position, and torque responses to unit...

Figure 3.43 Typical response of the current loop for a slower sampling perio...

Figure 3.44 Sampling period effect on sinusoid signal.

Figure 3.45 Motor voltage output signal.

Figure 3.46 Magnetically suspended ball.

Figure 3.47 Water tank with inlet valve computer controlled.

Figure 3.48 Step input to tank filling system.

Figure 3.49 Spacecraft motion with an angle

α

.

Figure 3.50 Command input sequence with intersampling.

Figure 3.51 Command input sequence.

Figure 3.52 Periodic command inputs.

Figure 3.53 Circle curve point-to-point motion.

Figure 3.54 Two-vessel system with pump.

Chapter 4

Figure 4.1 Root locus sketch for desired pole phase and magnitude estimation...

Figure 4.2 Root locus sketch for double poles.

Figure 4.3 Root locations and associated impulse response in the

z

-plane for...

Figure 4.4 Impulse responses for various values of various

ξ

and

ωn

...

Figure 4.5 Magnitude and phase frequency responses using MATLAB.

Figure 4.6 Magnitude and phase frequency responses with PM and GM values.

Figure 4.7 Typical percentage second-order oscillatory response.

Figure 4.8 Complex roots locus phase and magnitude value definition.

Figure 4.9 Block diagram of a DC motor with a lead screw system.

Figure 4.10 Chordal approximation for hole cutting with a different radius....

Figure 4.11 Process block diagram.

Figure 4.12 Process block diagram.

Chapter 5

Figure 5.1 Impulse response for different process model types and with a dea...

Figure 5.2 Frequency response of uncompensated

G

(

ω

)

and compensated clo...

Figure 5.3 System unit step response.

Figure 5.4 Uncompensated step response.

Figure 5.5 (a) Uncompensated step response with

K

p

=0.8689

and

Kd=0.0303

...

Figure 5.6 (a) Uncompensated system response. (b) Compensated system respons...

Figure 5.7 (a) Frequency responses for uncompensated PID with a pole-zero co...

Figure 5.8 An

s

-plane showing root structure.

Figure 5.9 (a) A

z

-plane plot showing the root location for an uncompensated...

Figure 5.10 Step response of a compensated system.

Figure 5.11 Generic command input feedforward block diagram.

Figure 5.12 Modified command input feedforward block diagram.

Figure 5.13 Block diagram of a DC motor with feedforward control.

Figure 5.14 (a) Desired ramp command. (b) Required manipulated input. (c) Ve...

Figure 5.15 (a) Desired ramp command input and position achieved. (b) Requir...

Figure 5.16 Control feedforward of a robot arm.

Figure 5.17 Feedforward control of disturbances.

Figure 5.18 Typical PID-based acceleration, velocity, and position state fee...

Figure 5.19 Classical (industrial) PID motion controller with velocity and p...

Figure 5.20 PID-based state-position cascade control topology for a DC motor...

Figure 5.21 Zero-error tracking state-variable motion controller and command...

Figure 5.22 Block diagram for generic cascade control topology.

Figure 5.23 DC motor model block diagram.

Figure 5.24 (a) Velocity control loop. (b) Current loop.

Figure 5.25 (a) Equivalent input voltage to current, velocity, and position ...

Figure 5.26 Cascade-based control block diagram of a DC motor.

Figure 5.27 Active PID-based state feedback control block diagram of a DC mo...

Figure 5.28 Modified position loop of cascaded control of a DC motor.

Figure 5.29 Average velocity and position-based state feedback control of a ...

Figure 5.30 Modified average velocity and position-based state feedback cont...

Figure 5.31 Position-based PID state feedback control of a DC motor.

Figure 5.32 Modified block diagram of position-based PID state feedback cont...

Figure 5.33 Generic MPC control topology.

Figure 5.34 Generic structure of a stepping motor structure.

Figure 5.35 Initial

P

and

V

register values.

Figure 5.36

P

and

V

register values after four additions.

Figure 5.37 Generic open-loop scalar control of an induction motor.

Figure 5.38 Generic closed-loop scalar control of an induction motor.

Figure 5.39 Basic structure of indirect oriented vector direct control witho...

