Control theory

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https://en.wikipedia.org/wiki/Control_theory

Control theory deals with the control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any delay, overshoot, or steady-state error and ensuring a level of control stability; often with the aim to achieve a degree of optimality.

To do this, a controller with the requisite corrective behavior is required. This controller monitors the controlled process variable (PV), and compares it with the reference or set point (SP). The difference between actual and desired value of the process variable, called the error signal, or SP-PV error, is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Other aspects which are also studied are controllability and observability. This is the basis for the advanced type of automation that revolutionized manufacturing, aircraft, communications and other industries. This is feedback control, which involves taking measurements using a sensor and making calculated adjustments to keep the measured variable within a set range by means of a "final control element", such as a control valve.

Extensive use is usually made of a diagrammatic style known as the block diagram. In it the transfer function, also known as the system function or network function, is a mathematical model of the relation between the input and output based on the differential equations describing the system.

Control theory dates from the 19th century, when the theoretical basis for the operation of governors was first described by James Clerk Maxwell. Control theory was further advanced by Edward Routh in 1874, Charles Sturm and in 1895, Adolf Hurwitz, who all contributed to the establishment of control stability criteria; and from 1922 onwards, the development of PID control theory by Nicolas Minorsky. Although a major application of mathematical control theory is in control systems engineering, which deals with the design of process control systems for industry, other applications range far beyond this. As the general theory of feedback systems, control theory is useful wherever feedback occurs - thus control theory also has applications in life sciences, computer engineering, sociology and operations research.

History

Centrifugal governor in a Boulton & Watt engine of 1788

Although control systems of various types date back to antiquity, a more formal analysis of the field began with a dynamics analysis of the centrifugal governor, conducted by the physicist James Clerk Maxwell in 1868, entitled On Governors. A centrifugal governor was already used to regulate the velocity of windmills. Maxwell described and analyzed the phenomenon of self-oscillation, in which lags in the system may lead to overcompensation and unstable behavior. This generated a flurry of interest in the topic, during which Maxwell's classmate, Edward John Routh, abstracted Maxwell's results for the general class of linear systems. Independently, Adolf Hurwitz analyzed system stability using differential equations in 1877, resulting in what is now known as the Routh–Hurwitz theorem.

A notable application of dynamic control was in the area of crewed flight. The Wright brothers made their first successful test flights on December 17, 1903, and were distinguished by their ability to control their flights for substantial periods (more so than the ability to produce lift from an airfoil, which was known). Continuous, reliable control of the airplane was necessary for flights lasting longer than a few seconds.

By World War II, control theory was becoming an important area of research. Irmgard Flügge-Lotz developed the theory of discontinuous automatic control systems, and applied the bang-bang principle to the development of automatic flight control equipment for aircraft. Other areas of application for discontinuous controls included fire-control systems, guidance systems and electronics.

Sometimes, mechanical methods are used to improve the stability of systems. For example, ship stabilizers are fins mounted beneath the waterline and emerging laterally. In contemporary vessels, they may be gyroscopically controlled active fins, which have the capacity to change their angle of attack to counteract roll caused by wind or waves acting on the ship.

The Space Race also depended on accurate spacecraft control, and control theory has also seen an increasing use in fields such as economics and artificial intelligence. Here, one might say that the goal is to find an internal model that obeys the good regulator theorem. So, for example, in economics, the more accurately a (stock or commodities) trading model represents the actions of the market, the more easily it can control that market (and extract "useful work" (profits) from it). In AI, an example might be a chatbot modelling the discourse state of humans: the more accurately it can model the human state (e.g. on a telephone voice-support hotline), the better it can manipulate the human (e.g. into performing the corrective actions to resolve the problem that caused the phone call to the help-line). These last two examples take the narrow historical interpretation of control theory as a set of differential equations modeling and regulating kinetic motion, and broaden it into a vast generalization of a regulator interacting with a plant.

Open-loop and closed-loop (feedback) control

A block diagram of a negative feedback control system using a feedback loop to control the process variable by comparing it with a desired value, and applying the difference as an error signal to generate a control output to reduce or eliminate the error.

 

Example of a single industrial control loop; showing continuously modulated control of process flow.

Fundamentally, there are two types of control loops: open loop control and closed loop (feedback) control.

In open loop control, the control action from the controller is independent of the "process output" (or "controlled process variable" - PV). A good example of this is a central heating boiler controlled only by a timer, so that heat is applied for a constant time, regardless of the temperature of the building. The control action is the timed switching on/off of the boiler, the process variable is the building temperature, but neither is linked.

In closed loop control, the control action from the controller is dependent on feedback from the process in the form of the value of the process variable (PV). In the case of the boiler analogy, a closed loop would include a thermostat to compare the building temperature (PV) with the temperature set on the thermostat (the set point - SP). This generates a controller output to maintain the building at the desired temperature by switching the boiler on and off. A closed loop controller, therefore, has a feedback loop which ensures the controller exerts a control action to manipulate the process variable to be the same as the "Reference input" or "set point". For this reason, closed loop controllers are also called feedback controllers.

The definition of a closed loop control system according to the British Standard Institution is "a control system possessing monitoring feedback, the deviation signal formed as a result of this feedback being used to control the action of a final control element in such a way as to tend to reduce the deviation to zero."

Likewise; "A Feedback Control System is a system which tends to maintain a prescribed relationship of one system variable to another by comparing functions of these variables and using the difference as a means of control."

Other examples

An example of a control system is a car's cruise control, which is a device designed to maintain vehicle speed at a constant desired or reference speed provided by the driver. The controller is the cruise control, the plant is the car, and the system is the car and the cruise control. The system output is the car's speed, and the control itself is the engine's throttle position which determines how much power the engine delivers.

