Sensor

A Sensor is a device, which responds to an input quantity by generating a functionally related output usually in the form of an electrical or optical signal.

What is Sensor Technology?

During the past two decades, there has been an unprecedented growth in the number of products and services, which utilise information gained by monitoring and measuring using different types of sensors. The development of sensors to meet the need is referred to as sensor technology and is applicable in a very broad domain including the environment, medicine, commerce and industry. Governments and policy makers throughout the work are realising the potential benefits of encouraging the growth in sensor technology not only as a result of new technological trends, and hence new products, for the indigenous industry to effect improved product quality and efficiency by broadening the level of control over their processes, but also in support of the implementation and enforcement of government legislation on environmental and safety issues. Such awareness has been copiously demonstrated in the recent UK national Technology Foresight exercise conducted to examine potential opportunities and to promote wealth creation and enhance quality. Through the 15 independent Technology Foresight panels, covering a wide range of industrial sectors, the worldwide need for sensor technology was reinforced. In 13 out of 15 panels, sensor technology was seen as an integral element in the overall development of products and services. In fact it emerged as the key technology to support a wide variety of research and industrial applications.

Classification of measurement errors:
A good sensor obeys the following rules:

• Is sensitive to the measured property only
• Is insensitive to any other property likely to be encountered in its application
• Does not influence the measured property

Ideal sensors are designed to be linear or linear to some simple mathematical function of the measurement, typically logarithmic. The output signal of such a sensor is linearly proportional to the value or simple function of the measured property. The sensitivity is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and has a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linear because the ratio is constant at all points of measurement.
Sensor deviations:
If the sensor is not ideal, several types of deviations can be observed:

• The sensitivity may in practice differ from the value specified. This is called a sensitivity error, but the sensor is still linear.
• Since the range of the output signal is always limited, the output signal will eventually reach a minimum or maximum when the measured property exceeds the limits. The full scale range defines the maximum and minimum values of the measured property.
• If the output signal is not zero when the measured property is zero, the sensor has an offset or bias. This is defined as the output of the sensor at zero input.
• If the sensitivity is not constant over the range of the sensor, this is called non linearity. Usually this is defined by the amount the output differs from ideal behavior over the full range of the sensor, often noted as a percentage of the full range.
• If the deviation is caused by a rapid change of the measured property over time, there is a dynamic error. Often, this behavior is described with a bode plot showing sensitivity error and phase shift as function of the frequency of a periodic input signal.
• If the output signal slowly changes independent of the measured property, this is defined as drift (telecommunication).
• Long term drift usually indicates a slow degradation of sensor properties over a long period of time.
• Noise is a random deviation of the signal that varies in time.
• Hysteresis is an error caused by when the measured property reverses direction, but there is some finite lag in time for the sensor to respond, creating a different offset error in one direction than in the other.
• If the sensor has a digital output, the output is essentially an approximation of the measured property. The approximation error is also called digitization error.
• If the signal is monitored digitally, limitation of the sampling frequency also can cause a dynamic error, or if the variable or added noise changes periodically at a frequency near a multiple of the sampling rate may induce aliasing errors.
• The sensor may to some extent be sensitive to properties other than the property being measured. For example, most sensors are influenced by the temperature of their environment.

All these deviations can be classified as systematic errors or random errors. Systematic errors can sometimes be compensated for by means of some kind of calibration strategy. Noise is a random error that can be reduced by signal processing, such as filtering, usually at the expense of the dynamic behavior of the sensor.
Resolution:

The resolution of a sensor is the smallest change it can detect in the quantity that it is measuring. Often in a digital display, the least significant digit will fluctuate, indicating that changes of that magnitude are only just resolved. The resolution is related to the precision with which the measurement is made. For example, a scanning tunneling probe (a fine tip near a surface collects an electron tunnelling current) can resolve atoms and molecules.
Sensors in nature:

All living organisms contain biological sensors with functions similar to those of the mechanical devices described. Most of these are specialized cells that are sensitive to:

• Light, motion, temperature, magnetic fields, gravity, humidity, moisture, vibration, pressure, electrical fields, sound, and other physical aspects of the external environment
• Physical aspects of the internal environment, such as stretch, motion of the organism, and position of appendages (proprioception)
• Environmental molecules, including toxins, nutrients, and pheromones
• Estimation of biomolecules interaction and some kinetics parameters
• Internal metabolic milieu, such as glucose level, oxygen level, or osmolality
• Internal signal molecules, such as hormones, neurotransmitters, and cytokines
• Differences between proteins of the organism itself and of the environment or alien creatures.

Biosensor:

biosensor

In biomedicine and biotechnology, sensors which detect analytes thanks to a biological component, such as cells, protein, nucleic acid or biomimetic polymers, are called biosensors. Whereas a non-biological sensor, even organic (=carbon chemistry), for biological analytes is referred to as sensor or nanosensor (such a microcantilevers). This terminology applies for both in vitro and in vivo applications. The encapsulation of the biological component in biosensors, presents with a slightly different problem that ordinary sensors, this can either be done by means of a semipermeable barrier, such as a dialysis membrane or a hydrogel, a 3D polymer matrix, which either physically constrains the sensing macromolecule or chemically (macromolecule is bound to the scaffold)

Control engineering

Control engineering or control systems engineering is the engineering discipline that applies control theory to design systems with desired behaviors. The practice uses sensors to measure the output performance of the device being controlled (often a vehicle) and those measurements can be used to give feedback to the input actuators that can make corrections toward desired performance. When a device is designed to perform without the need of human inputs for correction it is called automatic control (such as cruise control for regulating a car's speed). Multi-disciplinary in nature, control systems engineering activities focus on implementation of control systems mainly derived by  mathematical modeling of systems of a diverse range.