Figure 5.40 Gamma model of an induction motor.

Figure 5.41 Torque-speed curves for varying

R

a

(

t

),

φ

,

V

a

(

t

)

.

Figure 5.42 Typical thyristor-controlled DC drive.

Figure 5.43 Variation of speed with an external armature resistance.

Figure 5.44 Robot for labeling a box in a supply-chain system.

Figure 5.45 Elevator motion block diagram.

Figure 5.46 (a) Process block diagram. (b) Command input.

Figure 5.47 Automatic vehicle speed control system.

Figure 5.48 Flood tide control gate for (a) a barrier-open position and (b) ...

Figure 5.49 Position system block diagram.

Figure 5.50 (a) Robot arm. (b) Robot arm joint trajectory. (c) Combined robo...

Figure 5.51 Discrete DC motor block diagram.

Figure 5.52 Temperature-based blood treatment system.

Figure 5.53 (a) System block diagram with disturbance. (b) System block diag...

Figure 5.54 Block diagram for control of motor current.

Figure 5.55 Satellite with spin control thrusters.

Figure 5.56 (a) Velocity/position profile of a robot arm. (b) Block for cont...

Figure 5.57 Block diagram of a DC motor first-order model.

Figure 5.58 Lathe machine and laser-based measuring system.

Figure 5.59 Schematic block diagram of a laser-based tool offset controller....

Figure 5.60 Wind turbine control motion.

Figure 5.61 DC motor-amplifier position control.

Figure 5.62 Block diagram of a motor-control system.

Figure 5.63 DC motor state feedback control topology.

Figure 5.64 Oil distillation column and reboiler.

Figure 5.65 Block diagram of control system with a PI controller.

Figure 5.66 Vehicle speed control.

Chapter 6

Figure 6.1 Dependency charts showing a sequence of process events.

Figure 6.2 Logic circuit of motor starter with a D-flip-flop.

Figure 6.3 (a) Mealy state diagram. (b) Mealy state diagram with binary codi...

Figure 6.4 Generic state diagram sketching.

Figure 6.5 A two-state motor starter state diagram.

Figure 6.6 Schematic of the cement pozzolana scratching process.

Figure 6.7 (a) State diagram of the cement pozzolana scratching process. (b)...

Figure 6.8 (a) Robot-assisted biopsy surgery. (b) State diagram of the robo...

Figure 6.9 Laser surgery operating schematic.

Figure 6.10 Mealy state diagram of the laser surgery process.

Figure 6.11 Moore state diagram of the laser surgery process.

Figure 6.12 (a) Three-floor elevator system. (b) State diagram of a two-floo...

Figure 6.13 (a) Robot-assisted fruit picker. (b) Moore state diagram. (c) Eq...

Figure 6.14 Equivalent Mealy state diagram with binary coding.

Figure 6.15 Unmanned vehicle with embedded navigation free collision system....

Figure 6.16 Vehicle anti-braking system.

Figure 6.17 Logic controller circuit for a delayed pump activation of an ant...

Figure 6.18 Battery charging process.

Figure 6.19 Automatic vehicle adaptive front lighting system.

Figure 6.20 Equivalent state diagram for automatic lightning system.

Figure 6.21 (a) Anti-braking system state diagram. (b) Four-state vending ma...

Figure 6.22 Motor-driven pump control circuit.

Figure 6.23 Driverless car system.

Figure 6.24 State diagram for an anti-collision system in a driverless car....

Figure 6.25 Laser-based cutting system.

Figure 6.26 Logic control of system for screw table motion system.

Figure 6.27 Automatic garage gate.

Figure 6.28 Snake robot.

Figure 6.29 State diagram for snake robot motion.

Figure 6.30 Satellite with spin-control thrusters.

Figure 6.31 Relay-based control logic of a car engine starter.

Figure 6.32 Microwave oven system.

Figure 6.33 Mixing tank system.

Figure 6.34 Three-dimensional printing process.

Figure 6.35 Chamber-based indoor vegetable farming.

Chapter 7

Figure 7.1 (a) Example of a P&I diagram process temperature control system. ...

