Electric Drives and Servo System

Servomotor
• A servomotor is a rotary actuator or linear actuator that
allows for precise control of angular or linear position,
velocity and acceleration.

• It consists of a suitable motor coupled to a sensor for

position feedback. It also requires a relatively
sophisticated controller, often a dedicated module
designed specifically for use with servomotors.
• Servomotors are not a specific class of motor
although the term servomotor is often used to
refer to a motor suitable for use in a closed-
loop control system.

• Servomotors are used in applications such as

robotics, CNC machinery or automated
manufacturing.
Types of Servomotor
 DC Motor
• A DC motor is any of a class of rotary electrical
machines that converts direct current
electrical energy into mechanical energy.
• The most common types rely on the forces
produced by magnetic fields.
• Nearly all types of DC motors have some
internal mechanism, either electromechanical
or electronic, to periodically change the
direction of current flow in part of the motor.
AC Motor
Stepper Motor
 Difference Between Synchronous
Motor And Induction Motor
• AC motors can be divided into two main
categories –
• Synchronous motor
• Asynchronous motor. An asynchronous motor
is popularly called as Induction motor.
• Both the types are quite different from each
other. Major differences between a
synchronous motor and an induction motor
are discussed further.
 Constructional Difference
• Synchronous motor: Stator has
axial slots which consist stator
winding wound for a specific
number of poles.
• Generally a salient pole rotor is
used on which rotor winding is
mounted.
• Rotor winding is fed with a DC
supply with the help of slip
rings. A rotor with permanent
magnets can also be used.
• Induction motor: Stator
winding is similar to that of a
synchronous motor. It is
wound for a specific number
of poles.
• A squirrel cage rotor or a
wound rotor can be used. In
squirrel cage rotor, the rotor
bars are permanently short-
circuited with end rings.
• In wound rotor, windings are
also permanently short-
circuited, hence no slip rings
are required.
 Difference In Working
• Synchronous motor: Stator poles rotate at the
synchronous speed (Ns) when fed with a three phase
supply.
• The rotor is fed with a DC supply. The rotor needs to
be rotated at a speed near to the synchronous speed
during starting.
• If done so, the rotor poles get magnetically coupled
with the rotating stator poles, and thus the rotor
starts rotating at the synchronous speed.
• Synchronous motor always runs at a speed equal to
its synchronous speed.
• Induction motor: When the stator is fed with
two or three phase AC supply, a Rotating
Magnetic Field (RMF) is produced.
• The relative speed between stator's rotating
magnetic field and the rotor will cause an
induced current in the rotor conductors.
• The rotor current gives rise to the rotor flux.
• According to Lenz's law, the direction of this
induced current is such that it will tend to
oppose the cause of its production, i.e. relative
speed between stator's RMF and the rotor.
• Thus, the rotor will try to catch up with the
RMF and reduce the relative speed.
• Induction motor always runs at a speed which
is less than the synchronous speed.
 How Does A Servo Motor Work
• A servomotor is an electromechanical device
that produces torque and speed based on the
current and voltage supplied.
• A servo motor operates as part of a closed-
loop controller, providing torque and speed as
commanded by a servo controller that uses a
feedback device to close the loop.
• The feedback device provides information
such as current, speed, or position to the
servo controller, which adjusts the motor
action depending on the commanded
parameters.
• Servos are controlled by sending a variable
width electrical pulse or pulse width
modulation (PWM) over the control cable.
• There is a minimum heart rate, a maximum
heart rate, and a repetition rate.
• A servo motor can normally only rotate 90 ° in
each direction. Which adds up to a total of
180 ° of movement.
• The neutral position of the motor is defined as
the position where the servo has the same
potential rotation in both clockwise and
counterclockwise directions.
• The PWM sent to the motor determines the
position of the shaft and is based on the duration
of the pulse sent over the control cable; the rotor
turns into the desired position.
• The servo motor expects a pulse every 20
milliseconds and the length of the pulse
determines how far the motor turns. A pulse
of 1.5ms, for example, causes the motor to
turn to the 90° position.
• For less than 1.5ms it moves counterclockwise
towards the 0°position, and longer than 1.5ms
rotates the servo clockwise towards the 180°
position.
• When a move command is given to these
servos, they will move into position and hold
that position.
• If an external force is pressing against the
servo while the servo is holding a position, the
servo will resist moving from that position.
• The maximum force the servo can exert is
called the servo’s torque.
• Servos won’t hold their position forever,
however; The position pulse must be repeated
to tell the servo to stay in position.
Servomotor
 Direct Drive Actuator
• Theoretically, the term ‘direct drive’ can be
applied to any motor which directly drives a load
or rotor without transmission elements such as
gears, pulleys or chains.
• More usually, the term refers to brushless,
permanent-magnet, synchronous motors which
transmit their torque directly to their load or
rotor.
• Actuators are the component responsible for providing
movement and strength in the joints and axes of a
machine, like a robot.
• A key factor in machine operations is a control signal and
power input to facilitate movement. However, you also
need to convert the output of the motor into usable speed
and torque.
• Think of the gears on a bicycle.
• Your leg might not be strong enough to drive the wheel of
the bike directly.
• Gears are used to change the torque required to drive the
wheel.
• The same goes for robotic actuators, where a traditional
motor/gearbox combination functions together to convert the
lower torque output of the motor to achieve powerful motion in
the robot arm at a usable speed.