A primitive way to implement cruise control is simply to lock the throttle position when the driver engages cruise control. However, if the cruise control is engaged on a stretch of non-flat road, then the car will travel slower going uphill and faster when going downhill. This type of controller is called an open-loop controller because there is no feedback; no measurement of the system output (the car's speed) is used to alter the control (the throttle position.) As a result, the controller cannot compensate for changes acting on the car, like a change in the slope of the road.

In a closed-loop control system, data from a sensor monitoring the car's speed (the system output) enters a controller which continuously compares the quantity representing the speed with the reference quantity representing the desired speed. The difference, called the error, determines the throttle position (the control). The result is to match the car's speed to the reference speed (maintain the desired system output). Now, when the car goes uphill, the difference between the input (the sensed speed) and the reference continuously determines the throttle position. As the sensed speed drops below the reference, the difference increases, the throttle opens, and engine power increases, speeding up the vehicle. In this way, the controller dynamically counteracts changes to the car's speed. The central idea of these control systems is the feedback loop, the controller affects the system output, which in turn is measured and fed back to the controller.

Classical control theory

Main article: Classical control theory

To overcome the limitations of the open-loop controller, control theory introduces feedback. A closed-loop controller uses feedback to control states or outputs of a dynamical system. Its name comes from the information path in the system: process inputs (e.g., voltage applied to an electric motor) have an effect on the process outputs (e.g., speed or torque of the motor), which is measured with sensors and processed by the controller; the result (the control signal) is "fed back" as input to the process, closing the loop.

Closed-loop controllers have the following advantages over open-loop controllers:

• disturbance rejection (such as hills in the cruise control example above)
• guaranteed performance even with model uncertainties, when the model structure does not match perfectly the real process and the model parameters are not exact
• unstable processes can be stabilized
• reduced sensitivity to parameter variations
• improved reference tracking performance

In some systems, closed-loop and open-loop control are used simultaneously. In such systems, the open-loop control is termed feedforward and serves to further improve reference tracking performance.

A common closed-loop controller architecture is the PID controller.

Closed-loop transfer function

Further information: closed-loop transfer function

The output of the system y(t) is fed back through a sensor measurement F to a comparison with the reference value r(t). The controller C then takes the error e (difference) between the reference and the output to change the inputs u to the system under control P. This is shown in the figure. This kind of controller is a closed-loop controller or feedback controller.

This is called a single-input-single-output (SISO) control system; MIMO (i.e., Multi-Input-Multi-Output) systems, with more than one input/output, are common. In such cases variables are represented through vectors instead of simple scalar values. For some distributed parameter systems the vectors may be infinite-dimensional (typically functions).

If we assume the controller C, the plant P, and the sensor F are linear and time-invariant (i.e., elements of their transfer function C(s), P(s), and F(s) do not depend on time), the systems above can be analysed using the Laplace transform on the variables. This gives the following relations:

${\displaystyle Y(s)=P(s)U(s)}$

${\displaystyle U(s)=C(s)E(s)}$

${\displaystyle E(s)=R(s)-F(s)Y(s).}$

Solving for Y(s) in terms of R(s) gives

${\displaystyle Y(s)=\left({\frac {P(s)C(s)}{1+P(s)C(s)F(s)}}\right)R(s)=H(s)R(s).}$

The expression ${\displaystyle H(s)={\frac {P(s)C(s)}{1+F(s)P(s)C(s)}}}$ is referred to as the closed-loop transfer function of the system. The numerator is the forward (open-loop) gain from r to y, and the denominator is one plus the gain in going around the feedback loop, the so-called loop gain. If ${\displaystyle |P(s)C(s)|\gg 1}$, i.e., it has a large norm with each value of s, and if ${\displaystyle |F(s)|\approx 1}$, then Y(s) is approximately equal to R(s) and the output closely tracks the reference input.

PID feedback control

Main article: PID controller

 

A block diagram of a PID controller in a feedback loop, r(t) is the desired process value or "set point", and y(t) is the measured process value.

A proportional–integral–derivative controller (PID controller) is a control loop feedback mechanism control technique widely used in control systems.

A PID controller continuously calculates an error value e(t) as the difference between a desired setpoint and a measured process variable and applies a correction based on proportional, integral, and derivative terms. PID is an initialism for Proportional-Integral-Derivative, referring to the three terms operating on the error signal to produce a control signal.

The theoretical understanding and application dates from the 1920s, and they are implemented in nearly all analogue control systems; originally in mechanical controllers, and then using discrete electronics and later in industrial process computers. The PID controller is probably the most-used feedback control design.

If u(t) is the control signal sent to the system, y(t) is the measured output and r(t) is the desired output, and e(t) = r(t) − y(t) is the tracking error, a PID controller has the general form

${\displaystyle u(t)=K_{P}e(t)+K_{I}\int ^{t}e(\tau ){\text{d}}\tau +K_{D}{\frac {{\text{d}}e(t)}{{\text{d}}t}}.}$

The desired closed loop dynamics is obtained by adjusting the three parameters K_P, K_I and K_D, often iteratively by "tuning" and without specific knowledge of a plant model. Stability can often be ensured using only the proportional term. The integral term permits the rejection of a step disturbance (often a striking specification in process control). The derivative term is used to provide damping or shaping of the response. PID controllers are the most well-established class of control systems: however, they cannot be used in several more complicated cases, especially if MIMO systems are considered.