Figure 7.2 FAST decomposition method.

Figure 7.3 FAST decomposition method.

Figure 7.4 Example of a hybrid control schematic.

Figure 7.5 Process start- and stop-mode graphical analysis.

Figure 7.6 Three-floor elevator motion process.

Figure 7.7 (a) Continuous command and control of an elevator lift traction m...

Figure 7.8 FAST decomposition of an elevator motion system.

Figure 7.9 Graphical analysis of start and stop modes for an elevator motion...

Figure 7.10 Simplified schematic and description (P&I diagram) of a bottle-c...

Figure 7.11 Functional analysis using the FAST method.

Figure 7.12 Bottle-washing process operating and stop mode graphical analysi...

Figure 7.13 (a) SFC-based hierarchy of a bottle-washing process. (b) SFC for...

Figure 7.14 Subprocess function block diagrams.

Figure 7.15 (a): Simplified process schematic and description of a cement-dr...

Figure 7.16 Cement dryer control and command schematic.

Figure 7.17 Granulator process schematic.

Figure 7.18 Distillation column schematic.

Figure 7.19 Milk bottle-filling process.

Figure 7.20 To be complete wiring diagram of the hardwired logic control of ...

Figure 7.21

Figure 7.22 Simplified process schematic of a water control system in a hydr...

Figure 7.23 FAST analysis of a water control system in a hydroelectric dam....

Figure 7.24 Schematic of a cement pozzolana scratcher process.

Figure 7.25 Simplified P&I diagram of a beer pasteurization process.

Figure 7.26 Simplified schematic description of the beer fermentation proces...

Figure 7.27 Functional analysis using the FAST method for the beer fermentat...

Chapter 8

Figure 8.1 Sensing and data-acquisition chain.

Figure 8.2 Typical energized and unenergized solenoids.

Figure 8.3 Solenoid-based surface-roughness characterization.

Figure 8.4 Solid state circuit for control of a solar thermal heating system...

Figure 8.5 Schematic of an armature-controlled DC motor.

Figure 8.6 Park transformation.

Figure 8.7 Serial and parallel pump connections.

Figure 8.8 Single-acting, pressured air-powered cylinder.

Figure 8.9 (a) Two-position, three-way directional solenoid actuated control...

Figure 8.10 Typical electrical transducers and their computer interfaces.

Figure 8.11 Ultrasound-based distance measurement operation.

Figure 8.12 Capacitor-based distance measurement principle.

Figure 8.13 Measurement principle using a vision system.

Figure 8.14 LMP electric circuitry for an antenna positioning system.

Figure 8.15 Principle behind an incremental encoder with two tracks.

Figure 8.16 (a) Principle of an incremental encoder with two tracks. (b) Det...

Figure 8.17 Binary signals generated by an incremental encoder.

Figure 8.18 (a) Counting error on encoder channel A. (b) Non-counting interf...

Figure 8.19 Principle of an absolute encoder.

Figure 8.20 (a) Bit-based optical reading of a disk. (b) Binary signal-gener...

Figure 8.21 Linear velocity transducer (LVT) device for a laser-based cuttin...

Figure 8.22 Accelerometer measurement device mounted in a high-speed train....

Figure 8.23 Piezo-electric accelerometer circuit for vertical vibration meas...

Figure 8.24 Quarter bridge strain gauge circuit.

Figure 8.25 Displacement pressure flowmeter with LPG flow rate within a pipe...

Figure 8.26 (a) Pressure measurement principle. (b) Baffle-based pressure me...

Figure 8.27 Capacitance pressure sensor based on a Bellow transducer for a w...

Figure 8.28 Four types of pipe-based fluid flow rate measurement.

Figure 8.29 RFID detection principle. (a) Vacuum cleaner system. (b) Compute...

Figure 8.30 RTD sensor with an A/D data acquisition interface.

Figure 8.31 Electric-driven carts moving uphill.

Figure 8.32 Pneumatic-actuated snake-like robot.

Figure 8.33 Screw-based gear wheel motion for a garage door.

Figure 8.34 Gantry crane schematics.

Figure 8.35 Gantry crane 2D motion profiles.

