Introduction_to_Mechatronics_2(basics).pdf

Introduction to Mechatronics
Tewedage Sileshi

Outline
● What is Mechatronics?
● Development of Mechatronics
● Why Study Mechatronics?
● Core Concepts of Mechatronics
● Mechatronics Systems
● Design process
● Applications of Mechatronics
● Challenges in Mechatronics
● Future Trends in Mechatronics

What is Mechatronics?
● Definition: Mechatronics is a multidisciplinary field that combines
elements of mechanical engineering, electrical engineering, control
engineering, computer science, and systems engineering.
● It is an interdisciplinary field of engineering that deals with the design of
products whose function relies on the integration of mechanical, electrical,
and electronic components connected by a control scheme.
● Goal: To design and develop systems that integrate mechanical, electrical,
and computer components to achieve a specific task or function.
● Application of complex decision making to the operation of physical
systems
● Methodology used for the optimal design of electromechanical products.

Cont’d
● The word, mechatronics is composed of mecha from mechanics
and tronics from electronics. In other words, technologies and
developed products will be incorporating electronics more and
more into mechanisms, intimately and organically, making it
impossible to tell where one ends and the other begins

Development of Mechatronics
Early Developments
● Ancient
Civilizations: Simple
machines (levers,
pulleys, gears)
● Industrial
Revolution: Steam
engines, power looms
● Electrical Era:
Electric motors,
automation
Key Milestones:
● Industrial Robots
● Automotive
Electronics
● Consumer
Electronics
● Medical Devices
20th Century Advancements
● Servomechanisms:
Feedback control
● Computers:
Computational power
● Robotics: Industrial
robots
● Mechatronics Coined:
Japan, 1970s

Why study Mechatronics ?
● Interdisciplinary Approach: Broad knowledge base, problem-
solving skills, innovation
● Increased Efficiency and Automation: Optimized systems,
automation, cost reduction
● Innovation and Product Development: New products,
product improvement, competitive advantage
● Career Opportunities: Growing demand, diverse industries,
career advancement

Core Concepts of Mechatronics
● Mechanical Systems
● Electrical Systems
● Control Systems
● Computer Systems
● Systems Integration

Mechanical Systems
● Kinematics: Study of motion without considering the forces causing it. This
includes topics like position, velocity, acceleration, and displacement.
● Dynamics: Study of motion considering the forces causing it. This involves
topics like Newton's laws of motion, force analysis, torque, and
momentum.
● Materials: Understanding the properties and behavior of materials used in
mechatronic systems, such as strength, stiffness, and durability.
● Design: Designing mechanical components like gears, linkages, and
structures to meet specific requirements.

Electrical Systems
● Circuits: Understanding electrical circuits, including components
like resistors, capacitors, inductors, and their relationships.
● Sensors: Knowledge of various sensors used in mechatronics,
such as position sensors, temperature sensors, force sensors, and
their principles of operation.
● Actuators: Understanding different types of actuators (e.g.,
motors, solenoids, pneumatic cylinders) and their applications.
● Power Electronics: Knowledge of power conversion techniques,
including DC-DC converters, inverters, and rectifiers.

Control Systems
● Feedback Control: Understanding the concept of feedback
control, where the output of a system is measured and used
to adjust the input to achieve a desired output.
● System Modeling: Creating mathematical models to
represent the behavior of mechatronic systems.
● Controller Design: Designing controllers (e.g., PID, state-
space) to achieve desired system performance.
● Stability Analysis: Analyzing the stability of control systems
to ensure they operate reliably.

Computer Systems
● Microcontrollers: Understanding the architecture, programming,
and applications of microcontrollers in mechatronics.
● Programming: Proficiency in programming languages (e.g., C,
C++, Python) for controlling mechatronic systems.
● Computer-Aided Design (CAD): Using CAD software to design
mechanical and electrical components.
● Embedded Systems: Understanding the principles of embedded
systems and their integration into mechatronic devices.

Systems integration
● Interfacing: Connecting mechanical, electrical, and computer
components to create a cohesive system.
● Communication: Understanding communication protocols (e.g.,
CAN bus, RS-232) for data exchange between components.
● System Optimization: Optimizing the overall performance of the
mechatronic system by balancing the contributions of mechanical,
electrical, and computer components.
● Troubleshooting: Identifying and resolving issues that may arise
in mechatronic systems.

Mechatronics systems
● A mechatronic device is one that is able to
perceive the surrounding environment, make
appropriate decisions based on that information,
and execute those decisions (take action)

Components of mechatronics systems
● Sensors: Detect physical changes (e.g.,
temperature, motion, pressure).
● Actuators: Convert energy into motion (e.g.,
motors, hydraulic systems).
● Control Systems: Decision-making based on sensor
inputs (e.g., feedback loops).
● Microprocessors/Microcontrollers: The brain of the
system, processing inputs and controlling outputs.

Cont’d
1. Develop a high-level diagram of the necessary
system components

Cont’d
2. Specify each of these elements in greater detail:
Select specific components for each. This stage
requires:
● knowledge of the necessary requirements for
each block
● knowledge of various Commercial-off-the-shelf
(COTS) components that are available
● Input and output requirements for each

Cont’d
3. At this stage in the design process, interaction of
the various components should be considered. This
consideration should include:
● Input and output types
○ Analog/DC
○ Incremental, absolute
● Power requirements
● Impedance
● Signal power

Cont’d
4. As the design process progresses, the behavior of
the system should be modeled for analysis and
simulation purposes. This can be used to:
● Design controller
● Determine motor requirements
● Determine power requirements
● Evaluate system performance
● Determine driver requirements

Cont’d
5. Complete the design of the elements in the system
(mechanical, electrical, controller code). Update
the system framework as necessary
6. Develop a system prototype for testing and
evaluation purposes.

Applications of mechatronics
● Robotics
● Automotive Industry
● Aerospace Industry
● Manufacturing
● Consumer Electronics
● Medical Devices

Mechatronics in everyday life
● Home automation: washing machines, electric
water heaters, automated lighting systems.
● Automobiles: ABS, automatic braking systems,
adaptive cruise control.
● Healthcare: surgical robots, prosthetics.
● Manufacturing: industrial robots, CNC machines.

Challenges of mechatronics
● Complexity: Multiple domains, nonlinear dynamics,
uncertainty
● Integration: Compatibility, interfacing, optimization
● Cost: Initial investment, maintenance, cost-benefit analysis
● Reliability: Failure points, redundancy, fault tolerance
● Safety: Risk assessment, standards, human factors

Future trends of mechatronics
Trends:
● Autonomous systems and robotics.
● Artificial intelligence in mechatronics systems.
● Advances in sensors and computing power.
● Internet of Things (IoT) integration.
Opportunities:
Continuous innovations in healthcare, aerospace, and
consumer electronics.

System modeling
Tewedage Sileshi

Outline
● Introduction to system modeling
● Types of systems
● Types models
● Mapping models to systems
● Model Validation and Simulation
● Model Reduction and Simplification

Introduction to System Modeling
● A simplified representation of a real-world system.
● Used to analyze, predict, and control system behavior.
● Understanding complex interactions between mechanical, electrical, and
control components.
● Designing and optimizing mechatronic systems; troubleshooting and
maintaining mechatronic systems.
● Different models present the system from different perspectives
○ External perspective showing the system’s context or environment;
○ Behavioural perspective showing the behaviour of the system;
○ Structural perspective showing the system or data architecture.

Types of systems
1. Mechanical system
2. Electrical system
3. Fluid power system
4. Control/embedded system
5. Electromechanical system
6. Human machine system

Mechanical systems
● Mechanical systems involve physical components that interact through forces
and movements. These systems focus on energy transfer through motion and
mechanical structures.
Key Components:
● Gears, levers, pulleys, springs
● Linkages and joints
● Bearings and shafts
Examples:
● Robotic Arms: The mechanical structure allows the arm to move and perform
tasks.
● Conveyor Systems: Transporting materials in manufacturing lines using
mechanical rollers.
Key Focus: Motion, forces, energy transfer, stress, and strain.

Electrical systems
● Electrical systems are made up of components that generate, transmit, or
manipulate electrical energy. They are essential for powering mechatronic
systems and processing signals.
Key Components:
● Power sources (batteries, power supplies)
● Sensors (temperature, proximity, light)
● Actuators (motors, solenoids)
● Circuit elements (resistors, capacitors, transistors)
Examples:
● Motors and Drives: Used to convert electrical energy into mechanical motion in
systems like robotic arms.
● Sensor Networks: Collect and send data for processing, such as temperature or
motion sensors in automation systems.
Key Focus: Voltage, current, resistance, signal processing, and control logic.