Control theory
There are two major divisions in control theory, namely, classical and modern, which have direct implications over the control engineering applications. The scope of classical control theory is limited to single-input and single-output (SISO) system design, except when analyzing for disturbance rejection using a second input. The system analysis is carried out in time domain using differential equations, in complex-s domain with Laplace transform or in frequency domain by transforming from the complex-s domain. Most systems may be assumed to have a second order and single variable system response in the time domain ignoring multi-variable. A controller designed using classical theory often requires on-site tuning due to incorrect design approximations. Yet, due to easier physical implementation of classical controller designs as compared to systems designed using modern control theory, these controllers are preferred in most industrial applications. The most common controllers designed using classical control theory are PID controllers. A less common implementation may include either a Lead or Lag filter, and at times both. The ultimate end is meeting a requirements set typically provided in the time-domain called the Step response, or at times in the frequency domain called the Open-Loop response. The Step response characteristics applied in a specification are typically, percent overshoot, settling time, etc. The Open-Loop response characteristics applied in a specification are typically Gain and Phase margin and bandwidth. These characteristics may be evaluated through simulation including a dynamic model of the system under control coupled with the compensation model.

In contrast, modern control theory is carried out in the state space, and can deal with multi-input and multi-output (MIMO) systems. This overcomes the limitations of classical control theory in more sophisticated design problems, such as fighter aircraft control, with the limitation that no frequency domain analysis is possible. In modern design, a system is represented to the greatest advantage as a set of decoupled first order differential equations defined using state variables. Nonlinear, multivariable, adaptive and robust controltheories come under this division. Matrix methods are significantly limited for MIMO systems where linear independence cannot be assured in the relationship between inputs and outputs. Being fairly new, modern control theory has many areas yet to be explored. Scholars like Rudolf E. Kalman and Aleksandr Lyapunov are well-known among the people who have shaped modern control theory

Control engineering education

At many universities, control engineering courses are taught in Electrical and Electronic Engineering, Mechatronics Engineering,Mechanical engineering, and Aerospace engineering; in others it is connected to computer science, as most control techniques today are implemented through computers, often as embedded systems (as in the automotive field). The field of control within chemical engineering is often known as process control. It deals primarily with the control of variables in a chemical process in a plant. It is taught as part of the undergraduate curriculum of any chemical engineering program, and employs many of the same principles in control engineering. Other engineering disciplines also overlap with control engineering, as it can be applied to any system for which a suitable model can be derived.

Control engineering has diversified applications that include science, finance management, and even human behavior. Students of control engineering may start with a linear control system course dealing with the time and complex-s domain, which requires a thorough background in elementary mathematics and Laplace transform (called classical control theory). In linear control, the student does frequency and time domain analysis. Digital control and nonlinear control courses require z transformation and algebra respectively, and could be said to complete a basic control education. From here onwards there are several sub branches.

WHAT IS MECHATRONICS

Mechatronics is the synergistic combination of Mechanical engineering, Electronic engineering, Computer engineering, Control engineering...

What is Mechatronics Engineering?

Mechatronics refers to the efficient and effective integration of mechanical systems and electronics. It is a blend of mechanics and electronics, and has come to mean the synergistic use of:

• precision engineering
• control theory
• computer science
• mathematics
• sensor technology, to design enhanced or "smart" products, processes and systems.

It integrates the classical fields of mechanical engineering, electrical engineering, mathematics, and computer science/information technology. Its application areas range from power systems to transportation to optical telecommunications to biomedical engineering, along with a host of others. Mechatronic systems are almost everywhere you look.
Mechatronics has enabled the successful realisation of systems such as the Mars Rover that have left the world standing silently in wonder. The Mars Rover was the product of several years of research and development to ensure engineers on earth could effectively control the Rover. It had to be able to negotiate any terrain encountered as well as perform fine motions to grab and sample rock and soil - much like expecting a rugby union prop to also be an accomplished ballet dancer! Of course that call occurred after a brilliant and novel Mars landing strategy was developed.
The future is virtually unlimited for mechatronics, and much of it is here today. Robotic systems and components are already as small as a few microns and researchers are investigating nano-technologies using mechatronic systems for implantation into the human body to repair or replace damaged physiological functions. The next two decades will see an explosion of automated mechatronic systems infiltrating our lives more and more every year - improving our quality of life and our knowledge of the world and universe we live in.

What do Mechatronics Engineers do?

Mechatronics combines mechanical, electrical and software engineering in the design, development and control of diverse systems used in a range of industries including manufacturing, medicine and the service industries. Examples of mechatronic systems include aircraft, dishwashers, motor vehicles, automated manufacturing plants, medical and surgical devices and systems, robots of all types, many toys, artificial organs and many others. Mechatronics engineers are therefore involved in almost every possible industry at levels from applications development to manufacturing to advanced research.

Where do Mechatronics Engineers work?

Graduates with a Mechatronics degree can take up careers in a wide spectrum of industries including robotics, aerospace, chemical, defence and automotive and manufacturing where complex software plays a major role, as well as in businesses that require extensive computer support, such as banking and commerce. Contributions can be made to these industries in a variety of roles including design engineer, software engineer, project planner, product designer and project manager.