1. Chapter 1
Elements of Mechatronic Systems
1.1 Introduction
The word mechatronics is composed of “mecha” from mechanism and the “tron-
ics” from electronics. It is the synergistic integration of mechanical engineering,
with electronics and intelligent computer control in the design and manufacturing of
industrial products and processes. Mechatronics involves:
• Implementing electronics control in a mechanical system.
• Enhancing existing mechanical design with intelligent control.
• Replacing mechanical component with an electronic solution.
The growth of mechatronic systems has been fuelled by the growth in the con-
stituent areas. Advancements in traditional disciplines has also fuelled the growth
of mechatronics systems by providing technologies. For example, the invention of
the microprocessor had effected a lot on the redesign of mechanical systems and
design of new mechatronics systems. We can recall the spring-driven table clocks.
These have been replaced by microprocessor-based table clocks. There are many
examples of intelligent systems in all walks of life, including smart home appliances
such as dishwashers, vacuum cleaners, microwaves, and wireless network-enabled
devices. However it is the automobile market which has been the motivation for
the development of mechatronic systems. Before we move further let us see the
principal mechatronic components. The mechatronic components essentially consist
of actuators, sensors, input signal conditioning and interfacing unit, digital control
architecture, output signal conditioning and interfacing, and displays. The signal
flows among these elements is shown in Fig. 1.1. In the following sections, we
discuss about some of these mechatronic components and the issues involved in
mechatronic system design and implementation.
R. Merzouki et al., Intelligent Mechatronic Systems, 3
DOI: 10.1007/978-1-4471-4628-5_1, © Springer-Verlag London 2013

2. 4 1 Elements of Mechatronic Systems
Fig. 1.1 Various elements of
Input signal
a mechatronic system Actuators Sensors conditioning and
interfacing
Output signal
conditioning and Digital control
interfacing architecture
Displays
1.2 Actuators
Actuators are mechanical devices for moving or controlling something. These are
responsible for transformation of output of a microprocessor into a controlling action
on machine or device. For example electrical outputs of controller transforms into
linear motion of a load or say electrical output of controller transforms into an action
which controls the amount of liquid passing along a pipe. The actuators can be
classified as
• Electric Motors and Drives: These actuators transform electrical energy into
mechanical energy. There are various types of electric actuators, such as
– DC Motors
– AC Motors
– Linear Motors
– Stepper Motors
• Hydraulic Drives
• Pneumatic Drives
• Internal Combustion hybrids
• Actuators of the future.
1.3 Sensors
Sensors are elements of a mechatronic system which produce signals relating to the
quantities being measured. Let us take an example of electrical resistance thermome-
ter. The quantity being measured here is temperature. The sensor transforms it into
change of resistance of the sensor material. Examples of sensors include switches,
potentiometers, thermocouples, strain gauges, digital encoders, and accelerometers
and micro-electromechanical systems.
A transducer is an element that when subjected to some physical change experi-
ences a related change. Thus we can say that sensors are transducers.

3. 1.4 Input Signal Conditioning and Interfacing 5
1.4 Input Signal Conditioning and Interfacing
An important question is why do we require signal conditioning? The simple answer
is because we want to feed the sensor data to a microprocessor. The signal condi-
tioning process provides protection; it ensures that we get the signal to be of right
type and of right level. The signal conditioning process also eliminates or reduces
the noise. Sometimes we require signal manipulation for making it linear function
of some variable.
Interfacing is needed because we want to connect peripheral devices such as
sensors, keyboards, actuators, etc., to microprocessor. Due to different signal forms
and levels they cannot be directly connected.
We can list some basic interface requirements as
1. Electrical buffering/isolation—when peripherals operate at a different current or
voltage than the microprocessor bus system or ground references are different.
2. Timing control—when the data transfer rates of the peripheral and the micro-
processor are different, i.e., interfacing a microprocessor to a slower peripheral.
This can be achieved by using special lines between the microprocessor and the
peripheral to control the transfer of data. Such lines are called handshake lines
and the process is called handshaking.
3. Code conversion—when codes used by peripherals are different to that used by
microprocessor.
4. Changing the number of lines—when microprocessor uses different bits such as
4, 8, or 16 bits. This determines the number of lines in microprocessor data bus.
Note that peripherals may have different number of lines.
5. Serial to parallel and vice versa data transfer—a 8 bit microprocessor manipulates
8 bits of data at same time. This can be done by parallel data transfer where all
data are send simultaneously whereas in serial data transfer signals are sent one
by one.
6. Conversion from analog to digital and vice versa—when both are used together.
The examples of input signal conditioning and interfacing are discrete circuits,
analog to digital converters, digital to analog converters, filters, and amplifiers.
1.5 Digital Control Architecture
Microprocessor system has three parts (i) Central Processing Unit (CPU), (ii) Input
and Output interfaces, and (iii) Memory.
Microprocessor having memory, input and output arrangements all on same chip
are microcontrollers. Digital control architecture may include components such as
different logic circuits, microcontrollers, control algorithms, program logic con-
trollers, etc.