Fluid power system
● Fluid power systems use liquids (hydraulics) or gases (pneumatics) to generate,
control, and transmit power. These systems are common in applications
requiring high force or precise control.
Key Components:
● Hydraulic pumps, pneumatic compressors
● Valves, cylinders, actuators
● Pipes, hoses, reservoirs
Examples:
● Hydraulic Press: Uses hydraulic fluid to exert a large force for shaping materials.
● Pneumatic Systems: Used in factory automation for powering robotic grippers or
moving materials.
Key Focus: Pressure, flow rate, fluid dynamics, force generation.

Control/embedded system
● Control systems manage the operation of other systems by processing input
signals and generating appropriate outputs. Embedded systems are specialized
computing systems that control devices.
Key Components:
● Microcontrollers (e.g., Arduino, Raspberry Pi)
● Sensors and actuators
● Feedback control loops (e.g., PID controllers)
Examples:
● Programmable Logic Controllers (PLCs): Used in industrial automation to
manage processes like assembly line operations.
● Robotic Controllers: Embedded systems that handle real-time motion and
sensor data to guide a robot's actions.
Key Focus: Feedback control, signal processing, real-time decision-making, software-
hardware integration.

Electromechanical system
● Electromechanical systems combine mechanical and electrical components to
achieve specific movements or tasks. They are fundamental in robotics and
automation systems where electrical signals control mechanical motion.
Key Components:
● Motors (stepper, DC, servo)
● Actuators (linear, rotary)
● Sensors and control systems
Examples:
● Servo Motors: Used in CNC machines and robots for precise positioning.
● Electric Drives: Convert electrical energy to control mechanical systems in
devices like elevators or automated gates.
Key Focus: Interaction between electrical inputs and mechanical outputs, power
conversion, and precision control.

Human machine system
● Human-machine systems (HMI - Human Machine Interface) involve interactions
between a human operator and a machine. These systems focus on designing
interfaces that allow humans to control and interact with machines efficiently and
safely.
Key Components:
● Sensors for human input (e.g., touch screens, buttons)
● Feedback devices (e.g., displays, haptic feedback)
● Control algorithms to interpret human actions
Examples:
● Flight Simulators: Provide pilots with a virtual interface to practice flight scenarios.
● Control Panels in Factories: Allow human operators to oversee and manage
automated machinery.
Key Focus: Ergonomics, usability, safety, efficient interaction between human and machine.

Complex systems vs. simple systems
Criteria Simple systems Complex systems
Task requirments Single, well-defined task (e.g.,
moving an object, simple control of
motors).
Multiple or interdependent tasks (e.g.,
coordination between sensors, actuators,
and control systems).
Number of components Few components, straightforward
interactions.
Many subsystems, high integration across
mechanical, electrical, and software
components.
Control complexity Basic control strategies (open-loop,
PID, on/off control).
Advanced control methods (adaptive,
nonlinear, or model-predictive control,
real-time adjustments).
Predictability Predictable environment, limited
external factors affecting system
behavior.
Dynamic or unpredictable environments;
non-linear or complex input-output
relationships.
Adaptability/Flexibility Limited flexibility; designed for
specific tasks with minimal
adjustments.
High flexibility; can adapt to various tasks
or environmental changes (e.g.,
autonomous robots).

Cont’d
Time to develop Shorter development time, simpler
design and implementation.
Longer development cycle due to
complexity and higher design and testing
demands.
Cost Lower development and operational
costs.
Higher initial cost due to advanced
components and integration, but long-term
benefits.
Resource constraints Few resources are needed for
design, testing, and maintenance.
It requires more resources for design,
simulation, and system validation.
Scalability Limited scalability; hard to modify or
expand without major redesign.
Easily scalable; designed to incorporate
future upgrades and additional
functionality.
Example Dc motor control, basic conveyor
belt system.
Autonomous drones, industrial robots with
vision systems.
When to use When task requirements are simple,
cost and time constraints are tight,
and minimal adaptability is needed.
When the system must handle complex
tasks, interact with various subsystems,
and adapt to changes in real-time.

Types of models
● Physical model: tangible representations of real-world
systems. For understanding, testing, or experimentation.
● Mathematical model: use of equations and formulas to
describe system behavior.
● Computational/simulation models: use computer programs
to simulate system behavior.
● Prototype models: Physical or virtual representations of a
system
● Hybrid models combine elements from different modeling
types.

Physical models
● Context Models
● Process Models
● Behavioral Models
● Object Models
● Hardware Models

Context model
● Provide a high-level overview of a system, its environment, and its interactions
with external entities.
● It focuses on the system's boundaries and interactions with external entities
rather than internal details.
● The level of detail in the context model can vary depending on the purpose of
the analysis.
● The context model can be used to identify potential risks, dependencies, and
interfaces within the system.
● The context model can serve as a foundation for developing more detailed
system models, such as data flow diagrams or use case diagrams.

Process model
● A process model provides a more detailed view of the internal
workings of a system, focusing on the sequence of activities
and the flow of data.
● It shows the overall process and the processes that are
supported by the system.
● Data flow models may be used to show the processes and the
flow of information from one process to another.

Behavioral model
● Behavioral models focus on the dynamic aspects of a system, such as its interactions, states,
and transitions.
Data Processing Models
● Focus: The what of the system, showing how data is transformed and moved.
● Techniques:
○ Data flow diagrams (DFDs): Visualize the flow of data between system components.
○ Entity-relationship diagrams (ERDs): Represent the relationships between data
entities.
○ Process flow diagrams: Show the sequence of steps involved in data processing.
State Machine Models
● Focus: The how of the system, showing how it responds to events and changes its state.
● Techniques:
○ State diagrams: Depict the possible states of a system and the transitions between
them.
○ Finite-state machines: Formal mathematical models used to describe systems with a
finite number of states.
○ UML statecharts: A more expressive notation for state machines, including hierarchical
states and concurrency.

Sequence and state model of ATM

Object model
● Object models are a fundamental component of object-oriented analysis and design
(OOAD). They provide a visual representation of the structure and relationships
between objects in a system.
Key elements of object models:
● Classes: Templates or blueprints for objects, defining their attributes (data) and
methods (behavior).
● Objects: Instances of classes, representing specific entities within the system.
● Attributes: Data members that describe the properties of an object.
● Methods: Functions or procedures that define the behavior of an object.
● Relationships: Connections between objects, such as inheritance, association, and
aggregation.
Common types of object models:
● Class diagrams: Depict the classes in a system and their relationships.
● Object diagrams: Show specific instances of classes and their relationships at a
particular point in time.
● Sequence diagrams: Illustrate the interactions between objects over time.
● Collaboration diagrams: Focus on the relationships between objects and the
messages they exchange

Hardware models
● Prototypes: Physical representations of a product or system,
often built to test functionality and design concepts.
● Breadboards: Temporary platforms for connecting electronic
components to test circuits and prototypes.
● Test rigs: Specialized setups used to simulate specific operating
conditions and test the performance of mechatronic systems.

Mathematical models
● A set of mathematical equations (e.g., differential equations) that
describes the input-output behavior of a system.
● What is a model used for?
○ Simulation
○ Prediction/Forecasting
○ Prognostics/Diagnostics
○ Design/Performance Evaluation
○ Control System Design

Types of mathematical models
Types of models:
● Static Models: Do not involve time-dependent behavior (e.g., force-balance
equations for stationary structures).
● Dynamic Models: Include time-dependent behavior and typically involve
differential equations (e.g., modeling an electric circuit's voltage over time).
Mathematical Representations:
● Algebraic Equations: For steady-state or static systems
● Differential Equations: For systems with changing variables over time, like in
control and dynamic analysis.

Block diagrams
● A block diagram is a graphical representation of a system that uses blocks
to represent different components or functions, with arrows indicating the
flow of signals.
● Each block is essentially a function: An isolated “free” part of the system
● Connections represent input/output relations... an “information flow.”