• The more complex the gear system (i.e., the higher ratio or more
gear stages), which is typically required for higher-torque
applications, the more backlash that exists in the transmission
system.

• Backlash will impact the precision of the robot and in extreme

cases, may even affect safety.
 How it works
• Direct drive motors work in much the same way
as most brushless DC motors. Magnets are
attached to the motor’s rotor and windings are
arranged on the motor’s stator.
• As the windings are energized, they produce
electromagnetic fields which either attract or
repel the rotor’s magnets.
• Appropriate switching or ‘commutation’ of power
to the windings produces a controlled motion.
• There are linear and rotary direct drive motors but
rotary versions are by far the most frequently
used.

• Direct drive motors usually have a large number

of poles (>30 and sometimes >100) which allows
them to produce high torque at no or low speed
(usually <1000rpm).
• Direct drive motors with diameters of >1m are
possible, able to produce torque of
>10,000Nm.

• Many direct drive motors are ‘frameless’

which means that they are supplied without a
housing, bearings or feedback sensor.
• This allows machine builders and system
integrators to streamline their housing, shaft
and bearing design to optimize overall size,
shape, weight and dynamic performance.
• The torque-to-inertia ratio is also higher in
direct drive motors than traditional motor
arrangements and there is a low electrical
time constant.
• This means that the torque is applied quickly
when voltage is applied, achieving what
control engineers refer to as good servo
‘stiffness’.
 Electrical time constant
• The transient response time to the current
that flows to the armature of a motor to
which a power supply voltage is applied.
• It is expressed by this formula: Electrical time
constant = Armature inductance/Armature
resistance.
 Advantages of the direct drive
approach are
• Excellent dynamic performance and accurate control
of position and/or speed

• No backlash or wear

• High reliability due to low part count & elimination of

gears, pulleys, seals, bearings etc.

• Compact – with low axial height and large bore

feasible
• Low torque ripple or ‘cogging’ . Torque ripple is an
effect seen in many electric motor designs, referring
to a periodic increase or decrease in
output torque as the motor shaft rotates.