Applying Laplace transformation results in the transformed PID controller equation

${\displaystyle u(s)=K_{P}\,e(s)+K_{I}\,{\frac {1}{s}}\,e(s)+K_{D}\,s\,e(s)}$

${\displaystyle u(s)=\left(K_{P}+K_{I}\,{\frac {1}{s}}+K_{D}\,s\right)e(s)}$

with the PID controller transfer function

${\displaystyle C(s)=\left(K_{P}+K_{I}\,{\frac {1}{s}}+K_{D}\,s\right).}$

As an example of tuning a PID controller in the closed-loop system H(s), consider a 1st order plant given by

${\displaystyle P(s)={\frac {A}{1+sT_{P}}}}$

where A and T_P are some constants. The plant output is fed back through

${\displaystyle F(s)={\frac {1}{1+sT_{F}}}}$

where T_F is also a constant. Now if we set ${\displaystyle K_{P}=K\left(1+{\frac {T_{D}}{T_{I}}}\right)}$, K_D = KT_D, and ${\displaystyle K_{I}={\frac {K}{T_{I}}}}$, we can express the PID controller transfer function in series form as

${\displaystyle C(s)=K\left(1+{\frac {1}{sT_{I}}}\right)(1+sT_{D})}$

Plugging P(s), F(s), and C(s) into the closed-loop transfer function H(s), we find that by setting

${\displaystyle K={\frac {1}{A}},T_{I}=T_{F},T_{D}=T_{P}}$

H(s) = 1. With this tuning in this example, the system output follows the reference input exactly.

However, in practice, a pure differentiator is neither physically realizable nor desirable due to amplification of noise and resonant modes in the system. Therefore, a phase-lead compensator type approach or a differentiator with low-pass roll-off are used instead.

Linear and nonlinear control theory

The field of control theory can be divided into two branches:

• Linear control theory – This applies to systems made of devices which obey the superposition principle, which means roughly that the output is proportional to the input. They are governed by linear differential equations. A major subclass is systems which in addition have parameters which do not change with time, called linear time invariant (LTI) systems. These systems are amenable to powerful frequency domain mathematical techniques of great generality, such as the Laplace transform, Fourier transform, Z transform, Bode plot, root locus, and Nyquist stability criterion. These lead to a description of the system using terms like bandwidth, frequency response, eigenvalues, gain, resonant frequencies, zeros and poles, which give solutions for system response and design techniques for most systems of interest.
• Nonlinear control theory – This covers a wider class of systems that do not obey the superposition principle, and applies to more real-world systems because all real control systems are nonlinear. These systems are often governed by nonlinear differential equations. The few mathematical techniques which have been developed to handle them are more difficult and much less general, often applying only to narrow categories of systems. These include limit cycle theory, Poincaré maps, Lyapunov stability theorem, and describing functions. Nonlinear systems are often analyzed using numerical methods on computers, for example by simulating their operation using a simulation language. If only solutions near a stable point are of interest, nonlinear systems can often be linearized by approximating them by a linear system using perturbation theory, and linear techniques can be used.

Analysis techniques - frequency domain and time domain

Mathematical techniques for analyzing and designing control systems fall into two different categories:

• Frequency domain – In this type the values of the state variables, the mathematical variables representing the system's input, output and feedback are represented as functions of frequency. The input signal and the system's transfer function are converted from time functions to functions of frequency by a transform such as the Fourier transform, Laplace transform, or Z transform. The advantage of this technique is that it results in a simplification of the mathematics; the differential equations that represent the system are replaced by algebraic equations in the frequency domain which is much simpler to solve. However, frequency domain techniques can only be used with linear systems, as mentioned above.
• Time-domain state space representation – In this type the values of the state variables are represented as functions of time. With this model, the system being analyzed is represented by one or more differential equations. Since frequency domain techniques are limited to linear systems, time domain is widely used to analyze real-world nonlinear systems. Although these are more difficult to solve, modern computer simulation techniques such as simulation languages have made their analysis routine.

In contrast to the frequency domain analysis of the classical control theory, modern control theory utilizes the time-domain state space representation, a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations. To abstract from the number of inputs, outputs, and states, the variables are expressed as vectors and the differential and algebraic equations are written in matrix form (the latter only being possible when the dynamical system is linear). The state space representation (also known as the "time-domain approach") provides a convenient and compact way to model and analyze systems with multiple inputs and outputs. With inputs and outputs, we would otherwise have to write down Laplace transforms to encode all the information about a system. Unlike the frequency domain approach, the use of the state-space representation is not limited to systems with linear components and zero initial conditions. "State space" refers to the space whose axes are the state variables. The state of the system can be represented as a point within that space.

System interfacing - SISO & MIMO

Control systems can be divided into different categories depending on the number of inputs and outputs.

• Single-input single-output (SISO) – This is the simplest and most common type, in which one output is controlled by one control signal. Examples are the cruise control example above, or an audio system, in which the control input is the input audio signal and the output is the sound waves from the speaker.
• Multiple-input multiple-output (MIMO) – These are found in more complicated systems. For example, modern large telescopes such as the Keck and MMT have mirrors composed of many separate segments each controlled by an actuator. The shape of the entire mirror is constantly adjusted by a MIMO active optics control system using input from multiple sensors at the focal plane, to compensate for changes in the mirror shape due to thermal expansion, contraction, stresses as it is rotated and distortion of the wavefront due to turbulence in the atmosphere. Complicated systems such as nuclear reactors and human cells are simulated by a computer as large MIMO control systems.

Topics in control theory

Stability

The stability of a general dynamical system with no input can be described with Lyapunov stability criteria.