Steps to Create a Functional Block Diagram
1. Define the System and Objectives
2. Identify Inputs and Outputs
3. Break Down System Functions
4. Establish Relationships Between Blocks
5. Label and Define Each Block and Signal Path
6. Add Functional Details (If Necessary)
7. Validate the Diagram

Example of FBD
Objective: Motion Detecting Lamp
Inputs and Outputs: motion and light, respectively

Transfer functions
● A transfer function is a mathematical representation of a
system's input-output relationship in the Laplace domain. It is
a powerful tool for analyzing and designing linear, time-
invariant (LTI) systems.
Importance of Transfer Functions
● System Analysis: Helps in understanding system behavior,
stability, and dynamic response.
● Designing Control Systems: Crucial for designing controllers
like PID by analyzing how the system responds to inputs
across different frequencies.

Example: Mass-Spring-Damper System
=

Mathematical Building Blocks for System Modeling
Integrator (1/s):
● Represents accumulation or storage of a quantity over time (e.g., position as the
integral of velocity).
● Widely used to model dynamic systems like velocity from acceleration.
Differentiator (s):
● Represents the rate of change of a quantity (e.g., velocity is the derivative of position).
● Useful in control systems for predicting future states, though it can amplify noise.
Gain (K):
● A constant multiplier that scales the input signal.
● Used to adjust system response, like increasing the strength of a signal in a feedback
loop.
Summing Point (+/-):
● Adds or subtracts signals, often used in feedback loops to compare actual vs. desired
values.
● Crucial for error calculation in control systems.

Computational/simulation models
What are computational models?
● Mathematical representations of physical systems created to simulate and
analyze behavior without physical testing.
● Useful for analyzing complex systems, especially those with multiple
interacting components (e.g., mechanical, electrical).
Purpose of simulation in Mechatronics
● Allows engineers to study system performance, optimize design, and
troubleshoot without costly or time-consuming prototypes.
Benefits of Using Simulation Models
● Cost Efficiency: Reduces the need for physical prototypes.
● Risk Reduction: Minimizes the risk by predicting potential issues.
● Insight into Dynamics: Offers insights into system dynamics, stability, and
performance in real-world scenarios.

Types of Computational Models in
Mechatronics
1. Finite Element Analysis (FEA)
● Used for stress, strain, and thermal analysis in mechanical components.
● Essential in structural analysis to optimize materials and shapes.
1. Multibody Dynamic Models (MBD)
● Analyzes motion and forces within interconnected rigid or flexible bodies.
● Ideal for simulating the movement of mechanical systems like robotic
arms.
1. Control System Models
● Transfer functions and state-space models used in software to simulate
control loops.
● Useful for tuning and testing controllers without a physical system.

Cont’d
4. Thermal and Fluid Models
● Simulates thermal behavior and fluid flow using computational fluid
dynamics (CFD).
● Important for systems that involve heat transfer or fluid flow, such as
cooling systems.
5. Electromechanical Models
● Combines electrical and mechanical dynamics, often seen in motor control
and actuators.
● Useful in simulating electric machines, power electronics, and sensor
integration.

Simulation Software in Mechatronics
Commonly Used Tools
● MATLAB/Simulink: Widely used for control system modeling, simulations, and
algorithm development.
● ANSYS & Abaqus: Popular for FEA and thermal analysis.
● COMSOL Multiphysics: Useful for coupled physics simulations (thermal,
structural, fluid).
● SolidWorks and CATIA: Provide built-in simulation features for design and
mechanical analysis.
Practical Application
● Example: Simulating a DC motor speed control system in MATLAB/Simulink to
optimize the PID parameters.
● Another Example: Using FEA in ANSYS to optimize a robotic arm structure for
maximum load capacity with minimal material.

Advantages and Limitations of Computational
Models
Advantages
● Rapid Prototyping: Quickly iterate and test designs.
● Detailed Analysis: Provides deeper insight into complex interactions.
● Scalability: Easily adjust and extend models for different applications.
Limitations
● Model Accuracy: Depends on the assumptions made and input data quality.
● Computational Costs: Some simulations require extensive computing power
and time.
● Simplifications: Some physical complexities may be simplified, affecting
precision.

Prototype models
Definition of Prototype Models
● Prototype Models: Physical or virtual representations of a system built to
test and validate its design and functionality.
● Purpose in Mechatronics: Allows engineers to observe system behavior,
identify issues, and refine designs before moving to full-scale production.
Importance of Prototyping in System Modeling
● Risk Reduction: Helps detect design flaws early, reducing costly rework
in later stages.
● Hands-on Testing: Validates theoretical models under real-world
conditions, verifying assumptions made during system modeling.

Types of Prototype Models
Physical Prototypes
● Description: Tangible models of the system or components, often at a smaller
scale.
● Purpose: Used for functional testing, durability analysis, and ergonomic
assessments.
● Examples: 3D-printed parts for mechanical systems, PCB prototypes for
electrical circuits.
Virtual Prototypes
● Description: Digital representations of the system using CAD software or
simulation tools.
● Purpose: Used to test dynamics, simulate interactions, and assess performance
in a virtual environment before physical build.
● Examples: CAD models, Finite Element Analysis (FEA), and dynamic
simulations in MATLAB/Simulink.

Mapping models to systems
System Type Physical Models Mathematical Models
Computational/Simula
tion Models
Prototype Models
Mechanical
Scale models,
prototypes, test rigs
Equations of motion,
kinematic analysis,
structural analysis
FEA, multibody
dynamics simulations
Physical prototypes of
components or
subsystems
Electrical
Circuit boards,
breadboards, wiring
diagrams
Circuit analysis,
electromagnetic field
analysis
Circuit simulation
(SPICE),
electromagnetic field
simulations (FEM)
Electronic prototypes,
PCB prototypes
Fluid Power
Hydraulic or pneumatic
test rigs, flow benches
Fluid dynamics
equations, control
system models
CFD simulations,
system dynamics
simulations
Hydraulic or pneumatic
systems prototypes
Control/Embedded -
Transfer functions,
state-space models,
control system design
System dynamics
simulations, control
system simulations
(MATLAB/Simulink)
Embedded systems
prototypes,
microcontroller-based
systems
Electromechanical
Electromechanical
actuators, sensors,
and their integration
Electromechanical
equations (e.g., motor
dynamics, actuator
dynamics)
Multi-domain
simulations (electrical,
mechanical, and
control)
Electromechanical
systems prototypes
(e.g., robotic arms,
motor control systems)
Human-Machine
Manikin models,
mockups
Human factors models,
ergonomic analysis
Human-in-the-loop
simulations, virtual
reality simulations
User interfaces, human-
robot interaction
systems

Model validation
● Model validation is a critical step in the development of any
system model. It involves comparing the model's predictions
with real-world data to assess its accuracy and reliability.
Importance of Model Validation
● Ensuring Model Accuracy: Validating the model helps to
identify and correct errors or inaccuracies.
● Building Confidence: A validated model increases confidence
in its predictions and decision-making capabilities.
● Improving Model Performance: Validation can lead to model
refinement and improvement.

Steps in Model Validation
Define Validation Criteria
● Establish metrics or thresholds for acceptable model accuracy, such as error
tolerance, response time, or stability requirements.
Collect Data from the Real System
● Obtain real-world data from experiments, testing, or operational records of the
actual system.
Compare Model Predictions with Real Data
● Use the model to generate predictions and compare these with actual observed
data.
Adjust and Refine the Model
● Modify the model parameters, assumptions, or structure to better match real-
world data if discrepancies arise.

Validation Techniques
Cross-Validation
● Description: Divide data into subsets, using some for model development and others for
testing accuracy.
● Purpose: Ensures the model’s robustness and generalizability to new data.
Sensitivity Analysis
● Description: Examines how changes in model parameters impact model output.
● Purpose: Identifies parameters that significantly affect model accuracy, guiding refinements.
Goodness-of-Fit Testing
● Description: Statistical methods (e.g., R-squared, chi-square tests) to measure how well
model outputs fit real data.
● Purpose: Provides quantitative validation of model accuracy.
Error Analysis
● Description: Calculate errors between model predictions and actual data (e.g., Mean Squared
Error, Root Mean Squared Error).
● Purpose: Quantifies the deviation of the model from reality and identifies areas for
improvement.

Validation Challenges
Data Collection Issues
● Obtaining high-quality, accurate data can be challenging, especially in complex or
large systems.
Model Complexity vs. Accuracy
● Overly complex models can be accurate but hard to validate; simpler models may lack
precision but are easier to validate.
Real-World Variability
● Real systems often have unpredictable variations, which can be hard to capture in a model.
Computational Resources
● High-fidelity validation can require extensive computing power and time.