• It is measured as the difference in maximum and

minimum torque over one complete revolution,
generally expressed as a percentage.
• High torque at low speeds

• Energy efficiency from eradication of losses in

intermediate mechanical elements

• Low acoustic noise or self-induced vibration

• No/low maintenance
• Low cooling requirements due to advantageous
thermal geometry
• Relatively large air gaps between the motor stator
and the rotor, the more cooling air flowing
through the air gap, which results the heat
exchange becomes better between the motor and
the surrounding cooling air and the temperature
rise of the motor is lower.
 Applications
• Direct drive applications are found in a antenna
systems (e.g. vehicle mounted satellite
communications), surveillance & CCTV
cameras, scanners, telescopes, electro-optics, rate
tables, radar and weapons systems.
• There are also applications in CNC machine tools,
packaging equipment, robotics and even high end
record turntables.
Record turntable
Direct drive actuator
• https://www.youtube.com/watch?v=GvMNloa
NMDE
 Mathematical Modeling
• Modeling is important in process industries.
There is no definite algorithm to construct a
mathematical model that performs better in all
situations.
• Modeling is viewed as a state-of-art technique. It
involves mathematical knowledge of the system
of interest and making the knowledge to create
models.
• Researchers have different knowledge base,
and a unique way of looking at problems.
Various researchers may come up with variety
of models for the same system.
• There is usually plenty of room for argument
about which model is “best".
• It is very important to understand at the
outset that for any real system, no “perfect”
model exists. All the models are subjected to
realistic assumptions.
• A mathematical model is defined as the set of
equations that describes the behaviour of the
system.
• It is the art of translating problems from an
application area into tractable mathematical
formulations whose theoretical and numerical
analysis provides insight, answers and guidance
useful for the originating application.
• Developing a precise model of the system is
difficult, but the model validates if it describes
the dominating dynamic properties of the
system.
• Modeling can be performed using experimental
data referred to as system identification and by
physical principles.

• A model may consist of algebraic, differential, or

integral equations, stochastic processes,
geometrical structures, etc.
• Mathematical modeling increases the
understanding of the system, predicts the
future system behaviour, carry technical and
quantitative computations for control design
from which optimization can be done.
Various steps involved in developing dynamic
model of a system are as follows
• Step 1 :Define System Boundaries
• Step2 : Make Simplifying Assumptions
• Step3 : Formulate the balance equations
• Step4 : Draw a block diagram involving all
inputs, outputs, parameters.
• Step5 : Present the Model in State Space or
transfer Function form
• State Space is known as the set of all possible
and known states of a system. In state-space,
each unique point represents a state of the
system. For example, Take a pendulum moving
in to and fro motion. The state of such an
idealized pendulum is represented by its angle
and its angular velocity.
• A transfer function represents the relationship
between the output signal of a control system
and the input signal, for all possible input
values
• A general model exists which includes the
system of interest as a special case, but it is
very difficult to compute with or analyse the
general model.

• The goal is then to simplify or make

approximations to the general model which
will still reflect the behaviour of the particular
system of interest.
The scientific method of modeling process is given
below
• Step 1 : Make general observations of phenomena
• Step 2 : Formulate a hypothesis
• Step 3 : Develop a method to test hypothesis
• Step 4 : Obtain data
• Step 5 : Test hypothesis against data
• Step 6 : Attempt to confirm or deny hypothesis
• Specific reasons for modeling is related in one
way or other with the following two categories.
First category is to gain understanding.
• A mathematical model accurately represents
some behaviour of a real-world system of
interest which can often gain improved
understanding of that system through the
analysis of the model.
• Furthermore, in the process of building the
model, certain factors are most important in the
system, and how different parts of the system are
related.
• Second category is to predict or simulate.
• Very often it becomes mandatory to know how a
real-world system behaves, as in the case of
nuclear reactor, space flight etc.
• It is expensive, impractical, or impossible to
experiment directly with the system.

• Modeling is an important task to be carried

out in such situations and based on the
mathematical model, computer simulations
can be performed before implementing the
same on hardware.
• A reasonable trade off exists between
accuracy, cost and flexibility.

• Increasing the accuracy of a model generally

increases cost and decreases flexibility.

• The goal in creating a model is usually to

obtain a sufficiently accurate and flexible
model at a low cost.
Flow diagram of Modeling
• The flow diagram of modeling process is
shown in Figure .

• Real world data represents quantitative

measurements of the system of interest.