• A linear system is called bounded-input bounded-output (BIBO) stable if its output will stay bounded for any bounded input.
• Stability for nonlinear systems that take an input is input-to-state stability (ISS), which combines Lyapunov stability and a notion similar to BIBO stability.

For simplicity, the following descriptions focus on continuous-time and discrete-time linear systems.

Mathematically, this means that for a causal linear system to be stable all of the poles of its transfer function must have negative-real values, i.e. the real part of each pole must be less than zero. Practically speaking, stability requires that the transfer function complex poles reside

• in the open left half of the complex plane for continuous time, when the Laplace transform is used to obtain the transfer function.
• inside the unit circle for discrete time, when the Z-transform is used.

The difference between the two cases is simply due to the traditional method of plotting continuous time versus discrete time transfer functions. The continuous Laplace transform is in Cartesian coordinates where the ${\displaystyle x}$ axis is the real axis and the discrete Z-transform is in circular coordinates where the ${\displaystyle \rho }$ axis is the real axis.

When the appropriate conditions above are satisfied a system is said to be asymptotically stable; the variables of an asymptotically stable control system always decrease from their initial value and do not show permanent oscillations. Permanent oscillations occur when a pole has a real part exactly equal to zero (in the continuous time case) or a modulus equal to one (in the discrete time case). If a simply stable system response neither decays nor grows over time, and has no oscillations, it is marginally stable; in this case the system transfer function has non-repeated poles at the complex plane origin (i.e. their real and complex component is zero in the continuous time case). Oscillations are present when poles with real part equal to zero have an imaginary part not equal to zero.

If a system in question has an impulse response of

${\displaystyle \ x[n]=0.5^{n}u[n]}$

then the Z-transform (see this example), is given by

${\displaystyle \ X(z)={\frac {1}{1-0.5z^{-1}}}}$

which has a pole in ${\displaystyle z=0.5}$ (zero imaginary part). This system is BIBO (asymptotically) stable since the pole is inside the unit circle.

However, if the impulse response was

${\displaystyle \ x[n]=1.5^{n}u[n]}$

then the Z-transform is

${\displaystyle \ X(z)={\frac {1}{1-1.5z^{-1}}}}$

which has a pole at ${\displaystyle z=1.5}$ and is not BIBO stable since the pole has a modulus strictly greater than one.

Numerous tools exist for the analysis of the poles of a system. These include graphical systems like the root locus, Bode plots or the Nyquist plots.

Mechanical changes can make equipment (and control systems) more stable. Sailors add ballast to improve the stability of ships. Cruise ships use antiroll fins that extend transversely from the side of the ship for perhaps 30 feet (10 m) and are continuously rotated about their axes to develop forces that oppose the roll.

Controllability and observability

Main articles: Controllability and Observability

Controllability and observability are main issues in the analysis of a system before deciding the best control strategy to be applied, or whether it is even possible to control or stabilize the system. Controllability is related to the possibility of forcing the system into a particular state by using an appropriate control signal. If a state is not controllable, then no signal will ever be able to control the state. If a state is not controllable, but its dynamics are stable, then the state is termed stabilizable. Observability instead is related to the possibility of observing, through output measurements, the state of a system. If a state is not observable, the controller will never be able to determine the behavior of an unobservable state and hence cannot use it to stabilize the system. However, similar to the stabilizability condition above, if a state cannot be observed it might still be detectable.

From a geometrical point of view, looking at the states of each variable of the system to be controlled, every "bad" state of these variables must be controllable and observable to ensure a good behavior in the closed-loop system. That is, if one of the eigenvalues of the system is not both controllable and observable, this part of the dynamics will remain untouched in the closed-loop system. If such an eigenvalue is not stable, the dynamics of this eigenvalue will be present in the closed-loop system which therefore will be unstable. Unobservable poles are not present in the transfer function realization of a state-space representation, which is why sometimes the latter is preferred in dynamical systems analysis.

Solutions to problems of an uncontrollable or unobservable system include adding actuators and sensors.

Control specification

Several different control strategies have been devised in the past years. These vary from extremely general ones (PID controller), to others devoted to very particular classes of systems (especially robotics or aircraft cruise control).

A control problem can have several specifications. Stability, of course, is always present. The controller must ensure that the closed-loop system is stable, regardless of the open-loop stability. A poor choice of controller can even worsen the stability of the open-loop system, which must normally be avoided. Sometimes it would be desired to obtain particular dynamics in the closed loop: i.e. that the poles have ${\displaystyle Re[\lambda ]<-{\overline {\lambda }}}$, where ${\displaystyle {\overline {\lambda }}}$ is a fixed value strictly greater than zero, instead of simply asking that ${\displaystyle Re[\lambda ]<0}$.

Another typical specification is the rejection of a step disturbance; including an integrator in the open-loop chain (i.e. directly before the system under control) easily achieves this. Other classes of disturbances need different types of sub-systems to be included.

Other "classical" control theory specifications regard the time-response of the closed-loop system. These include the rise time (the time needed by the control system to reach the desired value after a perturbation), peak overshoot (the highest value reached by the response before reaching the desired value) and others (settling time, quarter-decay). Frequency domain specifications are usually related to robustness (see after).

Modern performance assessments use some variation of integrated tracking error (IAE, ISA, CQI).

Model identification and robustness

A control system must always have some robustness property. A robust controller is such that its properties do not change much if applied to a system slightly different from the mathematical one used for its synthesis. This requirement is important, as no real physical system truly behaves like the series of differential equations used to represent it mathematically. Typically a simpler mathematical model is chosen in order to simplify calculations, otherwise, the true system dynamics can be so complicated that a complete model is impossible.

