What is control system all about?
What is the need of control system?
The reason is because the control is all around us. Control system is everywhere
- homes in refrigerators,ovens etc
- air conditioners to control temperature
- Communication Systems (internet, mobile phones etc)
- process industries( to control the position of machine tools,automobile production etc)
- medical applications(surgery,artificial hearts,prosthetic limbs)
- robotics
- aerospace(autopilot designing,missile control etc)
Control is a common concept, since there always are variables and quantities, which must be made to behave in some desirable way over time.In addition to the engineering systems, variables in biological systems such as the blood sugar and blood pressure in the human body, are controlled by processes that can be studied by the automatic control methods. Similarly, in economic systems variables such as unemployment and inflation, which are controlled by government fiscal decisions can be studied using control methods. Our technological demands today impose extremely challenging and widely varying control problems. These problems range from aircraft and underwater vehicles to automobiles and space telescopes, from chemical processes and the environment to manufacturing, robotics and communication networks.
Control Systems is the the study of how systems move or change over time, and how you can design "control laws", which are mathematical algorithms that can affect that change in ways you want.
For instance, a controls engineer would design an autopilot for an aircraft. He or she would develop a set of differential equations that describe the aircraft's dynamics and the way modifying various control surfaces change the flight characteristics. He or she would then develop mathematical formula that would describe how to modify the control surfaces to get the aircraft to climb, turn, or maintain course.
Control Systems is the the study of how systems move or change over time, and how you can design "control laws", which are mathematical algorithms that can affect that change in ways you want.
For instance, a controls engineer would design an autopilot for an aircraft. He or she would develop a set of differential equations that describe the aircraft's dynamics and the way modifying various control surfaces change the flight characteristics. He or she would then develop mathematical formula that would describe how to modify the control surfaces to get the aircraft to climb, turn, or maintain course.
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| Control system used for designing quad-copters, autopilot,flight control system etc) |
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| Used for designing robotic arms |
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| The satellite pointing direction, or the space vehicle relative position |
But now the question is what is the origin of control systems? How it came? what is the history behind it?
Lets look at the glimpse behind the magic from Ancient Water Clocks to Autonomous Space Vehicles
Automatic control Systems were first developed over two thousand years ago. The first feedback control device on record is thought to be the ancient water clock of Ktesibios in Alexandria Egypt around the third century B.C. It kept time by regulating the water level in a vessel and, therefore, the water flow from that vessel. This certainly was a successful device as water clocks of similar design were still being made in Baghdad when the Mongols captured the city in 1258 A.D. A variety of automatic devices have been used over the centuries to accomplish useful tasks or simply to just entertain. The latter includes the automata, popular in Europe in the 17th and 18th centuries, featuring dancing figures that would repeat the same task over and over again; these automata are examples of open-loop control. Milestones among feedback, or "closed-loop" automatic control devices, include the temperature regulator of a furnace attributed to Drebbel circa 1620, and the centrifugal flyball governor used for regulating the speed of steam engines by James Watt in 1788. In his 1868 paper "On Governors", J. C. Maxwell (who discovered the Maxwell electromagnetic field equations) was able to explain instabilities exhibited by the flyball governor using differential equations to describe the control system. This demonstrated the importance and usefulness of mathematical models and methods in understanding complex phenomena, and signaled the beginning of mathematical control and systems theory. Elements control theory had appeared earlier but not as dramatically and convincingly as in Maxwell's analysis.Control theory made significant strides in the next 100 years. New mathematical techniques made it possible to control, more accurately, significantly more complex dynamical systems than the original fly ball governor. These techniques include developments in optimal control in the 1950's and 1960's, followed by progress in stochastic, robust, adaptive and optimal control methods in the 1970's and 1980's. Applications of control methodology have helped make possible space travel and communication satellites, safer and more efficient aircraft, cleaner auto engines, cleaner and more efficient chemical processes, to mention but a few. A more detailed description of what automatic control systems and its history from early 1900 to the modern control ://ieeecss.org/CSM/library/1996/june1996/02-HistoryofAutoCtrl.pdf
Control system : Present practice of control
A large number of engineering designs involve control system designs . Mostly control feature are embedded in microprocessors which observe the signal from the sensors and provide a command signal to the actuators to take an appropriate action. Application ranges from washing machines,refrigerators to high performance machines used in industries.Not only it is applicable to the industries but also in the control of engines, missiles and aircraft etc.Designers used all computer based simulations to designs the system which includes theoretical algorithms to perform the testing of a system like CAD( Computer Aided Design), MAT-LAB, Lab View,ANSYS etc.Using that, it measures the performance of a system like speed of response, sensitivity, efficiency etc. Control engineering experts keep up with the latest theoretical developments. Most control systems are put together by practical minded engineers who have a thorough understanding of application areas such as automotive engines, factory automation, robot dynamics, heating, ventilating and air conditioning.