• This data is processed and the information

pertaining to the real world data have been
collected.
• Based on the collective information of data
obtained, models have to be formulated or
constructed. Formulated Model is analyzed
and mathematical results are obtained.
• Interpretation on the results is carried out
and predictions on input-output behaviour of
the system under consideration were studied.
• The obtained results with the formulated
model are tested with real world data.
• Mathematical modeling is indispensable in
many applications, successful in many further
applications, gives precision and direction for
problem solution , enables a thorough
understanding of the system modelled,
prepares the way for better design or control
of a system and allows the efficient use of
modern computing capabilities.
• Modeling finds its applications in
Anthropology for modeling, classifying and
reconstructing skulls, in Archaeology for
Reconstruction of objects from preserved
fragments, in Artificial intelligence for
Computer vision, Image interpretation,
Robotics, Speech recognition, Optical
character recognition and Reasoning under
uncertainty, in Arts for Computer animation
(Jurassic Park), in Astronomy for Detection of
planetary systems, correcting the Hubble
telescope, Origin of the universe and
evolution of stars
• In Biology for Protein folding, Human genome
project and Population dynamics, in Chemical
engineering for Chemical equilibrium and
Planning of production units, in Chemistry for
Chemical reaction dynamics, Molecular modeling
and Electronic structure calculations, in
Computer science for Image processing, Realistic
computer graphics (ray tracing), Criminalist
science, Finger print recognition and Face
recognition, in Economics for Labour data
analysis, in Electrical engineering for Stability of
electric circuits, Microchip analysis, Power supply
network optimization, in Finance for Risk analysis
and Value estimation of options,
• In Fluid mechanics for Wind channel and
Turbulence, in Geosciences for Prediction of oil or
ore deposits, Map production, Earth quake
prediction, in Internet for Web search and
Optimal routing , in Linguistics for Automatic
translation, in Materials Science for Microchip
production, Microstructures and Semiconductor
modeling, in Mechanical engineering for Stability
of structures (high rise buildings, bridges, air
planes), Structural optimization and Crash
simulation, in Medicine for Radiation therapy
planning, Computer-aided tomography, Blood
circulation models, in Meteorology for Weather
prediction, Climate prediction (global warming,
what caused the ozone hole), in Music for
Analysis and synthesis of sounds,
• In Neuroscience for Neural networks, Signal
transmission in nerves, in Pharmacology for
Docking of molecules to proteins, Screening of
new compounds, in Physics for Elementary
particle tracking and Laser dynamics, In
Political Sciences for Analysis of elections, in
Space Sciences for Trajectory planning, Flight
simulation and Shuttle re-entry, in Transport
Science for Air traffic scheduling, Taxi for
handicapped people and Automatic pilot for
cars and airplanes.
• Steady state occurs after the system becomes
settled and at the steady state, system starts
working normally.
• Steady state response of control system is a
function of input signal and it is also called as
forced response.
• Steady State VS Transient State: Basically every
system has a transient and a steady state.
• The steady state is the state that is established
after a certain time in your system.
• The transient state is basically between the
beginning of the event and the steady state.
 Fundamentals of Servo Motion
Control
• The fundamental concepts of servo motion
control have not changed significantly in the
last 50 years.
• The basic reasons for using servo systems in
contrast to open loop systems include the
need to improve transient response times,
reduce the steady state errors and reduce the
sensitivity to load parameters.
• Improving the transient response time
generally means increasing the system limits.
• Faster response times mean quicker settling
allowing for higher machine output.
• Reducing the steady state errors relates to
servo system accuracy.
• Finally, reducing the sensitivity to load
parameters means the servo system can
tolerate fluctuations in both input and output
parameters.
• An example of an input parameter fluctuation
is the incoming power line voltage.
• Examples of output parameter fluctuations
include a real time change in load inertia or
mass and unexpected shaft torque
disturbances.
• Servo control in general can be broken into two
fundamental classes of problems.
• The first class deals with command tracking. It
addresses the question of how well does the
actual motion follow what is being commanded.
• The typical commands in rotary motion control
are position, velocity, acceleration and torque.
For linear motion, force is used instead of torque.
• The part of servo control that directly deals with
this is often referred to as “Feedforward” control.
• It can be thought of as what internal
commands are needed such that the user’s
motion commands are followed without any
error, assuming of course a sufficiently
accurate model of both the motor and load is
known.
• The second general class of servo control
addresses the disturbance rejection
characteristics of the system.
• Disturbances can be anything from torque
disturbances on the motor shaft to incorrect
motor parameter estimations used in the
feedforward control.
• The familiar “P.I.D.” (Proportional Integral and
Derivative position loop) and “P.I.V. ”
(Proportional position loop Integral and
proportional Velocity loop) controls are used
to combat these types of problems.
• In contrast to feedforward control, which
predicts the needed internal commands for
zero following error, disturbance rejection
control reacts to unknown disturbances and
modeling errors.
• Complete servo control systems combine both
these types of servo control to provide the
best overall performance.
Hydraulic Piston Servo
 Robot Selection
• According to Bosch Rexroth, the evaluation
criteria you should follow is an outline referred to
as LOSTPED— load, orientation, speed, travel,
precision, environment and duty cycle.