System identification

Further information: System identification

The process of determining the equations that govern the model's dynamics is called system identification. This can be done off-line: for example, executing a series of measures from which to calculate an approximated mathematical model, typically its transfer function or matrix. Such identification from the output, however, cannot take account of unobservable dynamics. Sometimes the model is built directly starting from known physical equations, for example, in the case of a mass-spring-damper system we know that ${\displaystyle m{\ddot {x}}(t)=-Kx(t)-\mathrm {B} {\dot {x}}(t)}$. Even assuming that a "complete" model is used in designing the controller, all the parameters included in these equations (called "nominal parameters") are never known with absolute precision; the control system will have to behave correctly even when connected to a physical system with true parameter values away from nominal.

Some advanced control techniques include an "on-line" identification process (see later). The parameters of the model are calculated ("identified") while the controller itself is running. In this way, if a drastic variation of the parameters ensues, for example, if the robot's arm releases a weight, the controller will adjust itself consequently in order to ensure the correct performance.

Analysis

Analysis of the robustness of a SISO (single input single output) control system can be performed in the frequency domain, considering the system's transfer function and using Nyquist and Bode diagrams. Topics include gain and phase margin and amplitude margin. For MIMO (multi-input multi output) and, in general, more complicated control systems, one must consider the theoretical results devised for each control technique (see next section). I.e., if particular robustness qualities are needed, the engineer must shift their attention to a control technique by including these qualities in its properties.

Constraints

A particular robustness issue is the requirement for a control system to perform properly in the presence of input and state constraints. In the physical world every signal is limited. It could happen that a controller will send control signals that cannot be followed by the physical system, for example, trying to rotate a valve at excessive speed. This can produce undesired behavior of the closed-loop system, or even damage or break actuators or other subsystems. Specific control techniques are available to solve the problem: model predictive control (see later), and anti-wind up systems. The latter consists of an additional control block that ensures that the control signal never exceeds a given threshold.

System classifications

Linear systems control

Main article: State space (controls)

For MIMO systems, pole placement can be performed mathematically using a state space representation of the open-loop system and calculating a feedback matrix assigning poles in the desired positions. In complicated systems this can require computer-assisted calculation capabilities, and cannot always ensure robustness. Furthermore, all system states are not in general measured and so observers must be included and incorporated in pole placement design.

Nonlinear systems control

Main article: Nonlinear control

Processes in industries like robotics and the aerospace industry typically have strong nonlinear dynamics. In control theory it is sometimes possible to linearize such classes of systems and apply linear techniques, but in many cases it can be necessary to devise from scratch theories permitting control of nonlinear systems. These, e.g., feedback linearization, backstepping, sliding mode control, trajectory linearization control normally take advantage of results based on Lyapunov's theory. Differential geometry has been widely used as a tool for generalizing well-known linear control concepts to the nonlinear case, as well as showing the subtleties that make it a more challenging problem. Control theory has also been used to decipher the neural mechanism that directs cognitive states.

Decentralized systems control

Main article: Distributed control system

When the system is controlled by multiple controllers, the problem is one of decentralized control. Decentralization is helpful in many ways, for instance, it helps control systems to operate over a larger geographical area. The agents in decentralized control systems can interact using communication channels and coordinate their actions.

Deterministic and stochastic systems control

Main article: Stochastic control

A stochastic control problem is one in which the evolution of the state variables is subjected to random shocks from outside the system. A deterministic control problem is not subject to external random shocks.

Main control strategies

Every control system must guarantee first the stability of the closed-loop behavior. For linear systems, this can be obtained by directly placing the poles. Nonlinear control systems use specific theories (normally based on Aleksandr Lyapunov's Theory) to ensure stability without regard to the inner dynamics of the system. The possibility to fulfill different specifications varies from the model considered and the control strategy chosen.

List of the main control techniques

• Adaptive control uses on-line identification of the process parameters, or modification of controller gains, thereby obtaining strong robustness properties. Adaptive controls were applied for the first time in the aerospace industry in the 1950s, and have found particular success in that field.
• A hierarchical control system is a type of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system.
• Intelligent control uses various AI computing approaches like artificial neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms or a combination of these methods, such as neuro-fuzzy algorithms, to control a dynamic system.
• Optimal control is a particular control technique in which the control signal optimizes a certain "cost index": for example, in the case of a satellite, the jet thrusts needed to bring it to desired trajectory that consume the least amount of fuel. Two optimal control design methods have been widely used in industrial applications, as it has been shown they can guarantee closed-loop stability. These are Model Predictive Control (MPC) and linear-quadratic-Gaussian control (LQG). The first can more explicitly take into account constraints on the signals in the system, which is an important feature in many industrial processes. However, the "optimal control" structure in MPC is only a means to achieve such a result, as it does not optimize a true performance index of the closed-loop control system. Together with PID controllers, MPC systems are the most widely used control technique in process control.
• Robust control deals explicitly with uncertainty in its approach to controller design. Controllers designed using robust control methods tend to be able to cope with small differences between the true system and the nominal model used for design. The early methods of Bode and others were fairly robust; the state-space methods invented in the 1960s and 1970s were sometimes found to lack robustness. Examples of modern robust control techniques include H-infinity loop-shaping developed by Duncan McFarlane and Keith Glover, Sliding mode control (SMC) developed by Vadim Utkin, and safe protocols designed for control of large heterogeneous populations of electric loads in Smart Power Grid applications. Robust methods aim to achieve robust performance and/or stability in the presence of small modeling errors.
• Stochastic control deals with control design with uncertainty in the model. In typical stochastic control problems, it is assumed that there exist random noise and disturbances in the model and the controller, and the control design must take into account these random deviations.
• Self-organized criticality control may be defined as attempts to interfere in the processes by which the self-organized system dissipates energy.