Appendix E

Figure E.1 Diode schematics and symbol.

Figure E.2

n

-input OR logic gate using a diode resistor.

Figure E.3

n

-input AND logic gate using a diode resistor.

Figure E.4

n

-input NOR logic gate using a diode transistor.

Figure E.5

n

-input NAND logic gate using a diode transistor.

Figure E.6 Power transistor schematics and symbol.

Figure E.7 NOT logic gate using a resistor transistor.

Figure E.8 AND logic gate using a resistor transistor.

Figure E.9 Transistor–transistor logic implementation of a NAND gate.

Figure E.10 Symbols for some electronic devices.

Figure E.11 MOSFET schematic.

Figure E.12 Thyristor schematic and symbol.

Figure E.13 Thyristor switching due to voltage and current variation.

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Control of Mechatronic Systems

Model-Driven Design and Implementation Guidelines

Patrick O.J. Kaltjob

Ecole Nationale Superieure Polytechnique

Yaounde, Cameroun

This edition first published 2019

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Names: Kaltjob, Patrick O. J., author.

Title: Control of mechatronic systems : model-driven design and implementation guidelines / Patrick O. J. Kaltjob.

Description: Hoboken, NJ : John Wiley & Sons, 2020. | Includes bibliographical references and index.

Identifiers: LCCN 2018051541 (print) | LCCN 2019022413 (ebook) | ISBN 9781119505808 (hardcover)

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Preface

The control of mechatronic systems and electrical-driven processes aims to provide tools to ensure their operating performance in terms of productivity, optimization, reliability, safety, continuous operations and even stability. This is usually achieved through hybrid control paradigms using digital or analog tools. Nowadays, digital tools are widely considered to implement control systems as they offer numerous advantages including their ability: (i) to ease the control system implementation; (ii) to design complex and built-in intelligent information processing combining multiple functions for control, fault detection and diagnostic, monitoring and planning decisions; (iii) to integrate logic and continuous control algorithms as well as supervision programs into hybrid control strategies; (iv) to enhance the synchronization of input and output process operations; (v) to coordinate control actions among geographically distributed systems and processes and (iv) to achieve reliable and optimal operating conditions.

The digital control system architecture usually consists of the integration of the following functional units: a data processing and computing unit, an electrical-driven actuating unit, a measuring and detecting unit, a data acquisition (DAQ) and transmitting unit and a signal conditioning unit. The data processing and computing unit can be implemented through devices such as microcontroller (μC), programmable logic controller (PLC) with a control function, digital signal processing (DSP)a and a field-programmable gate array (FPGA).

The design of efficient control systems requires the mathematical modeling of mechatronic systems and process dynamics. This can be achieved in accordance with the operating characteristics (discrete and continuous) and objectives as well as technological constraints of the related instrumentation (signal conversion, transmission, conditioning, measurement, actuation etc.). However, in most of the current engineering literature on the design of digital control systems, the mathematical foundation of discrete time and discrete event systems is usually presented separately from the technological constraints of control instrumentation. For example, the operating time delay models or signal to noise ratio from digital device interfaces are not usually considered. Hence, the theoretical control algorithms proposed have limited practical applicability.

Challenges in the development of a practical design approach for the control of mechatronic systems and electrical-driven processes are: (i) to size and select control instrumentation in accordance with controlled system design objectives; (ii) to develop accordingly the mathematical discrete hybrid model capturing their continuous and discrete event behavioristic characteristics and (iii) to integrate the control systems with respect to technological constraints and operational characterization (discrete and continuous) (e.g. time delays, signal to noise ratios etc.).

This book intends to revisit the design concept for the control of mechatronic systems and electrical-driven processes along with the selection of control instrumentation. By reviewing the theory on discrete-time and discrete event systems as well as various elements of control instrumentation, it offers an integrated approach for: (i) the modeling and the analysis of mechatronic systems dynamics and electrical-driven process operations; (ii) the selection of actuating, sensing and conversion devices and (iii) the design of various controllers for single to multiple function electrical-driven products (mechatronic systems) and processes. Furthermore, it covers some design applications from several engineering disciplines (mechanical, manufacturing, chemical, electrical, computer, biomedical) through real-life digital control system design problems (e.g. a driverless vehicle, newborn incubator, elevator motion) and industrial process control case studies (e.g. a power grid, wind generator, crude oil distillation, brewery bottle filling, beer fermentation).