• Using the LOSTPED approach can help you avoid

the common mistake of purchasing a high-
performance SCARA robot with all the features
you may think you need when your application
really only needs a simpler Cartesian system.
• “With a Cartesian robot, the building blocks for a
basic system can be purchased and then later
customized. Mounting brackets, actuators,
motors and controls can be changed as
application needs change,”

• If the workspace is tight, a compact SCARA robot

is likely more suitable. But if space is not an issue,
a simpler Cartesian system can often be built
without all the extra features included in a SCARA
or six-axis design.
• Application complexity is also an issue to
consider. For example, a six-axis robot can move
in all the planes that a human arm does.
• So, for applications where there is a mechanical
interference, such as a box in a corner with parts
inside, a six-axis arm can bend to reach in and
grab that part more easily.

• However, in a pick-and-place application with a

20 kg payload and no need for high accuracy,
both a SCARA and a Cartesian robot could handle
the application. “But a 20 kg payload is at the
upper end of a SCARA robot’s capabilities,
requiring more costly controls and components,”.
• “With a Cartesian robot, a 20 kg payload is no
problem, which makes it possible to save money
by downsizing the mechanics, using smaller
components, and less complex controls.”

• Cartesian robots also make sense when the

application involves long travel spans, he adds.
For example, if the X-axis of robotic travel needs
to be, say, 10 meters for an automated storage
and retrieval system, a gantry system can be
constructed from linear modules. But that length
of travel cannot be handled by a SCARA or six-axis
system.
Computer Controlled Robot
Computer Controlled Robot
 Motion Control
There are two modes to control this robot arm,
first is manual by dragging the track bars that
controls a specific servo such as

• Gripper: click open & close button to open

and close the robot gripper.

• Base: Drag the tracking bar right & left or even

use keyboard arrows (right/left) to move the
base right and left.
• Shoulder: Drag the tracking bar right & left or
even use keyboard arrows (up/down) to move
the shoulder Up and Down

• Elbow: Drag the tracking bar right & left or

even use keyboard keys (w/s) to move the
elbow motor Up and Down
 Motion Record
• Once you want to automate the movement of
the robot arm you have to record the position
in every step by clicking "Rec. Position" Button
or press (R) in keyboard, then the application
will take care of it.
• In every step you record the application
detects the moved motor position and save it
inside a list separately.
• By clicking the button "Start Auto mood" the
application will send those saved positions in
a form of sequence orders.
 Monitoring
• A graphical interface is there that draws every
step in a graph to let the user notes any unusual
change might happen, at the same time there is a
table of data on the right side gives the specific
angle and the accurate time for its move.

• So by using these features, any movements can

be composed and then send it to the Robotic Arm
as a task, as they exactly do in manufacturing
automation.