People in systems and control

Main article: People in systems and control

Many active and historical figures made significant contribution to control theory including

• Pierre-Simon Laplace invented the Z-transform in his work on probability theory, now used to solve discrete-time control theory problems. The Z-transform is a discrete-time equivalent of the Laplace transform which is named after him.
• Irmgard Flugge-Lotz developed the theory of discontinuous automatic control and applied it to automatic aircraft control systems.
• Alexander Lyapunov in the 1890s marks the beginning of stability theory.
• Harold S. Black invented the concept of negative feedback amplifiers in 1927. He managed to develop stable negative feedback amplifiers in the 1930s.
• Harry Nyquist developed the Nyquist stability criterion for feedback systems in the 1930s.
• Richard Bellman developed dynamic programming in the 1940s.
• Warren E. Dixon, control theorist and a professor
• Andrey Kolmogorov co-developed the Wiener–Kolmogorov filter in 1941.
• Norbert Wiener co-developed the Wiener–Kolmogorov filter and coined the term cybernetics in the 1940s.
• John R. Ragazzini introduced digital control and the use of Z-transform in control theory (invented by Laplace) in the 1950s.
• Lev Pontryagin introduced the maximum principle and the bang-bang principle.
• Pierre-Louis Lions developed viscosity solutions into stochastic control and optimal control methods.
• Rudolf E. Kálmán pioneered the state-space approach to systems and control. Introduced the notions of controllability and observability. Developed the Kalman filter for linear estimation.
• Ali H. Nayfeh who was one of the main contributors to nonlinear control theory and published many books on perturbation methods
• Jan C. Willems Introduced the concept of dissipativity, as a generalization of Lyapunov function to input/state/output systems. The construction of the storage function, as the analogue of a Lyapunov function is called, led to the study of the linear matrix inequality (LMI) in control theory. He pioneered the behavioral approach to mathematical systems theory.

Natural disaster

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Natural_disaster 

Global multihazard proportional economic loss by natural disasters as cyclones, droughts, earthquakes, floods, landslides and volcanoes

A natural disaster is "the negative impact following an actual occurrence of natural hazard in the event that it significantly harms a community". A natural disaster can cause loss of life or damage property, and typically leaves some economic damage in its wake. The severity of the damage depends on the affected population's resilience and on the infrastructure available. Examples of natural hazards include: avalanche, coastal flooding, cold wave, drought, earthquake, hail, heat wave, hurricane (tropical cyclone), ice storm, landslide, lightning, riverine flooding, strong wind, tornado, typhoon, tsunami, volcanic activity, wildfire, winter weather.

In modern times, the divide between natural, man-made and man-accelerated disasters is quite difficult to draw. Human choices and activities like architecture, fire, resource management or even climate change potentially play a role in causing "natural disasters". In fact, the term "natural disaster" has been called a misnomer already in 1976. A disaster is a result of a natural or man-made hazard impacting a vulnerable community. It is the combination of the hazard along with exposure of a vulnerable society that results in a disaster.

Natural disasters can be aggravated by inadequate building norms, marginalization of people, inequities, overexploitation of resources, extreme urban sprawl and climate change. The rapid growth of the world's population and its increased concentration often in hazardous environments has escalated both the frequency and severity of disasters. With the tropical climate and unstable landforms, coupled with deforestation, unplanned growth proliferation, non-engineered constructions make the disaster-prone areas more vulnerable. Developing countries suffer more or less chronically from natural disasters due to ineffective communication combined with insufficient budgetary allocation for disaster prevention and management.

An adverse event will not rise to the level of a disaster if it occurs in an area without vulnerable population. In a vulnerable area, however, such as Nepal during the 2015 earthquake, an adverse event can have disastrous consequences and leave lasting damage, which can take years to repair. The disastrous consequences also affect the mental health of affected communities, often leading to post-traumatic symptoms. These increased emotional experiences can be supported through collective processing, leading to resilience and increased community engagement.

Terminology

The term "disaster" is defined as follows:

Disasters are serious disruptions to the functioning of a community that exceed its capacity to cope using its own resources. Disasters can be caused by natural, man-made and technological hazards, as well as various factors that influence the exposure and vulnerability of a community.

— International Federation of Red Cross and Red Crescent Societies (IFRC)

The term "natural disaster" has been called a misnomer already in 1976. Many disasters result from the combination of natural hazards and social and human vulnerability, often involving development activities that ignore or fail to reduce the disaster risks. Nature alone is blamed for disasters even when disasters result from failures in development such as inadequate building norms, marginalization of people, inequities, overexploitation of resources, extreme urban sprawl and climate change. The implications of defining disasters as solely natural events are serious when it comes to understanding the causes of a disaster and the distribution of political and financial responsibility in disaster risk reduction, disaster management, compensation, insurance and disaster prevention.

Related terms

Natural hazard

Further information: Natural hazard

Natural hazards and natural disasters are related but are not the same. A natural hazard is the threat of an event that will likely have a negative impact. A natural disaster is the negative impact following an actual occurrence of natural hazard in the event that it significantly harms a community.

— Federal Emergency Management Agency (FEMA) of the United States

There are 18 natural hazards included in the National Risk Index of FEMA: avalanche, coastal flooding, cold wave, drought, earthquake, hail, heat wave, hurricane (tropical cyclone), ice storm, landslide, lightning, riverine flooding, strong wind, tornado, tsunami, volcanic activity, wildfire, winter weather. In addition there are also tornados and dust storms. Several of these have a higher risk of occurring now due to the effects of climate change.