4. 6 1 Elements of Mechatronic Systems
1.6 Output Signal Conditioning and Interfacing
The components of this system may include digital to analog, analog to digital con-
verter, amplifiers, power transistors, power op amps, pulse width modulation, etc.
1.7 Displays
A display provides visual feedbacks to user based on which he/she can take some
decisions. The examples of displays include LED’s, digital display, cathode ray tubes,
liquid crystal displays, etc.
1.8 Intelligent System
We can define an intelligent system as a system that learns during its existence. Or
we can say that the system senses its environment with the help of sensors; it learns,
what action for each situation it should take so that it achieves its objectives. Thus,
an intelligent system can be defined only if the system exists along with surrounding,
with which it interacts. The system must be able to receive communications from
the surrounding. This communication is for transmitting information. An intelligent
system must have an objective and should have ability to check whether the last
action which it performed has been able to move it closer to its objective or not.
1.9 Reconfigurable Systems
A process may continue to operate as long as all of its critical faults can be detected
and it remains observable and controllable. A reconfigurable system is one which can
accommodate the faults using the redundancies present in the system. The redundan-
cies are usually in the form of additional actuators and sensors present in the system
than the minimum required.
The fault indicators, fault signature analysis and the fault isolation (root cause
analysis) schemes must be modified every time a system is reconfigured. The func-
tional services offered by the components are organized into coherent subsets called
Operating Modes (OM), where each OM is associated to a functional model. Automa-
tion specifies the conditions to change from one OM to another. Theoretically, a
process can operate normally, as long as at least one device is available for each
basic function. When a device fails, the branch associated with it is removed and the
system is reconfigured using the next device, according to a defined hierarchy.

5. 1.10 Autonomous Supervisory Control 7
Signal
Digital control conditioning Actuators
architecture and interfacing
Operator Plant
Signal
Displays conditioning Sensors
and interfacing
Fig. 1.2 Schematic representation of supervisory control
Signal
Digital control conditioning Actuators
architecture and interfacing
Operator Plant
Signal
Displays conditioning Sensors
and interfacing
Fig. 1.3 Schematic representation of autonomous supervisory control
1.10 Autonomous Supervisory Control
The supervisory control is derived from the supervisor’s control of the subordinate
staff in a manufacturing plant. The supervisory control in mechatronic system means
that provision for human operator intervention in a control process exists. The inter-
vention may be in the form of person programming intermittently and receiving data
from a computer which is connected through various sensors in a controlled process.
The controlled process can be the trajectory following action to be carried by tip
of a manipulator. In case of autonomous supervisory control, human intervention is
not present. The schematic representation of supervisory control and autonomous
supervisory control are shown in Figs. 1.2 and 1.3, respectively.
1.11 Artificial Intelligence
Humans demonstrate intelligence by communicating effectively and by learning.
Artificial intelligence (AI) is the field of study for simulation of human behavior and
cognitive process on a computer. It is the study of the nature of the whole space of
intelligent minds. AI takes help of computational techniques for performing job that
requires intelligence when performed by humans. The issue of AI involves knowl-
edge representation, search, perception, and inference. An intelligent machine has
built-in capability to reason. They perform functions that require intelligence when
performed by people. Intelligent system can be constructed from explicit, declarative
knowledgebases, operated by general, formal reasoning mechanism. They have the