Model reduction and simplification
What is Model Reduction?
● Definition: The process of simplifying a complex model to
retain essential characteristics while reducing complexity.
● Purpose: Achieves faster computation, easier analysis, and
improved understanding.
Why Simplify Models?
● Efficiency: Reduced models require less computational
power.
● Focus: Emphasizes key dynamics and removes non-essential
details.
● Application: Useful in real-time control, simulations, and
design processes.

Model Reduction Techniques
Aggregation
● Description: Combines similar elements or states in the model to reduce dimensionality.
● Example: Aggregating groups of resistors into a single equivalent resistor in an electrical circuit
model.
Asymptotic Approximation:
● Description: Approximates the model using simpler mathematical functions
● Example: Taylor series or polynomial approximations.
Truncation and State Elimination
● Description: Removes states or variables with negligible impact on system behavior.
● Example: Eliminating very high-frequency dynamics in a motor that don’t significantly affect
overall response.
Model Order Reduction (MOR)
● Description: Reduces the order of system equations to lower computational demands.
● Example: Using techniques like balanced truncation or Hankel norm approximation to simplify
system matrices.

Challenges and best practices in Model
Reduction
Challenge
● Loss of accuracy: Reducing the model order can lead to loss of accuracy,
especially for complex systems.
● Stability: Reduced-order models may exhibit different stability properties than
the original model.
● Physical interpretability: Simplified models may lose physical interpretability.
Best practices
● Clear objectives: Define the specific goals of the reduction process.
● Appropriate techniques: Choose the appropriate techniques based on the
specific characteristics of the system and the desired level of accuracy.
● Validation: Validate the reduced-order model against the original model and
experimental data.
● Iterative process: Continuously refine and improve the reduced-order model.

Next Lecture
Case study and Simulation

Sensors and Signal Conditioning
Tewedage Sileshi

Outline
● Introduction to sensors
● Sensors in Mechatronics
● Signal conditioning
● Signal Conditioning Techniques
● Sensor Integration in Mechatronics Systems
● Case Studies
● Exercises

Introduction to sensors
● Measurement is an important subsystem of a mechatronics system.
Its main function is to collect the information on system status and
feed it to the microprocessor(s) for controlling the whole system.
● Measurement system systems comprise sensors, transducers, and
signal processing devices.
● A transducer is a device that is actuated by power from one system
and supplies power, usually in another form, to a second system.
● A sensor is a device that responds to a physical stimulus and
transmits a resulting impulse.

Sensors
● Definition: A device that detects or measures a physical quantity and
converts it into a measurable electrical signal.
Types:
● Resistive Sensors: Strain gauges, thermistors, potentiometers
● Capacitive Sensors: Proximity sensors, level sensors
● Inductive Sensors: Proximity sensors, position sensors
● Piezoelectric Sensors: Force sensors, acceleration sensors
● Optical Sensors: Photodiodes, phototransistors, and photoresistors
● Magnetic Sensors: Hall effect sensors, magnetometers

Transducers
Definition: A device that converts one form of energy into another.
Role: Often used in conjunction with sensors to amplify or modify the
signal.
Examples:
● Analog-to-Digital Converters (ADCs): Convert analog signals
from sensors into digital signals for processing by
microcontrollers or computers.
● Digital-to-Analog Converters (DACs): Convert digital signals into
analog signals to control actuators.

Signal Conditioning Circuitry
Purpose: Amplifies, filters, and conditions the signal for
further processing.
Common Techniques:
● Amplification: Increases the signal strength.
● Filtering: Removes unwanted noise and interference.
● Offset and Bias: Adjusts the signal level.
● Impedance Matching: Ensures efficient power transfer
between components.

Sensor Performance
Range
● Maximum and minimum values that can be measured. For example, a thermocouple
for the measurement of temperature might have a range of 22–25 °C.
Span
● The span is the difference between the maximum and minimum values of the input.
Thus, the above-mentioned thermocouple will have a span of 200 °C.
Resolution or discrimination
● Smallest discernible change in the measured value. For example, if an LVDT sensor
measures a displacement up to 20 mm and it provides an output as a number
between 1 and 100, then the resolution of the sensor device is 0.2 mm.

Cont’d
Error: difference between the measured and actual values
● random errors
● systematic errors
Accuracy, precision
● Accuracy is a measurements closeness to the actual value
● Precision is measurements closeness to each other

Cont’d
Linearity
● Maximum deviation from a ‘straight-line’ response
● Normally expressed as a percentage of the full-scale value
Sensitivity
● A measure of the change produced at the output for a
given change in the quantity being measured

Types of sensors
Sensor Type Measurement
Parameter
Examples Applications in Mechatronics
Position
Sensors
Displacement or
position Potentiometers, LVDTs
Robotic arm positioning, CNC machine tool
tracking
Proximity
Sensors
Presence or absence
of objects Inductive, Capacitive
Object detection in conveyor systems, collision
avoidance in robots
Motion
Sensors Velocity, acceleration Encoders, Tachometers
Speed control in motors, navigation systems in
drones
Force/Torque
Sensors Force, torque
Strain gauges, Load
cells
Gripper feedback in robotic systems, torque
monitoring in motors
Temperature
Sensors Temperature Thermocouples, RTDs
Thermal control in 3D printers, engine
temperature monitoring
Pressure
Sensors Pressure
Piezoelectric, Strain-
based
Hydraulic systems monitoring, pneumatic
actuator feedback

Cont’d
Magnetic
Sensors Magnetic fields
Hall effect sensors,
Magnetometers
Position sensing in motors, magnetic
navigation systems
Light Sensors
Light intensity,
presence Photodiodes, LDRs
Automated lighting control, optical encoders
in robotics
Sound
Sensors Acoustic waves
Microphones,
Ultrasonic
Voice recognition in robots, obstacle
detection using ultrasonic waves
Chemical
Sensors
Gas or liquid
composition
pH sensors, Gas
sensors
Environmental monitoring and quality control
in food processing

Sensors in Mechatronics
● Feedback Control: Sensors provide real-time information about
the system's state, allowing for precise control and adjustments.
● Monitoring and Diagnostics: Sensors can detect faults and
anomalies, enabling predictive maintenance and improving system
reliability.
● Data Acquisition and Analysis: Sensors collect data for analysis
and optimization, leading to improved performance and energy
efficiency.
● Human-Machine Interface: Sensors can provide feedback to
human operators, such as visual displays or auditory alerts.

Signal conditioning
Definition: The process of modifying or manipulating an
electrical signal to make it suitable for further processing or
display.
Why is it necessary?
● Amplifying weak signals
● Filtering out noise and interference
● Converting signals to a desired format
● Isolating sensitive circuits
● Linearizing nonlinear sensor outputs

Signal Conditioning Techniques
Amplification
● Purpose: Increases the amplitude of a weak signal.
● Common techniques:
○ Operational amplifiers (op-amps)
○ Instrumentation amplifiers
Filtering
● Purpose: Removes unwanted frequency components.
● Types of Filters:
○ Low-pass filter: Allows low-frequency signals to pass.
○ High-pass filter: Allows high-frequency signals to pass.
○ Band-pass filter: Allows a specific range of frequencies to pass.
○ Notch filter: Attenuates a specific frequency band.

Cont’d
Offset and Bias
● Purpose: Shifts the DC level of a signal.
● Applications:
○ Zeroing a sensor output
○ Adjusting the input range of an ADC
Impedance Matching
● Purpose: Ensures efficient power transfer between components.
● Techniques:
○ Using buffer amplifiers
○ Employing impedance matching networks

Cont’d
Isolation
● Purpose: Prevents interference and ground loops.
● Techniques:
○ Op-amp-based isolators
○ Optical isolators
○ Transformer isolation
Linearization
● Purpose: Converts nonlinear sensor outputs into linear signals.
● Techniques:
○ Look-up tables
○ Analog function generators
○ Digital signal processing (DSP)

Sensors integration in mechatronics systems
Sensor integration is a critical aspect of mechatronics systems, enabling
them to perceive and interact with the physical world.
Key Considerations for Sensor Integration:
● Sensor Selection:
○ Identify the required physical quantities to be measured (e.g.,
position, velocity, force, temperature, pressure).
○ Choose sensors with appropriate sensitivity, range, resolution, and
accuracy.
○ Consider environmental factors like temperature, humidity, and
vibration.
● Signal Conditioning:
○ Amplify weak signals.
○ Filter out noise and interference.
○ Convert signals to a suitable format for processing.