Scale

Main articles: List of natural disasters by death toll and List of countries by natural disaster risk

Between 1995 and 2015, according to the UN's disaster-monitoring system, the greatest number of natural disasters occurred in America, China and India. 2012, there were 905 natural disasters worldwide, 93% of which were weather-related disasters. Overall costs were US$170 billion and insured losses $70 billion. 2012 was a moderate year. 45% were meteorological (storms), 36% were hydrological (floods), 12% were climatological (heat waves, cold waves, droughts, wildfires) and 7% were geophysical events (earthquakes and volcanic eruptions). Between 1980 and 2011 geophysical events accounted for 14% of all natural catastrophes.

According to 2019 WHO report countries with the highest share of disability-adjusted life years (DALY) lost due to natural disasters are Bahamas, Haiti, Zimbabwe and Armenia (probably mainly due to Spitak Earthquake).

According to the UN, Asia-Pacific is the world's most disaster prone region. According to ReliefWeb, a person in Asia-Pacific is five times more likely to be hit by a natural disaster than someone living in other regions.

Impacts

A natural disaster may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage.

Various phenomena like earthquakes, landslides, volcanic eruptions, floods, hurricanes, tornadoes, blizzards, tsunamis, cyclones, wildfires, and pandemics are all natural hazards that kill thousands of people and destroy billions of dollars of habitat and property each year. However, the rapid growth of the world's population and its increased concentration often in hazardous environments has escalated both the frequency and severity of disasters. With the tropical climate and unstable landforms, coupled with deforestation, unplanned growth proliferation, non-engineered constructions make the disaster-prone areas more vulnerable. Developing countries suffer more or less chronically from natural disasters due to ineffective communication combined with insufficient budgetary allocation for disaster prevention and management.

On the environment

During emergencies such as natural disasters and armed conflicts more waste may be produced, while waste management is given low priority compared with other services. Existing waste management services and infrastructures can be disrupted, leaving communities with unmanaged waste and increased littering. Under these circumstances human health and the environment are often negatively impacted.

Natural disasters (e.g. earthquakes, tsunamis, hurricanes) have the potential to generate a significant amount of waste within a short period. Waste management systems can be out of action or curtailed, often requiring considerable time and funding to restore. For example, the tsunami in Japan in 2011 produced huge amounts of debris: estimates of 5 million tonnes of waste were reported by the Japanese Ministry of the Environment. Some of this waste, mostly plastic and styrofoam washed up on the coasts of Canada and the United States in late 2011. Along the west coast of the United States, this increased the amount of litter by a factor of 10 and may have transported alien species. Storms are also important generators of plastic litter. A study by Lo et al. (2020) reported a 100% increase in the amount of microplastics on beaches surveyed following a typhoon in Hong Kong in 2018.

A significant amount of plastic waste can be produced during disaster relief operations. Following the 2010 earthquake in Haiti, the generation of waste from relief operations was referred to as a “second disaster”. The United States military reported that millions of water bottles and styrofoam food packages were distributed although there was no operational waste management system. Over 700,000 plastic tarpaulins and 100,000 tents were required for emergency shelters. The increase in plastic waste, combined with poor disposal practices, resulted in open drainage channels being blocked, increasing the risk of disease.

Conflicts can result in large-scale displacement of communities. People living under these conditions are often provided with minimal waste management facilities. Burn pits are widely used to dispose of mixed wastes, including plastics. Air pollution can lead to respiratory and other illnesses. For example, Sahrawi refugees have been living in five camps near Tindouf, Algeria for nearly 45 years. As waste collection services are underfunded and there is no recycling facility, plastics have flooded the camps’ streets and surroundings. In contrast, the Azraq camp in Jordan for refugees from Syria has waste management services; of 20.7 tonnes of waste produced per day, 15% is recyclable.

On vulnerable groups

Women

Because of the social, political and cultural context of many places throughout the world, women are often disproportionately affected by disaster. In the 2004 Indian Ocean tsunami, more women died than men, partly due to the fact that fewer women knew how to swim. During and after a natural disaster, women are at increased risk of being affected by gender based violence and are increasingly vulnerable to sexual violence. Disrupted police enforcement, lax regulations, and displacement all contribute to increased risk of gender based violence and sexual assault. Women who have been affected by sexual violence are at a significantly increased risk of sexually transmitted infections, unique physical injuries and long term psychological consequences. All of these long-term health outcomes can prevent successful reintegration into society after the disaster recovery period.

In addition to LGBT people and immigrants, women are also disproportionately victimised by religion-based scapegoating for natural disasters: fanatical religious leaders or adherents may claim that a god or gods are angry with women's independent, freethinking behaviour, such as dressing 'immodestly', having sex or abortions. For example, Hindutva party Hindu Makkal Katchi and others blamed women's struggle for the right to enter the Sabarimala temple for the August 2018 Kerala floods, purportedly inflicted by the angry god Ayyappan. In response to Iranian Islamic cleric Kazem Seddiqi's accusation of women dressing immodestly and spreading promiscuity being the cause of earthquakes, American student Jennifer McCreight organised the Boobquake event on 26 April 2010: she encouraged women around the world to participate in dressing immodestly all at the same time while performing regular seismographic checks to prove that such behaviour in women causes no significant increase in earthquake activity.