Through this book, the reader should gain methods for: (i) model formulation, analysis and auditing of single to multiple function electrical-driven products and processes; (ii) model-driven design of software and hardware required for digital control instrumentation; (iii) sizing and selection of electrical-driven actuating systems (including electric motors) along with their commonly used electro-transmission elements and binary actuators; (iv) selection and calibration of devices for process variable measurement and computer interfaces and (v) modeling, operating and integrating a wide variety of sensors and actuators. Hence, the textbook is organized into eight chapters.

Introduction to control of mechatronic systems

.

Chapter 1

gives a brief conceptual definition and classification of mechatronic systems, electrical-driven technical processes and control systems structure. Here, a functional decomposition of the generic control system architecture is presented along with some examples to illustrate control instrumentation for sensing, actuating, computing, signal converting and conditioning. Furthermore, typical functions of generic controlled system for electromechanical product and processes are described along with the interconnection between the control instrumentation. Generic requirements for control systems design are outlined based on challenges to software-based control system integration (design of hybrid architecture) and hardware-based control system integration (instrumentation sizing, compliance and selection). This is summarized within a list of major steps of control design projects.

Physics-based system and process dynamics modeling

.

Chapter 2

presents numerous examples of dynamics modeling for various electrical-driven systems and processes including transportation systems (e.g. a sea port gantry crane, hybrid vehicle, Segway, elevator, driverless car), production systems and processes (e.g. an energy-based wind turbine, drilling machine, cement based pozzolana scratcher), chemical processes (e.g. oil distillation, cake conveyor oven, city water treatment, fermentation, poultry scalding and defeathering), fluidic and thermal systems and processes (mixing tank, purified water distribution, conveyor oven, poultry scalding and defeathering thermal process) or biomedical systems (e.g. infant incubator, human blood glucose insulin metabolism). Systems and process behaviors can be captured through differential equations using an experimental data modeling approach and classical physical laws of conservation and continuity. The resulting models are capable of displaying multiple and nonlinear variables as well as time variant parameter characteristics that can further be simplified according to the system physical properties or operating boundaries. A methodology for physics-based modeling is presented through the deterministic or stochastic behavior models of commonly encountered electrical-driven systems and large-scale processes. A review on linear modeling methods such as stochastic, dynamics response or state space is presented in the Appendices.

Discrete time system modeling and signal conversion methods

.

Chapter 3

focuses on methods to derive discrete approximation of continuous systems and signals using tools, such as the hold equivalent, pole-zero mapping, numerical integration and

z

-transformation theorems. A technological description of computer control architecture and interface is proposed with respect to DAQ unit operations from the bus structure to data gathering, logging and processing with respect to signal noise reduction and approximation consideration. Critical issues related to signal conversion, such as aliasing effects, along with the methodology for selection of sample period are also covered. A selection methodology of the sample period is also outlined. Overall, the chapter topics include technology and methods for continuous signal digital conversion and reconstruction such as bilinear transformation, discrete-time command sequence generation, computer control interface for data logging, conditioning and processing, sample time selection and computer conversion technology using various conversion techniques (i.e. successive approximation, dual slope ADC, delta-encoded ADC, etc.), as well as processing delay effects.

Discrete time analysis methods

.

Chapter 4

presents methods related to discrete system dynamical analysis in the frequency and time domains. Moreover, stability definition and tests for discrete time system are discussed and controlled system performance assessment tools are outlined. This chapter aims to present discrete controller design specifications. Chapter topics include frequency analysis tools such as (DTFT, FFT, DFT), discrete zero and pole location plots, stability tests and criterion for discrete time systems (Jury–Marden test, Routh–Hurwitz), steady-state error, performance indices (ITAE, ISE), time and frequency properties for controller design (settling time, percentage overshoot, gain and phase margins).

Continuous digital controller design

.