Methodology
The first step in understanding the main ideas of control methodology is realizing that we apply control in our everyday life; for instance, when we walk, lift a glass of water, or drive a car. The speed of a car can be maintained rather precisely, by carefully observing the speedometer and appropriately increasing or decreasing the pressure on the gas pedal. Higher accuracy can perhaps be achieved by looking ahead to anticipate road inclines that affect the speed. This is the way the average driver actually controls speed. If the speed is controlled by a machine instead of the driver, then one talks about automatic speed control systems, commonly referred to as cruise control systems. An automatic control system, such as the cruise control system in an automobile, implements in the controller a decision process, also called the control law, that dictates the appropriate control actions to be taken for the speed to be maintained within acceptable tolerances. These decisions are taken based on how different the actual speed is from the desired, called the error, and on the knowledge of the car's response to fuel increases and decreases. This knowledge is typically captured in a mathematical model. Information about the actual speed is fed back to the controller by sensors, and the control decisions are implemented via a device, the actuator, that increases or decreases the fuel flow to the engine.
Foundations and Methods
Central in the control systems area is the study of dynamical systems. In the control of dynamical systems, control decisions are expected to be derived and implemented over real time. Feedback is used extensively to cope with uncertainties about the system and its environment. Feedback is a key concept. The actual values of system variables are sensed, fed back and used to control the system. Hence the control law decision process is based not only on predictions about the plant behavior derived from the system model (as in open-loop control), but also on information about the actual system behavior (closed-loop feedback control).The theory of control systems is based on firm mathematical foundations. The behavior of the system variables to be controlled is typically described by differential or difference equations in the time domain; by Laplace, Z and Fourier transforms in the transform (frequency) domain. There are well understood methods to study stability and mathematical theories from partial differential equations, topology, differential geometry and abstract algebra are sometimes used to study particularly complex phenomena. Control system theory research also benefits other areas, such as Signal Processing, Communications, Bio medical Engineering and Economics. Challenges in Control.The ever increasing technological demands of society impose needs for new, more accurate, less expensive and more efficient control solutions to existing and novel problems. Typical examples are the control demands for passenger aircraft and automobiles. At the same time, the systems to be controlled often are more complex, while less information may be available about their dynamical behavior; for example such is the case in large flexible space structures. The development of control methodologies to meet these challenges will require novel ideas and interdisciplinary approaches, in addition to further developing and refining existing methods.
Future Control Goals
What does the future hold? The future looks bright. We are moving toward control Systems that are able to cope and maintain acceptable performance levels under significant unanticipated uncertainties and failures, systems that exhibit considerable degrees of autonomy. We are moving toward autonomous underwater, land, air and space vehicles; highly automated manufacturing; intelligent robots; highly efficient and fault tolerant voice and data networks; reliable electric power generation and distribution; seismically tolerant structures; and highly efficient fuel control for a cleaner environment. Control systems are decision-making systems where the decisions are based on predictions of future behavior derived via models of the systems to be controlled, and on sensor-obtained observations of the actual behavior that are fed back. Control decisions are translated into control actions using control actuators. Developments in sensor and actuator technology influence control methodology,whichis also influenced by the availability of low cost computational resources.