Cont’d
● Interfacing with Microcontrollers:
○ Use analog-to-digital converters (ADCs) to convert analog
sensor signals into digital format.
○ Employ appropriate communication protocols (e.g., I2C, SPI,
UART).
○ Write efficient code to read sensor data and perform necessary
calculations.
● Data Processing and Fusion:
○ Filter and process sensor data to remove noise and improve
accuracy.
○ Combine data from multiple sensors to obtain a more complete
picture of the system's state.
○ Use techniques like Kalman filtering and sensor fusion to
estimate system parameters.

Case studies: Human Activity Monitoring
Using Smartphone Sensors
Objective:
● Use a smartphone’s built-in sensors to log data and
classify human activities (e.g., walking, running, or
standing).
Steps:
1. Sensor Logger App Setup:
○ Download a sensor logger app
○ Enable accelerometer and gyroscope data logging.
2. Data Collection:
○ Perform activities (walking, running, standing).
○ Record sensor data for 1 minute per activity.

Cont’d
3. Data Preprocessing:
● Clean the data: Remove noise or irrelevant readings.
● Normalize data: Scale readings for consistent comparison.
● Extract features: Calculate mean, variance, and frequency
components.
4. Discussion Points:
● What patterns in the accelerometer/gyroscope data
differentiate activities?
● How does preprocessing improve the signal for analysis?

Exercise
Pre-processing and Feature Extraction from IMU Data for Hand gesture
recognition, Gait activity Recognition or Gait Event Detection
● Data Acquisition and Preparation
○ Data Collection
○ Data Pre-processing
● Feature Extraction
○ Time-Domain Features
○ Frequency-Domain Features
○ Time-Frequency Domain Features
● Data Analysis and Visualization
○ Data Visualization
○ Feature Analysis

Outline
● Introduction to actuators
● Classification of actuators
● Actuators selection and control
● Integration of Actuators into Mechatronic Systems
● Case study
● Exercises

Introduction to actuators
What is an Actuator?
● A device that converts energy into mechanical motion or force.
● The "muscle" of a mechatronic system.
● Essential for interaction with the physical world.
● They are the "action-taking" components of a system, bridging the gap
between control signals and physical movement.
Key Roles of Actuators in Mechatronics:
1. Physical Output:
○ Motion Generation: Actuators generate precise and controlled
motion, essential for robotic arms, robotic vehicles, and
automation systems.
○ Force Generation: Actuators provide the necessary force to
manipulate objects, assemble products, and perform other tasks.

Cont’d
2. System Interaction:
○ Interfacing with the Environment: Actuators allow mechatronic
systems to interact with their surroundings, such as opening and
closing doors, picking up objects, or adjusting machine settings.
○ Human-Machine Interaction: Actuators enable haptic feedback
and physical interaction with users, enhancing user experience
and safety.
3. System Control:
○ Feedback Control: Actuators can be integrated into closed-loop
control systems to achieve precise and accurate performance.
○ Real-time Response: Actuators can respond quickly to changes
in the environment or control signals, enabling real-time
adjustments.

Classification of actuators
Electric Actuators:
● DC Motors
● AC Motors
● Stepper Motors
● Servo Motors
Pneumatic Actuators:
● Pneumatic Cylinders
● Rotary Actuators
Hydraulic Actuators:
● Hydraulic Cylinders
● Hydraulic Motors
Mechanical Actuators:
● Gears
● Belts
● Levers
● Cam mechanisms

Electrical actuators
Electric actuators convert electrical energy into mechanical energy to
produce motion or force. They are widely used in mechatronics systems
due to their precision, versatility, and ease of control.
Type of
Electric
Actuator
Functionality Features Applications
DC Motors Rotational motion
Simple, continuous and
smooth rotation, low cost,
good speed control
Robotics,
automation,
automotive
AC Motors Rotational motion
High power, robust,
reliable
Industrial
machinery, power
generation
Stepper
Motors
Rotational motion in
discrete steps
Precise positioning, open-
loop control
3D printers, CNC
machines, robotics
Servo Motors
Rotational motion within
a specified range
Precise positioning,
closed-loop control, high
speed
Robotics,
automation, RC
vehicles

Pneumatic actuators
Pneumatic actuators convert compressed air into mechanical
motion. They are widely used in industries due to their simplicity,
reliability, and safety.
Type of Pneumatic
Actuator
Pneumatic
Cylinder
Linear motion
Simple design, reliable, fast
response
Assembly lines, material
handling, clamping
systems
Vane Motor Rotary motion
Compact design, high torque
at low speeds
Rotary tables, indexing
mechanisms
Gear Motor
Rotary motion with gear
reduction
High torque, precise
positioning
Valve actuation, conveyor
systems
Rack and Pinion
Actuator
Linear motion from rotary
input
Simple design, versatile
Linear positioning
systems, valve actuation

Hydraulic actuators
Hydraulic actuators convert hydraulic fluid pressure into mechanical
motion or force. They are widely used in heavy machinery and industrial
applications due to their high power density and precise control.
Type of Hydraulic
Actuator
Hydraulic Cylinder Linear motion
High force output, precise
control, durability
Construction equipment,
heavy machinery,
aerospace systems
Gear Motor Rotary motion
High power density, variable
speed, reversible operation
Mobile equipment,
industrial machinery,
marine systems
Vane Motor Rotary motion
Compact design, high torque
at low speeds
Mobile equipment,
industrial machinery
Piston Motor Rotary or linear motion
High power density, efficient,
versatile
Mobile equipment,
industrial machinery

Mechanical actuators
Mechanical actuators are essential components in mechatronics systems,
providing the physical motion and force required to perform tasks. They are
often combined with electrical, pneumatic, or hydraulic actuators to create
complex and precise movements.
Type of Mechanical
Actuator
Joints
Enables relative motion
between components
Simple, versatile, low friction
Robotics, mechanisms,
linkages
Linkages
Transmits and modifies
motion
Amplifies or reduces forces,
changes motion direction
Mechanical clocks, robotic
arms, automotive systems
Gears
Transmits rotational motion
and torque
Speed reduction/increase,
torque multiplication
Automotive transmissions,
machine tools, robotic
systems
Cam Mechanisms
Converts rotary motion into
linear or oscillating motion
Precise control of motion,
complex motion profiles
Engine valve trains,
automation systems,
printing presses

Cont’d
Type of
Actuator
Advantages Limitations Applications
Electric
Actuators
Precise control,
efficient, clean,
versatile
Lower power output,
potential for overheating
Robotics, automation,
automotive
Pneumatic
Actuators
Fast response, simple
design, reliable, safe
Less precise control, noisy,
compressed air infrastructure
required
Assembly lines, material
handling, automotive
Hydraulic
Actuators
High force and torque,
precise control,
durability
Complex design, potential for
leaks, requires hydraulic fluid
Construction equipment,
heavy machinery,
aerospace
Mechanical
Actuators
Simple design,
reliable, low cost
Limited motion range,
potential for wear and tear
Mechanical clocks,
robotic arms,
automotive systems

Key Factors for Actuator Selection
Application Requirements:
● Nature of motion: Linear or rotary.
● Load capacity: Force/torque
requirements.
● Speed: How fast the actuator needs to
operate.
● Precision: Level of accuracy needed for
positioning or motion.
Energy Source Availability:
● Power source: Electrical, hydraulic, or
pneumatic systems available in the
environment.
Environmental Conditions:
● Temperature, humidity, dust, and
corrosive surroundings.
● Actuator durability and protection (e.g.,
IP ratings).
Cost and Maintenance:
● Initial cost, operational cost,
and ease of maintenance.
Integration with Control Systems:
● Compatibility with controllers,
sensors, and feedback
mechanisms.
Noise and Safety Requirements:
● Noise levels (e.g., hydraulic
actuators may be noisier than
electric ones).
● Safety for operators and
nearby equipment.

Control Strategies for Actuators
Open-Loop Control:
● Actuator operates without feedback.
● Simple, cost-effective, but less precise.
● Example: Stepper motors in low-precision applications.
Closed-Loop Control (Feedback Control):
● Real-time feedback (e.g., from sensors) ensures accurate operation.
● Common in systems requiring high precision and adaptability.
● Examples:
○ Servomotors using encoders for position control.
○ Hydraulic systems with pressure sensors.