Chapter 5

presents various approaches to design the PID controller algorithms, such as continuous time design, discrete design and direct design using roots-locus, and frequency response techniques as well as some advanced techniques, such as model predictive control. Hence, using time or frequency domain controller specifications, numerous examples of designing and tuning control algorithms are described ranging from PID family, deadbeat, feedforward and cascade, to non-interacting control algorithms. In addition to stability analysis tests, performance indices and dynamics response analysis are derived in frequency and time domains. Furthermore, the open loop controller design for stepper motors as well as scalar and vector control design for induction motors are described. Model predictive control algorithms suitable for process operations with physical, safety and performance constraints are also presented. Eventually, comparative analyses between classical PID controllers with various state feedback topologies for DC motor speed control are performed. Overall, chapter topics include cascade control, design and tuning methods for discrete-time classical PID family controllers, scalar and vector control. The digital state feedback controller concept is revisited for cases where it is not possible to measure all state variables. Comparatively, analyses between classical PID controllers and various state feedback topologies for DC motor speed control are presented.

Logic controller design

.

Chapter 6

presents Boolean function-based models that have been derived by using sequential or combinatorial logic-based techniques to capture the relationship between the state outputs of discrete event system operations and the state inputs of their transition conditions. Hence, after performing process description and functional analysis, a design methodology of a logic controller for process operations (discrete event systems) is proposed. Subsequent systems behavioristic formal modeling is achieved by using techniques such as truth table and K-maps, sequence table analysis and switching theory, state diagram (Mealy and Moore) or even state function charts. Some illustrative examples covering key logic controller design steps are presented from process schematics and involved I/O equipment listing, wiring diagrams with some design strategies such as fail-safe design and interlocks, to state transition tables, I/O Boolean function and timing diagrams. Examples of logic controller designs include cases of elevator vertical transportation, an automatic fruit picker, a driverless car and biomedical systems such as robot surgery and laser-based surgery. Overall, the chapter topics cover: (i) the methodology for Boolean algebra based on the modeling of discrete event systems and (ii) logic controller design methodology to derive input/output (I/O) Boolean functions based on truth table and Karnaugh maps, switching theory or state diagrams, wiring and electrical diagrams and P&I and PF diagrams.

Hybrid process controller design

.

Chapter 7

presents a generic design and implementation methodology for process monitoring and control strategies (logic and continuous) with algorithms to ensure operations safety of hybrid systems (i.e. systems integrating discrete event and discrete time characteristics). First, functional and operational process requirements are outlined to define hybrid control and supervision systems with respect to logic and continuous control software and data integration and process data gathering as well as multi-functional process data analysis and reporting. Subsequently, a design methodology is proposed for the design of monitoring and control systems. Some cases are used to illustrate the design of process monitoring and hybrid control for elevator motion, drying cement pozzolana and a brewery bottle washing process. Overall, chapter topics include hybrid control system design, piping and instrumentation diagram, system operations FAST and SADT decomposition methods, process start and stop operating mode graphical analysis and a

sequential functional chart

(

SFC

) as well as process interlock design.

Instrumentation modeling: sensors, detectors and electrical-driven actuators

.

Chapter 8

provides an overview of electrical-driven actuators models and sensors encountered in mechatronics with their technical specifications and performance requirements. This is suitable for electric motors, electrofluidic and electrothermal actuating systems. Similarly, binary actuators such as electroactive polymers, piezo-actuators, shape alloys, solenoids and even nano devices are technically described and modeled. In addition,

Chapter 8

describes a spectrum of digital and analog sensing and detecting methods as well as the technical characterization and physical operating principles of the instrumentation commonly encountered in mechatronic systems. Among sensors presented, there are motion sensors (position, distance, velocity, flow and acceleration), force sensors, pressure or torque sensors (contact-free and contact) temperature sensors and detectors, proximity sensors, light sensors and smart sensors, capacitive proximity, pressure switches and vacuum switches, RFID-based tracking devices and electromechanical contact switches. In addition, some smart sensing instrumentation based on electrostatic, piezo-resistive, piezo-electric and electromagnetic sensing principles are presented. Overall, chapter topics include actuating systems such as motors (AC, DC and stepper), belt, screw-wheels, pumps, heaters and valves along with detection and measurement devices of process variables (force, speed, position, temperature, pressure, gas and liquid chemical content), RFID detection, sensor characteristics (resolution, accuracy, range etc.) and nano as well as smart sensors.