Put Control in Your Future
The area of controls is challenging and rewarding as our world faces increasingly complex control problems that need to be solved. Immediate needs include control of emissions for a cleaner environment, automation in factories, unmanned space and underwater exploration, and control of communication networks. Control is challenging since it takes strong foundations in engineering and mathematics, uses extensively computer software and hardware and requires the ability to address and solve new problems in a variety of disciplines, ranging from aeronautical to electrical and chemical engineering, to chemistry, biology and economics.
Before going into the deeper side of the system, lets glance over the branches of control system engineering.Here we are going to give a brief listing of the various different methodologies within the sphere of control engineering.
1. Classical Controls
Control methodologies where the ODE's that describe a system are transformed using the Laplace, Fourier, or Z Transforms, and manipulated in the transform domain.
2. Modern Controls
Methods where high-order differential equations are broken into a system of first-order equations. The input, output, and internal states of the system are described by vectors called "state variables".
3. Robust Control
Control methodologies where arbitrary outside noise/disturbances are accounted for, as well as internal inaccuracies caused by the heat of the system itself, and the environment.
4.Optimal Control
In a system, performance metrics are identified, and arranged into a "cost function". The cost function is minimized to create an operational system with the lowest cost.
5. Adaptive Control
In adaptive control, the control changes its response characteristics over time to better control the system.
6. Nonlinear Control
The youngest branch of control engineering, nonlinear control encompasses systems that cannot be described by linear equations or ODE's, and for which there is often very little supporting theory available.
7. Game Theory
Game Theory is a close relative of control theory, and especially robust control and optimal control theories. In game theory, the external disturbances are not considered to be random noise processes, but instead are considered to be "opponents". Each player has a cost function that they attempt to minimize, and that their opponents attempt to maximize.
Branches Of Control System Engineering
Before going into the deeper side of the system, lets glance over the branches of control system engineering.Here we are going to give a brief listing of the various different methodologies within the sphere of control engineering.
8. Fuzzy Control
It is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).
9. Model Predictive Control
It is an advanced method of process control that has been in use in the process industries in chemical plants and oil refineries since the 1980's. In recent years it has also been used in power system balancing models.Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification.
It is a control system based on fuzzy logic—a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).
9. Model Predictive Control
It is an advanced method of process control that has been in use in the process industries in chemical plants and oil refineries since the 1980's. In recent years it has also been used in power system balancing models.Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification.
The mathematical concepts which are used in the control systems are as follows:
1. Complex Analysis: Complex analysis, traditionally known as the theory of functions of a complex variable, is the branch of mathematical analysis that investigates functions of complex numbers. It is useful in many branches of mathematics, including algebraic geometry, number theory etc.
2. Differential Equation: A differential equation is a mathematical equation that relates some function with its derivatives. In applications, the functions usually represent physical quantities, the derivatives represent their rates of change, and the equation defines a relationship between the two. Because such relations are extremely common, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology.
3. Matrices: It is a rectangular array of numbers or symbols arranged in the rows and columns.
4. Calculus: Calculus of variations is a field of mathematical analysis that deals with maximizing or minimizing functions, which are mappings from a set of functions to the real numbers. Functions are often expressed as definite integrals involving functions and their derivatives. The interest is in external functions that make the functional attain a maximum or minimum value – or stationary functions – those where the rate of change of the functional is zero.
5. Linear Algebra:Linear algebra is the branch of mathematics concerning vector spaces and linear mappings between such spaces. It includes the study of lines, planes, and subspaces, but is also concerned with properties common to all vector spaces.
6. Real Analysis- Real analysis (traditionally, the theory of functions of a real variable) is a branch of mathematical analysis dealing with the real numbers and real-valued functions of a real variable. In particular, it deals with the analytic properties of real functions and sequences, including convergence and limits of sequences of real numbers, the calculus of the real numbers, and continuity, smoothness and related properties of real-valued functions.