Integration of Actuators into Mechatronic
Systems
System Design Considerations:
● Define motion requirements: Speed, force/torque, accuracy, and range
of motion.
● Select compatible actuator based on:
○ Energy source (electrical, hydraulic, pneumatic).
○ Type of motion (linear, rotary).
Control System Integration:
● Connect the actuator to the controller (e.g., microcontroller, PLC).
● Implement appropriate control algorithms (e.g., PID for precise motion).

Cont’d
Feedback Mechanism (Closed-Loop Systems):
● Sensors provide real-time data on position, speed, force, etc.
● Example feedback devices:
○ Encoders (position and speed).
○ Load cells (force).
Power Supply and Drive Circuitry:
● Ensure compatibility of actuator power requirements with the system.
● Use motor drivers, amplifiers, or solenoid valves as necessary.

Cont’d
Mechanical Integration:
● Mount actuators securely to the system frame or mechanism.
● Use couplings, gears, or linkages to transmit motion effectively.
System Testing and Calibration:
● Test actuator response to control inputs.
● Calibrate for optimal performance and minimize errors.

Challenges in Actuator Integration
Signal Interfacing: Ensuring the control signal matches the
actuator’s input requirements.
Noise and Interference: Electrical noise affecting actuator
performance.
Mechanical Alignment: Precise mounting to prevent misalignment
or inefficiencies.
Power Management: Avoiding overloading or overheating of
actuators.

Class Activity: Designing an Actuator
System for a Parking Lot Barrier
Activity Description:
Scenario: Your team has been tasked with designing an actuator system for an automated
barrier at a parking lot entrance.
Actuator Design Specification:
● Select the most suitable type of actuator for the barrier (e.g., electric, pneumatic,
hydraulic, or mechanical).
● Justify your choice based on the following considerations:
○ Weight of the barrier.
○ Speed of operation.
○ Power source availability.
○ Environmental conditions (e.g., outdoor exposure).
Control Strategy:
● Decide whether the system should use an open-loop or closed-loop control strategy.
● Explain the benefits and limitations of your choice.

Cont’d
Sensors for Integration:
● Identify the sensors needed to integrate the actuator into the system.
○ Examples:
■ Position sensors for barrier angle feedback.
■ Force sensors to detect obstructions.
● Discuss how these sensors improve the system's functionality.
Challenges and Mitigation Strategies:
● Highlight potential challenges in implementing your design (e.g.,
environmental durability, noise, cost).
● Suggest mitigation strategies to address these challenges.

Next Lecture
Control and Microprocessor

Control and Microprocessor
Tewedage Sileshi

Outline
● Introduction to control systems
● Types of control systems
● Control Strategies
● Microprocessors in Mechatronics
● Programming microprocessor
● Future Trends in Control and Microprocessor-Based
Systems

Introduction to control systems
● A system designed to regulate or guide the behavior of other systems to
achieve a desired output.
● Control systems can be classified as continuous time control systems
and discrete time control systems based on the type of signal used.
● In continuous time control systems, all the signals are continuous in
time. But, in discrete time control systems, there exists one or more
discrete time signals.
● Control systems can be classified as SISO control systems and MIMO
control systems based on the number of inputs and outputs present.
● SISO (single input and single output) control systems have one input
and one output. Whereas, MIMO (Multiple Inputs and Multiple Outputs)
control systems have more than one input and more than one output.

Types of control systems
Open-Loop Control System:
● Does not use feedback to correct errors.
● Simpler to implement but less accurate.
● Examples: Traffic lights, washing machines.

Cont’d
Closed-Loop Control System:
● Uses feedback to correct errors.
● More accurate and robust.
● Examples: Temperature control systems, robotic systems.
Why Feedback Matters
● Ensures system stability and accuracy.
● Adapts to disturbances and variations in the system.
● Example: Maintaining a car's speed on an incline.

Control strategies
● Methods or techniques employed to manage the
behavior of a system to achieve desired performance.
● In mechatronics, control strategies are crucial for
ensuring the accurate and efficient operation of various
systems.
● Objective: Ensure stability, accuracy, and efficiency.

Common Control Strategies
1. PID Control:
● Proportional (P) Control: Responds to the error signal
proportionally.
● Integral (I) Control: Accumulates the error signal over
time to eliminate steady-state error.
● Derivative (D) Control: Anticipates future error based on
the rate of change of the error.
● Advantages: Simple to implement, widely used.
● Disadvantages: Can be sensitive to noise and parameter
tuning.

Cont’d
2. State-Space Control:
● Model-Based Approach: Represents the system's dynamics
in state-space form.
● Design Techniques: Pole placement, optimal control, model
predictive control.
● Advantages: Handles complex systems, flexible design.
● Disadvantages: Requires accurate system models,
computational complexity.

Cont’d
3. Optimal Control:
● Formulates Control Problem: Defines a cost function to be
minimized.
● Solves Optimization Problem: Uses mathematical techniques
to find the optimal control input.
● Advantages: Efficient and precise control.
● Disadvantages: Computational complexity requires accurate
system models.

Cont’d
4. Adaptive Control:
● Self-Tuning: Automatically adjusts control parameters
based on system changes.
● Model-Reference Adaptive Control: Compares the
system's output to a reference model.
● Advantages: Robustness to uncertainties.
● Disadvantages: Complex implementation.

Cont’d
5. Fuzzy Logic Control:
● Rule-Based Approach: Uses fuzzy logic to make
decisions based on imprecise information.
● Advantages: Flexibility, robustness to uncertainties.
● Disadvantages: Requires careful rule base design.

Choosing the Right Control Strategy
● System Complexity: Simple systems may require simple control
strategies, while complex systems may require more advanced
techniques.
● Performance Requirements: The required level of accuracy,
speed, and stability will influence the choice of control strategy.
● Sensor Availability: The availability of sensors can impact the
choice of control strategy.
● Computational Resources: The computational power available
will limit the complexity of the control algorithm.
● Environmental Factors: Environmental factors like noise,
disturbances, and uncertainties can affect the performance of the
control system.

Comparison of Strategies
Control Strategy Advantages Disadvantages Applications
PID Control
Simple, widely
used, robust
Sensitive to parameter tuning,
may not be suitable for
complex systems
Temperature control,
motor control, robotics
State-Space Control
Handles complex
systems, flexible
design
Requires accurate system
models, computational
complexity
Aerospace, robotics,
automotive
Optimal Control
Efficient and
precise control
Computational complexity,
requires accurate system
models
process control
Adaptive Control
Robustness to
uncertainties,
self-tuning
Complex implementation,
requires online identification
automotive
Fuzzy Logic Control
Flexibility,
robustness to
uncertainties
Requires careful rule base
design, potential for
overfitting
Consumer electronics,
automotive, medical
devices

Microprocessors in Mechatronics
● A microprocessor is a compact integrated circuit that
performs computational tasks based on instructions.
● Role in Mechatronics: Acts as the controller in many
automated and robotic systems.
● Examples: Arduino, Raspberry Pi, ARM Cortex.
Key Functions in Mechatronics
1. Control: Executes algorithms for real-time control of
systems.
2. Computation: Processes data from sensors and inputs.
3. Communication: Interfaces with other devices via
communication protocols.
4. Decision Making: Implements logic for intelligent behavior.

Microprocessors vs. Microcontrollers vs.
PLCs
● Microprocessors: Focus on computational tasks; require
external components for peripherals.
● Microcontrollers: Integrated solution with memory, I/O
ports, and peripherals.
● PLCs: Rugged devices for industrial automation with
high reliability.

Common Microprocessors in Mechatronics
Arduino (Microcontroller-based)
● Easy to use for prototyping.
● Widely used in student projects.
Raspberry Pi
● General-purpose computer for advanced tasks like image
processing.
ARM Cortex Processors
● High-performance processors for embedded systems.

Applications in Mechatronics
● Robotics: Control of motion and sensors in robotic
arms.
● Automotive Systems: Engine control, autonomous
navigation.
● Industrial Automation: Conveyor belts, CNC machines.
● Smart Devices: Home automation systems, drones.

Arduino
● Arduino is an open-source electronics platform based on easy-to-use
hardware and software.
● Used for prototyping, education, and control applications.
Key features:
● Microcontroller-based (e.g., ATmega328 on Arduino Uno).
● Simple to program using Arduino IDE (C/C++ based).
● Flexible for various applications in robotics, IoT, and mechatronics.
Why Arduino?
● Easy-to-learn platform for beginners.
● Widely used in prototyping and education.
● Affordable and versatile.