This textbook emphases on the modeling and analysis of real-life environment and the integration of control design and instrumentation components of mechatronic systems through a suitable selection and tuning of actuating, sensing, transmitting and computing or controlling units. Indeed, this book covers control instrumentation such as sensors, transducers and actuators as well as aspects of matching and interconnecting these control instruments, particularly the interface between connected devices and signal conversion, modification and conditioning. As such, the reader is expected at the conclusion of this textbook to have fully mastered: (i) the design requirements and the design methodology for control systems; (ii) the sizing and selection of the instrumentation involved in industrial process control as well as microelectromechanical devices and smart sensors; (iii) the use of microprocessors for process control, as well as signal conditioning and (iv) the sizing and the selection of actuating equipment for industrial processes. Numerous examples and case studies are used to illustrate formal modeling, hybrid controller design and the selection of instrumentation for electrical-driven machine actuation and DAQ related to systems dynamics and process operations. Through these case studies, the reader should gain practical understanding of topics related to the control system and instrumentation allowing him/her to fulfill a control and instrument engineering position where he/she is expected: (i) to possess a good knowledge of instrumentation operating conditions and control requirements; (ii) to size and select control instrumentation; (iii) to design, develop and implement digital controllers; (iv) to design engineering processes and electrical-driven systems; (v) to collaborate with design engineers and process engineers and technicians for the cost- and time-based acquisition of systems and processes control equipment and (vi) to perform technical audit to ensure instruments compliance with health and safety regulations.

This book is conceived to develop the reader's skills for engineering-based problem solving, engineering system design, critical analysis and implementation of control systems and instrumentation. It allows self-study via comprehensive and straightforward step-by-step modular procedures. In addition, examples with their accompanying MATLAB® routines, as well as design and selection related exercises and problems, are provided with their solutions. Furthermore, a dedicated textbook companion website allows the reader to download additional material for teaching, such as slide presentations of the chapter material, data files for additional laboratory sessions, example files as well as 2D and 3D innovative virtual labs of physical real-life systems (i.e. model-based simulation tools that could be associated to real life system for in-class lab sessions).

Suggestions for a teaching plan for applied control theory of mechatronic systems and electrical-driven processes would be as follows: (i) Chapter 1 through Chapter 5 (up to Section 5.3.1) for an introductory digital control level course during a semester; (ii) Chapters 2, 3 and 5 (Sections 5.3 and 5.4) for advanced control students with a control theory background; (iii) Chapters 1, 3 (Sections 3.3 and 3.4) and 8 for electric-driven machine and instrumentation students with computer hardware and software programming experience; (iv) Chapters 2, 3 (Sections 3.3 and 3.4), 5 (Sections 5.2.4, 5.3 and 5.4) and 6–8 for field control and instrumentation engineers interested in the design or the migration of process control of hybrid systems.

Acknowledgment

This book makes extensive use of MATLAB® routines, distributed by Mathworks, Inc. A user with a current MATLAB license can download trial products from their website. Someone without a MATLAB license can fill out a request form on the site, and a sales rep will arrange the trial for them. For additional MATLAB product information, please contact:

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About the Companion Website

This book is accompanied by a companion website which aims to support the teaching efforts of instructors through:

(email author at [email protected] to have FREE access to the secured website)

The website includes:

Lectures material for following courses package:

Digital control systems

Instrumentation: sizing and selection sensors and actuators

Mechatronic systems design

Process automation and monitoring

Advanced control systems: predictive, distributed, adaptive control strategies

Electric motor/machine control: stepper, DC, AC/induction

Control and instrumentation

For each course listed above reading guides, other classroom resources (visual summary, course outlines/summary, animation slides);

For each lecture session, multiple choice questions, for each course sample exams;

for each Textbook chapter, solution manual, study questions, flash cards;

Solved real-life problems and projects, 2D and 3D applications for sessions of laboratory simulation.