6. 8 1 Elements of Mechatronic Systems
ability to synthesize discrete pieces of information in creating a new understanding
of any problem and its possible solution. Some important AI terminologies are
• Perception: It is the collection of information using sensors by the intelligent
system and organization of gathered information so that decisions can be taken.
• Reasoning: It is the process of going from what is known to what is unknown. The
reasoning can be either deterministic or nondeterministic. Deterministic reasoning
uses if-then rules whereas nondeterministic reasoning makes predictions based on
probability.
• Learning: It is the adoption of the environment based on experience by the intel-
ligent system.
Important AI systems are
(i) Expert system
(ii) Fuzzy system
(iii) Artificial neural network
(iv) Genetic algorithm
(v) Evolutionary programming
(vi) Ant colony intelligent system
(vii) Particle swam intelligent system.
1.12 Knowledgebase
A knowledgebase is a special kind of database for knowledge management [1],
providing the means for the computerized collection, organization, and retrieval of
knowledge. It aims to provide right information at right moment. Knowledgebases
are categorized into two major types:
Machine-readable knowledgebases store knowledge in a computer-readable form,
usually for the purpose of having automated deductive reasoning applied to them.
They contain a set of data, often in the form of rules that describe the knowledge in
a logically consistent manner. An ontology can define the structure of stored data—
what types of entities are recorded and what their relationships are. Logical operators,
such as And (conjunction), Or (disjunction), material implication, and negation may
be used to build it up from simpler pieces of information. Consequently, classical
deduction can be used to reason about the knowledge in the knowledgebase. Some
machine-readable knowledgebases are used with artificial intelligence, for example
as part of an expert system that focuses on a domain-like prescription drugs or
customs law.
Human-readable knowledgebases are designed to allow people to retrieve and use
the knowledge they contain. They are commonly used to complement a help desk
or for sharing information among employees within an organization. They might
store troubleshooting information, articles, white papers, user manuals, or answers

7. 1.12 Knowledgebase 9
to frequently asked questions. Typically, a search engine is used to locate information
in the system, or users may browse through a classification scheme.
Any smart control system includes a knowledgebase [3]. Information on the
mechatronic systems that include the equipment being controlled, as well as the
controllable process, may contain errors and inconsistencies and may be incom-
plete. In other words, knowledge regarding mechatronic systems is characterized
by indeterminacy. In constructing a knowledgebase, the main criterion is minimiza-
tion of the following components of the indeterminacy: incompleteness, inadequacy
(errors), and inconsistency. Another goal is to minimize the redundancy of the data in
the knowledgebase. Accordingly, a method of formalized representation of knowl-
edge regarding the mechatronic system must not only include representation of the
relevant knowledge but also determination and modeling of the indeterminacies in
the knowledge. Thus, mechatronic system modeling may help in enriching the exist-
ing knowledgebase of the mechatronic system. Information from the knowledgebase
may be used in the synthesis, control, prediction, and diagnostic systems for mecha-
tronic systems. It also helps in creating systems to support design and technological
decision making, and in the operation of mechatronic systems.
The integration of mechatronic systems can be performed by the components [2]
(hardware-integration) and by information processing (software-integration). The
information processing consists of low-level and high-level feedback control, super-
vision and diagnosis, and general process management. Special signal processing,
model-based, and adaptive methods are applied. With the aid of a knowledgebase
and inference mechanisms mechatronic systems with increasing intelligence will be
developed.
The integration by information processing (software integration) is mostly based
on advanced control functions. Besides a basic feedforward and feedback control
an additional influence may take place through the process knowledge and corre-
sponding online information processing. This means a processing of available sig-
nals in higher levels. This includes the solution of tasks such as supervision with
fault diagnosis, optimization and general process management. The respective prob-
lem solutions result in real-time algorithms which must be adapted to the mechani-
cal process properties, for example expressed by mathematical models in the form
of static characteristics, differential equations, etc. Therefore, a knowledgebase is
required for organizing the methods for design and information gaining, process
models, and performance criteria. By this way the mechanical parts are governed in
various ways through higher level information processing with intelligent properties,
possibly including learning, thus forming an integration by process adapted software.
The knowledgebase contains quantitative and qualitative knowledge. The quan-
titative part operates with analytic (mathematical) process models, parameter and
state estimation methods, analytic design methods (e.g., for control and fault detec-
tion), and quantitative optimization methods. Similar modules hold for the qualitative
knowledge, e.g., in form of rules (fuzzy and soft computing). Further knowledge is
the past history in the memory and the possibility to predict the behavior. Finally,
tasks or schedules may be included.