Arduino Uno R3 Overview
Technical Specifications:
● Microcontroller: ATmega328P.
● Operating voltage: 5V.
● Digital I/O pins: 14 (6 PWM).
● Analog input pins: 6.
● Communication: USB, UART, SPI, I2C.
Common uses:
● Driving motors, controlling LEDs, or interfacing sensors.

The Arduino IDE
Integrated Development Environment (IDE): A software application
for writing, compiling, and uploading code to the Arduino board.
Key Features:
● Code Editor: Supports syntax highlighting, auto-completion, and
code formatting.
● Compiler: Translates the code written in the Arduino language
(C/C++ based) into machine code.
● Uploader: Transfers the compiled code to the Arduino board's
memory.
● Serial Monitor: Allows for serial communication with the Arduino
board.

Cont’d
Basic workflow:
1. Write code in Arduino IDE.
2. Compile and upload via USB.
3. Observe results.

Future Trends in Control and Microprocessor
Based Systems
Artificial Intelligence and Machine Learning:
● Intelligent control systems
● Adaptive and self-learning systems
Internet of Things (IoT):
● Connected devices and remote control
● Data-driven insights and optimization
Cyber-Physical Systems (CPS):
● Integration of physical and digital worlds
● Real-time control and monitoring
Edge Computing:
● Distributed processing for faster
response times
● Improved privacy and security
Human-Machine Interaction (HMI):
● Natural language interfaces
● Virtual and augmented reality
● Biometric authentication

Future Outlook and Challenges
● The future of control and microprocessor-based systems is
characterized by smarter, more adaptive, and interconnected
architectures.
● Deeper integration across diverse industries will lead to innovative
applications and enhanced efficiency.
Challenges:
● Real-Time Constraints: Managing high-speed data processing and
decision-making.
● Security: Protecting interconnected systems from cyber threats.
● Power Efficiency: Ensuring low-power operation for portable and
mobile devices.
● Integration Complexity: Ensuring seamless communication between
edge devices and cloud servers.

Example 1: DC Motor Speed Control using a
PID Controller in Simulink
Plant: DC motor
Input Controller Plant Output
Feedback

Cont’d
Components
1. Signal Blocks:
Step Input: Represents the desired motor speed (setpoint).
1. Math Operations:
Gain Block: Models motor dynamics, such as inertia and torque.
1. Control Systems:
PID Controller Block: Adjusts motor input to achieve desired speed.
1. Sinks: Visualizes motor speed and controller response over time.

Cont’d
Configure Block Parameters
● Step Input Block:
Step time: t=1
Initial value: 0
Final value: 1 (or desired setpoint value).
● PID Controller:
P=2, I=1, D=0.5 (example gains).
● Gain Block:
Gain: 0.1 (model your motor dynamics).
● Sum Block:
Set to “+/-” configuration to compute
Error=Setpoint−Output

Cont’d
Tune the Controller:
● Proportional (Kp): Speed response.
● Integral (Ki): Steady-state error.
● Derivative (Kd): Damping effect.
Setup:
● Set simulation time (e.g., 10 seconds).
● Choose solver type (e.g., ODE45).
Observe Results:View motor speed and controller output
Key Metrics:
● Rise Time: Time taken to reach desired speed.
● Steady-State Error: Difference between desired and actual speed.
● Overshoot: Maximum speed deviation from setpoint.

Feedback
Gain Block (Sensor Scaling):
● Set the gain value based on the sensor's scaling factor. For example:
○ If the sensor converts 1V to 100 units, set Gain=100
○ Default: Gain=1(no scaling).
Transfer Function Block (Optional Sensor Dynamics):
● If the sensor has first-order dynamics, use a transfer function:
where τ is the time constant.
Random Number Block (Optional Noise):
● Set parameters to simulate sensor noise:
○ Mean: 0.
○ Variance: Appropriate for your system (e.g., 0.01).

Custom input
● Drag and drop the "From Workspace" block into your Simulink model.
● Configure the Block:
● In the block parameters, select the variable name (external_data) from the workspace.
● Choose the appropriate data type (e.g., 'array').
● Connect the "From Workspace" block to the appropriate input point in your Simulink
model.
● Create a separate MATLAB script to generate the input signal data and save it to a MAT-
file.
time = 0:0.1:10; % Time vector (0 to 10 seconds with 0.1s step)
inputData = sin(time); % Example: Sine wave data
input = [time' inputData']; % Combine time and data for Simulink
● Simulink Model:
○ Use the "To Workspace" block to log the simulation time from Simulink to the
workspace.
○ Use the "From Workspace" block to read the pre-generated input signal data from the
MAT-file.
○ Interpolate the input signal data based on the simulation time.

Example 2: Simulating a Temperature
Control System
System Overview
● Plant: A room with a heater modeled using thermal
dynamics.
● Controller: PID controller to regulate temperature.
● Input: Desired temperature (setpoint).
● Output: Actual room temperature.
● Disturbance: External factors like heat loss or
environmental temperature changes.

Cont’d
Input: Use a constant block for the desired temperature (setpoint).
Gain Block: Represents thermal mass (e.g., 1/101/101/10).
PID Controller: Add a PID Controller block.
Plant Model: Use Gain, Integrator, and Sum blocks to represent
the room's thermal dynamics:

Cont’d
Connect the blocks to represent the control system:
● Constant (desired temperature) → PID controller → Heater
power.
● Heater power → Thermal dynamics (Gain and Integrator) →
Room temperature.
● Room temperature → Feedback loop to PID controller.
● Room temperature → Scope for output visualization.
Set Solver to ODE45.
Set simulation time (e.g., 100 seconds).

Outline
● Introduction
● Architecture of PLC
● Operation of PLC
○ Sensors and Actuators in PLC Systems
○ Communication in PLC Systems
● PLC Programming
● Ladder diagram basics
● Simulation

Introduction
● A Programmable Logic Controller (PLC) is an industrial
computer designed for real-time control of machines,
processes, and systems.
● Functions include monitoring inputs, executing a user-
defined program, and controlling outputs.
Core Characteristics:
● Rugged and designed for industrial environments.
● Modular and scalable architecture.
● Deterministic execution for real-time applications.

Application of PLC
● Manufacturing and Assembly Lines: Automating tasks such as
sorting, packaging, and assembly.
● Robotics Control: Managing robotic arms in welding, painting,
and material handling.
● Process Control: Used in industries like oil and gas, food
processing, and chemical manufacturing.
● Building Automation: HVAC systems, lighting control, and
security systems.
● Transportation Systems:Traffic light control and railway
signaling.

Advantage
Flexibility: Easily reprogrammed for different
applications.
Modularity: Expandable with I/O modules for
various input/output needs.
Reliability: Rugged and designed for continuous
operation in harsh industrial environments.
Diagnostics: Built-in diagnostics and
troubleshooting tools.
Safety: Integrated safety features to prevent
accidents.
Limitation
Initial Cost: Can have a higher initial
investment compared to traditional
relay-based systems.
Programming Complexity: Requires
specialized knowledge and training
for programming.
Vendor Lock-in: Some proprietary
systems may limit flexibility and
interoperability.

Basic Architecture of a PLC
The architecture of a PLC consists of several core components working
together to execute control tasks.
Main components include:
● Power Supply: Converts AC power to the DC power required for PLC
operation.
● Central Processing Unit (CPU): Processes inputs, executes programs,
and updates outputs; Handles diagnostics and error detection;
Maintains system timing (real-time clock).
● Input/Output (I/O) Modules: Receive signals from sensors, switches,
and other devices.
● Communication Interfaces: Allow PLCs to communicate with other
devices and systems.
● Programming Device: A programming device (PC or dedicated unit) is
used to: Create and edit PLC programs, Upload/download programs
to the CPU, Monitor system performance in real-time.

Operation of a PLC
The PLC operates in a cycle known as the Scan Cycle, which includes:
1. Input Scan: Reads the status of inputs (e.g., switches, sensors).
Converts analog signals into digital data using ADC (Analog-to-
Digital Converter).
2. Program Execution: Executes the user-defined logic based on input
data. Common languages include Ladder Logic, Structured Text, and
Function Block Diagram.
3. Output Scan: Updates the outputs (e.g., actuators, motors) based on
logic results. Uses DAC (Digital-to-Analog Converter) for analog
outputs.
4. Housekeeping: Conducts diagnostics, communication tasks, and
prepares for the next cycle.
Scan time: Milliseconds to seconds, ensuring real-time control.