1Introduction to the Control of Mechatronic Systems

1.1 Introduction

The rapid expansion of automated electrically-driven systems (e.g. electromechanical machines) is related to the development of digital control strategies in order to enhance their performance and extend their functionality while significantly reducing their operating cost and complexity. However, those digital control strategies are dependent on the performance of the control instrumentation related to measurement, signal conditioning, actuating, and digital control technologies. Recent technology advancements offer a plethora of control systems instrumentation, each with design-specific requirements and compliance constraints. Hence, in addition to system modeling, the design of digital control strategies has to consider: (i) the selection of control instrumentation in accordance with performance objectives; and (ii) the integration of the control systems instrumentation and process equipment with respect to operating constraints.

Consequently, it is suitable to lay out a generic design procedure for digital control systems, especially in: (i) controlling electrically-driven systems; (ii) sizing and selecting control instrumentation related to information processing and computing, electrically-driven actuation, process sensing and data acquisition; (iii) integrating those control instrumentation with respect to controlled system performance objectives and operating constraints; and (iv) integrating multifunctional control applications.

In this chapter, the definition and classification of electrically-driven systems and technical processes are presented first. Then the functional relationship between electromechanical machine control and control within interconnected and synchronized electromechanical systems is outlined. Various components of control systems instrumentation are described along with their design requirements. Furthermore, major steps of control system migration projects are presented with some illustrative examples of industrial process control. Finally, key project management steps and the associated subsequent design documents are listed.

1.2 Description of Mechatronic Systems

Mechatronic systems are either electrically-driven products or technical processes. Electrically-driven products are machines transforming current, voltage, or other electrical power into mechanical, fluidic, pneumatic, hydraulic, thermal, or chemical power. Hence, those systems can be classified according to their functional objectives either as: (i) specialized machines performing specific operations; or (ii) multipurpose and adjustable machines. Control systems are a set of technologies enabling algorithmic computing or signal processing devices to use signals emitted from analog or digital detecting, sensing, and communicating devices in order to perform automatic operations of systems or process actuation. Such systems are expected to perform them routinely and independently of human intervention with a performance superior to manual operation.

Thus, control systems aim to provide the necessary input signals to achieve the desired patterns of variations of specific process variables. Therefore, the functions of control systems are embedded in electromechanical systems (machine or product control).

Example 1.1

Figure 1.1 shows a typical 3D printing robot for customized cooking with speed- and temperature-controlled system which could be combined with monitoring indicators for cooking time and cooking stage, as well as a control panel allowing the selection of the final mixing of the product and cooking program. This system would require:

the angular position control of a pressure valve delivering semi-liquefied food (paste), the

x-y

axis position control of the carriage driving the extruder head (nozzle) made of two motors with a screw mechanism, the table angular speed and the

z

-axis position control;

the heater temperature control (nozzle level);

the remote pressure and force control for the valve in charge of injecting pressured food paste feed based on environmental (e.g. space mission) and biological conditions (e.g. lower gravity forces); and

the logic control for the discrete selection of ingredients.

Figure 1.1 Customized 3D food printer.

Such control design combination enhances the product or machine functionality while reducing operating and maintenance costs. This is done by integrating data processing and computing operations within a field device or machine (e.g. washing machine, navigation systems etc.). Among the commonly encountered automated machines or products are those with: (i) embedded control functions; (ii) dedicated control functions; or (iii) a control function limited to a couple of sensors and actuators involved.

A technical process is the sum of all interacting machines within that process transforming and/or storing material, energy, or information. Such technical processes can be classified according to their operational objectives as follows:

Transportation-related processes, such as material handling processes, energy flow processes, and information transmission processes.

Transformation-related processes, such as chemical processes, manufacturing processes, power generation, and storage processes.

Technical processes can be characterized according to functional objectives, such as:

Processes characterized by a continuous flow of material or energy (e.g. cement drying process, electric power distribution, paper production). Here, the process variables are physically-related variables with a continuous range of values, such as temperatures in a heating system. The process parameters are physical properties (e.g. power transmission network impedance, liquefied gas density). Process control consists of maintaining the process state on a determined level or trajectory. In this case, process dynamics models can be obtained through differential equations.