8. 10 1 Elements of Mechatronic Systems
The knowledgebase consists of mathematical process models, parameter estima-
tion, and controller design methods and control performance criteria. The feedback
control is organized in lower level and higher level controllers, a reference value
generation module and controller parameter adaptation. With this structure the main
control functions of mechatronic systems can be organized.
1.13 Decision Support System
A decision support system (DSS) for a mechatronic system is a computer-based
information system that supports organizational decision-making activities. DSSs
may serve the operations, and planning levels of a microprocessor and help to make
decisions, which may be rapidly changing and not easily specified in advance. DSSs
include knowledgebased systems. A properly designed DSS for a mechatronic system
may be software-based system intended to help compile useful information from a
combination of raw data, or system models to identify and solve problems and make
decisions. Let us take an example. Say a walking robot is moving in a straight line
but if some obstruction comes in path of the walking robot then decision support
system must be able to gather the data from sensor such as infrared and accordingly
actuate the motors so that the path of the robot is changed and obstruction is not
encountered.
1.14 Diagnosis
Diagnosis in a mechatronic system refers to identification of nature and cause for
failures of mechatronic components, say actuators, sensors, and microcontrollers.
1.15 Fault, Failure, and Safety
Modern technological systems rely on sophisticated control systems to meet increased
performance and safety requirements. Over the last three decades, the growing
demand for identification of fault, resulting failure and safety concerns from such
failure has attracted lot of research in the area. Apart from safety, reliability, maintain-
ability, and survivability in technical systems has drawn significant research in Fault
Detection and Diagnosis (FDD). Such efforts have led to the development of many
FDD techniques. On the other hand, research on reconfigurable fault-tolerant con-
trol systems has increased progressively since the initial research on restructurable
control and self-repairing flight control systems began in the early 1980s.

9. 1.16 Fault Tolerance 11
1.16 Fault Tolerance
Fault-tolerant control has proved to be a powerful tool for improving the safety in
mechatronic system. Modern technological systems rely on sophisticated control
systems to meet increased performance and safety requirements. A conventional
feedback control design for a complex system may result in an unsatisfactory per-
formance, or even instability, in the event of malfunctions in actuators, sensors or
other system components. To overcome such weaknesses, new approaches to con-
trol system design have been developed in order to tolerate component malfunctions
while maintaining desirable stability and performance properties. This is particu-
larly important for safety-critical systems, such as aircraft, spacecraft, nuclear power
plants, and specially automobile and mechatronics systems. In such systems, the con-
sequences of a minor fault in a system component can be catastrophic. Therefore,
the demand on reliability, safety and fault tolerance is generally high. It is necessary
to design control systems which are capable of tolerating potential faults in these
systems in order to improve the reliability and availability while providing a desir-
able performance. These types of control systems are often known as fault-tolerant
control systems (FTCS). More precisely, FTCS are control systems which possess
the ability to accommodate component failures automatically. They are capable of
maintaining overall system stability and acceptable performance in the event of such
failures.
Fault-Tolerant Control (FTC) relates to recovery from fault such that the system
is controlled under actual constraints without replacing part(s) of the faulty system.
FTC approaches can be classified into two categories: passive approach (e.g., robust
control) and active approach (e.g., adaptive control). In active FTC, plant faults
are diagnosed and estimated and subsequently the controller is redesigned for fault
accommodation. Historically, from the point of view of practical application, a signif-
icant amount of research on fault-tolerant control systems was motivated by aircraft
flight control system designs. The goal was to provide “self-repairing” capability in
order to ensure a safe landing in the event of severe faults in the aircraft. Increased
air traffic has necessitated the need for fault-tolerant flight control systems. Fault
tolerance is no longer limited to high-end systems, and consumer products, such as
automobiles. It is increasingly dependent on microelectronic/mechatronic systems,
on-board communication networks, and software, thus requiring new techniques for
achieving fault tolerance.
1.17 Examples of Mechatronic Systems
1.17.1 A Copy Machine
It is an excellent example of mechatronic system. It has analog and digital circuits,
sensors, actuators, and microprocessors. The working of copy machine can be stated
as follows:

10. 12 1 Elements of Mechatronic Systems
1. User places an original in a loading bin and pushes a button to start the process.
2. The original is transported to the platen glass.
3. A high-intensity light source scans the original and transfers the corresponding
image as a charge distribution to a drum.
4. Blank piece of paper is retrieved from loading cartridge, and image is transferred
onto the paper with an electrostatic deposition of ink toner powder that is heated
to bond to the paper.
5. A sorting mechanism then delivers the copy to an appropriate bin.
Now, let us see the mechatronic components in copy machine.
1. Actuators: In a copy machine servomotor or stepper motor is used as actuator.
The principal job of actuator here is to load and transport the paper, turn the drum,
and index the drum.
2. Sensors: Optical sensors and microswitches detect the presence or absence of
paper, its proper positioning, and check whether or not door and latches are in
proper position. Encoders are used to track motor rotation.
3. Control: Analog circuits control the lamp, heater, and other power circuit. Digital
circuit controls digital display, indicator lights, buttons, and switches, forming the
user interface. Other digital circuits used include logic circuit and microprocessor
that coordinates all the functions of the machine.
1.17.2 Walking Robot
A walking robot is another good example of mechatronic system. It has following
mechatronic components:
1. Actuators: Servo motors or direct current (DC) motor or stepper motor can be
used as an actuator to propel the legs and body.
2. Sensors: A walking robot may have many sensors depending upon the level of
intelligence required in it. Some of the sensors can be
• Infrared (IR) sensor for obstruction detection.
• Bumper sensor for obstruction detection.
• Compass for orientation detection.
• Accelerometer for tilt detection.
• Ultrasonic sensor for range detection.
3. Micro Controller: One can use any microcontroller such as PIC, ATMega for
coordinating the activity of actuators and sensors.