Sensors and Actuators in PLC Systems
Sensors: Detect physical conditions and provide data to the PLC.
● Proximity sensors, temperature sensors, pressure sensors, etc.
Actuators: Perform physical actions based on PLC commands.
● Solenoids, motors, valves, etc.
Signal Conditioning:
● Amplification, filtering, and conversion of signals for accurate
processing.
● Example: Converting a temperature sensor's low-voltage signal into a
readable range for the PLC.

Communication in PLC Systems
Importance of Communication:
● Data exchange between PLCs.
● Integration with SCADA (Supervisory Control and Data Acquisition)
systems.
● Remote monitoring and control.
● Data logging and historical data analysis.
Communication Protocols:
● Serial Communication: RS-232, RS-485 (for point-to-point or multi-
drop connections).
● Fieldbus: High-speed, industrial communication protocols (e.g.,
Profibus, Profinet, EtherNet/IP).
● Industrial Ethernet: Enables high-speed data exchange and network
connectivity.
Network Topologies: Star, ring, bus, and mesh topologies.

What devices does a PLC interact with?
● INPUT RELAYS (contacts): Connect to the outside world. They physically
exist and receive signals from switches, sensors, etc. Typically, they are
not relays but rather transistors.
● INTERNAL UTILITY RELAYS (contacts): These do not receive signals
from the outside world, nor do they physically exist. They are simulated
relays and are what enables a PLC to eliminate external relays. There are
also some special relays that are dedicated to performing only one task.
Some are always on, while others are always off. Some are on only once
during power-on and are typically used for initializing data that was
stored.
● COUNTERS:These again do not physically exist. They are simulated
counters, and they can be programmed to count pulses. Typically these
counters can count up, down, or both up and down. Since they are
simulated, they are limited in their counting speed. Some manufacturers
also include high-speed counters that are hardware-based. We can think
of these as physically existing. Most times these counters can count up,

Cont’d
● TIMERS: These also do not physically exist. They come in many
varieties and increments. The most common type is an on-delay type.
Others include off-delay and both retentive and non-retentive types.
Increments vary from 1 ms through 1s.
● OUTPUT RELAYS (coils): These are connected to the outside world.
They physically exist and send on/off signals to solenoids, lights, etc.
They can be transistors, relays, or triacs, depending upon the model
chosen.
● DATA STORAGE: Typically there are registers assigned to simply
store data. They are usually used as temporary storage for math or
data manipulation. They can also typically be used to store data when
power is removed from the PLC. Upon power-up, they will still have
the same contents as before power was removed.

PLC Programming
The process of instructing the PLC to perform specific tasks based on
input signals and control logic.
Programming Languages:
● Ladder Logic: Most common and user-friendly, resembles relay
ladder diagrams.
● Function Block Diagram (FBD): Uses graphical blocks
representing functions (e.g., AND, OR, NOT).
● Structured Text (ST): Text-based language similar to high-level
programming languages (e.g., Pascal).
● Instruction List (IL): Uses mnemonic codes to represent
instructions.

Program Organization
Divide the program into logical sections:
1. Initialization: Setup of variables and configurations.
2. Input Processing: Read and condition inputs.
3. Logic Execution: Process inputs to determine outputs.
4. Output Processing: Update outputs based on logic.
PLC Programming Software:
● Provided by PLC manufacturers (e.g., Rockwell Automation
RSLogix, Siemens TIA Portal).
● Offers tools for programming, debugging, and monitoring PLC
operation.

Ladder Logic
A graphical programming language that resembles relay ladder diagrams.
Function Blocks:
● Power Rail: Represents the power supply. The power rails simulate the
power supply lines.
● Contacts: Represent input signals (normally open, normally closed).
● Coils: Represent output signals (e.g., turning on a motor, activating a
solenoid).
● Logic Gates: AND, OR, NOT gates to implement complex logic.
● Timers and Counters: For time-based operations and counting events.
The processor (or “controller controller)" scans ladder rungs from top to
bottom and from left to right. The basic sequence is altered whenever jump
or subroutine instructions are executed.

Logic Functions
● PLC programming is a logical procedure
● In a PLC program, “things” (inputs and rungs) are either TRUE or
FALSE
● If the proper input conditions are TRUE: The rung becomes
TRUE and an output action occurs (for example, a motor turns on)
● If the proper input conditions are not TRUE: The rung becomes
FALSE and an output action does not occur.
● Ladder logic is based on the following logic functions:
○ AND
○ OR: Sometimes called “inclusive OR.”
○ Exclusive OR

Cont’d
R1 = PB1 .OR. (PB2 .AND. PB3)
pb3

Function Block Diagram (FBD)
A graphical programming language where program logic is represented by
interconnected function blocks.
Function Blocks:
● Input Blocks: Represent input signals from sensors and switches.
● Output Blocks: Control actuators and output devices.
● Logic Gates: AND, OR, NOT, XOR for Boolean logic operations.
● Timers/Counters: Implement time-based operations and event counting.
● Math Functions: Perform mathematical operations (e.g., addition, subtraction,
multiplication, division).
● Comparison Functions: Compare values (e.g., greater than, less than, equal to).
Connections: Lines connecting the input and output terminals of function blocks.
Data Flow: Data flows from input blocks through the network of function blocks to
output blocks.

Structured Text (ST)
A text-based programming language for PLCs, similar to high-level languages like
Pascal.
Basic ST Elements
● Variables:
○ Declare variables to store data (e.g., input signals, output signals,
internal variables).
○ Data types: BOOL, INT, REAL, STRING, etc.
● Operators:
○ Arithmetic operators: +, -, *, /
○ Logical operators: AND, OR, NOT, XOR
○ Comparison operators: >, <, >=, <=, =, <>
● Control Flow Statements:
○ IF-THEN-ELSE: Conditional execution based on a condition.
○ CASE: Select a block of code to execute based on a value.
○ FOR loop: Repeat a block of code a specified number of times.
○ WHILE loop: Repeat a block of code as long as a condition is true.

Instruction List (IL)
A text-based programming language using mnemonics and operands to
represent instructions.
Instructions:
● LD: Load input signal.
● AND: Logical AND operation.
● OR: Logical OR operation.
● NOT: Logical NOT operation.
● OUT: Set the output signal.
● MOV: Move data between variables.
● JMP: Jump to a specific label.
● CALL: Call a subroutine.
Operands: Input/Output addresses, variables, constants.

Feature Ladder Logic (LD)
Function Block Diagram
(FBD)
Structured Text (ST) Instruction List (IL)
Ease of Use
User-friendly; intuitive for
electricians and engineers
familiar with relay diagrams.
Graphical interface; easy
to visualize functions.
Text-based; requires
programming
knowledge.
Compact but requires
familiarity with
mnemonic codes.
Representation
Resembles electrical relay
ladder diagrams.
Graphical blocks
connected by lines.
High-level textual
syntax (similar to
Pascal).
Low-level textual
instructions
(mnemonics).
Application
Simple on/off control and
sequential logic.
Ideal for complex
systems with reusable
functions.
Best for advanced logic
and mathematical
operations.
Suitable for resource-
constrained systems.
Readability Easy to read for beginners.
Clear for visual learners;
harder for large systems.
Clear for programmers
but challenging for
others.
Difficult to read for
large or complex
systems.
Flexibility
Limited for complex
computations.
Moderately flexible with
custom functions.
Highly flexible for all
types of logic.
Limited flexibility;
requires optimized
programming.
Execution Speed Moderate. Moderate. High.
Very high due to low-
level nature.
Use Cases
Common in manufacturing and
simple automation tasks.
Process control and
modular designs.
Advanced control
strategies and data
manipulation.
Legacy systems and
compact coding
requirements.
Learning Curve Low. Low to Moderate. Moderate to High. High.

Troubleshooting and Debugging
Common Issues:
● Incorrect program logic
● Wiring errors
● Faulty sensors or actuators
● Communication problems
Debugging Techniques:
● Use PLC diagnostic tools.
● Monitor input/output signals.
● Step through the program to identify errors.
● Use simulation software to test the program before implementation.

Example
Oil is consumed randomly. The tank needs to be refilled by turning on
a pump. Two hydrostatic switches are used to detect a high and low
level.

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