11. 1.18 Why Mechatronics System Simulation? 13
1.18 Why Mechatronics System Simulation?
Mechatronic system designs are complex by nature, and are becoming more complex
day by day. As the number of system’s peripheral components grow to accommodate
ever increasing demands for functionality and performance from consumers, the
system design must integrate analog and digital hardware, as well as the software that
controls them. As mechatronic system integrates different components its behavior
is determined by interdependencies between different components. Therefore, an
integrated and interdisciplinary engineering approach is necessary. So, engineers
must be assisted by tools which allow a systems analysis with respect to capabilities,
capacities, and behavior without really constructing the system. This necessitates an
appropriate modeling and simulation tool for mechatronic systems.
A mechatronic system design requires an integrated modeling and simulation
approach where the whole system needs to be designed together to meet the desired
performance specifications. The first level of modeling is called a conceptual model.
The concept of a new product needs to be validated before additional resources are
allocated to design and fabricate that product. Simulation is great tool for concept
validation. Once the conceptual model has been validated, the system level design
goes a step further where one determines the constraints on integration of components
of the system. These constraints relate to the specifications for various components
such as the power requirements in the actuators (called actuator sizing) and sensor
limits.
The next step in the design of an autonomous mechatronic system involves inte-
gration of the control system model with the system model. Besides the selection
of control laws, the control system parameters have to be tuned in this stage so that
the performance specifications are met with desired accuracy. The controller design
also involves selection and placement of sensors and actuators in the system. Note
that the actuator specifications cannot be determined if the system is not modelled
with its control laws; the actuator must be able to deliver the desired output dictated
by the controller. Moreover, if the sensor response time and feedback delay etc. are
not accounted for in the model then one might get a wrong design. Thus, the system
model needs integration of all components of the mechatronic system so that the
actuator, sensor and system dynamics are all accounted for during the design stage.
The detailed design of components is done after the system-level design and con-
trol integration has been validated through simulation. Note that initial system level
design uses gross or approximate parameter values. The detailed design accounts
for further constraints such as the mechanical strength of components (load limit,
fatigue life, etc.), geometric or assembly compatibilities, logistical issues (electrical
or hydraulic power delivery lines) and packaging of electronic components (cool-
ing system, heat exchangers, etc.). The detailed design gives more accurate estima-
tion of system parameter values which have to be again used in the system level
model. An iterative process then converges to the final system design which will be
used to fabricate the mechatronic system. The computerized modeling and simula-
tion to evolve a product design called virtual prototyping. Like manufacturing of a

12. 14 1 Elements of Mechatronic Systems
physical product can be optimized through rapid prototyping tools, the virtual pro-
totyping through modeling and simulation offers a solution to quick, maintainable,
optimized, and evolving product design. With availability of a virtual prototype of a
product, it becomes easier to perform product redesign or enhancement.
1.19 Future of Mechatronics
We can expect continued advancements in cost-effective actuators, sensors, micro-
processors and microcontrollers development enabled by advancements in
applications of microelectromechanical systems (MEMS), adaptive control method-
ologies, real-time programming methods, networking and wireless technologies, and
software tools for advanced system modeling, virtual prototyping, and testing. The
Internet when utilized in combination with wireless technology, may also lead to new
mechatronic products. While developments in automobile technology provide vivid
examples of mechatronics development, there are numerous examples of intelligent
systems in all walks of life. In area of medical science we can expect advances in
robot-assisted surgery, in vivo robots and implantable sensors and actuators. Other
areas that will benefit from mechatronic advances may include robotics, manufac-
turing, space technology, underwater exploration, and transportation.
References
1. http://en.wikipedia.org/wiki/knowledgebase. Accessed 03 July 2012
2. R. Isermann, Mechatronic systems—a challenge for control engineering. in Proceedings of the
American Control Conference, pp. 2617–2632, Albuquerque, New Mexico, June 1997
3. V.Ts. Zoriktuev, S.G. Goncharova, I.F. Mesyagutov, Representation and derivation of knowl-
edge in the control systems of mechatronic machine-tool systems. Russ. Eng. Res. 28, 177–181
(2008)