During and after natural disasters, routine health behaviors become interrupted. In addition, health care systems may have broken down as a result of the disaster, further reducing access to contraceptives. Unprotected intercourse during this time can lead to increased rates of childbirth, unintended pregnancies and sexually transmitted infections (STIs). Methods used to prevent STIs (such as condom use) are often forgotten or not accessible during times surrounding a disaster. Lack of health care infrastructure and medical shortages hinder the ability to treat individuals once they acquire an STI. In addition, health efforts to prevent, monitor or treat HIV/AIDS are often disrupted, leading to increased rates of HIV complications and increased transmission of the virus through the population.

Pregnant women are one of the groups disproportionately affected by natural disasters. Inadequate nutrition, little access to clean water, lack of health-care services and psychological stress in the aftermath of the disaster can lead to a significant increase in maternal morbidity and mortality. Furthermore, shortage of healthcare resources during this time can convert even routine obstetric complications into emergencies. During and after a disaster, women's prenatal, peri-natal and postpartum care can become disrupted. Among women affected by natural disaster, there are significantly higher rates of low birth weight infants, preterm infants and infants with low head circumference.

On governments and voting processes

Everyone is desperate for food and water. There's no food, water, or gasoline. The government is missing.
— Lian Gogali Aid worker following 2018 Sulawesi earthquake and tsunami

Disasters stress government capacity, as the government tries to conduct routine as well as emergency operations. Some theorists of voting behavior propose that citizens update information about government effectiveness based on their response to disasters, which affects their vote choice in the next election. Indeed, some evidence, based on data from the USA, reveals that incumbent parties can lose votes if citizens perceives them as responsible for a poor disaster response or gain votes based on perceptions of well-executed relief work. The latter study also finds, however, that voters do not reward incumbent parties for disaster preparedness, which may end up affecting government incentives to invest in such preparedness. Other evidence, however, also based on the USA, finds that citizens can simply backlash and blame the incumbent for hardship following a natural disaster, causing the incumbent party to lose votes. One study in India finds that incumbent parties extend more relief following disasters in areas where there is higher newspaper coverage, electoral turnout, and literacy --- the authors reason that these results indicate that incumbent parties are more responsive with relief to areas with more politically-informed citizens who would be more likely to punish them for poor relief efforts.

Violent conflicts within states can exacerbate the impact of natural disasters by weakening the ability of states, communities and individuals to provide disaster relief. Natural disasters can also worsen ongoing conflicts within states by weakening the capacity of states to fight rebels.

In Chinese and Japanese history, it has been routine for era names or capital cities and palaces of emperors to be changed after a major natural disaster, chiefly for political reasons such as association with hardships by the populace and fear of upheaval (i.e. in East Asian government chronicles, such fears were recorded in a low profile way as an unlucky name or place requiring change).

Responses

Main articles: Disaster response, Disaster risk reduction, and Emergency management

International campaigns

In 2000, the United Nations launched the International Early Warning Programme to address the underlying causes of vulnerability and to build disaster-resilient communities by promoting increased awareness of the importance of disaster risk reduction as an integral component of sustainable development, with the goal of reducing human, economic and environmental losses due to hazards of all kinds.

The International Day for Disaster Reduction (IDDR) is an international day that encourages every citizen and government to take part in building more disaster-resilient communities and nations. The United Nations General Assembly designated October 13 as the International Day for Natural Disaster Reduction as part of its proclamation of the International Decade for Natural Disaster Reduction. In 2009, the UN General Assembly decided to designate October 13 as the official date for this day, and also changed the name to International Day for Disaster Reduction.

Protection by international law

The United Nations Office for the Coordination of Humanitarian Affairs was formed by General Assembly Resolution 44/182.

Under the Convention on the Rights of Persons with Disabilities, "States Parties shall take, in accordance with their obligations under international law, including international humanitarian law and international human rights law, all necessary measures to ensure the protection and safety of persons with disabilities in situations of risk, including situations of armed conflict, humanitarian emergencies and the occurrence of natural disasters." The 1998 UN Guiding Principles on Internal Displacement and 2009 Kampala Convention also protect people displaced due to natural disasters.

Disasters caused by geological hazards

Global death from natural disasters

 

Global damage cost from natural disasters

Avalanches and landslides

Main articles: Landslide and Avalanche

See also: List of landslides and List of avalanches

 

A landslide in San Clemente, California in 1966

A landslide is described as an outward and downward slope movement of an abundance of slope-forming materials including rock, soil, artificial fill, or a combination of these.

During World War I, an estimated 40,000 to 80,000 soldiers died as a result of avalanches during the mountain campaign in the Alps at the Austrian-Italian front. Many of the avalanches were triggered by artillery fire.

Earthquakes

Main article: Earthquake

See also: Lists of earthquakes

 

Global Number of deaths from earthquake (1960-2017)

 

Global number of recorded earthquake events

An earthquake is the result of a sudden release of energy in the Earth's crust that creates seismic waves. At the Earth's surface, earthquakes manifest themselves by vibration, shaking, and sometimes displacement of the ground. Earthquakes are caused by slippage within geological faults. The underground point of origin of the earthquake is called the seismic focus. The point directly above the focus on the surface is called the epicenter. Earthquakes by themselves rarely kill people or wildlife — it is usually the secondary events that they trigger, such as building collapse, fires, tsunamis and volcanic eruptions, that cause death. Many of these can possibly be avoided by better construction, safety systems, early warning and planning.

Sinkholes

Main article: Sinkhole

See also: List of sinkholes

When natural erosion, human mining or underground excavation makes the ground too weak to support the structures built on it, the ground can collapse and produce a sinkhole. For example, the 2010 Guatemala City sinkhole, which killed fifteen people, was caused when heavy rain from Tropical Storm Agatha, diverted by leaking pipes into a pumice bedrock, led to the sudden collapse of the ground beneath a factory building.

Volcanic eruptions

See also: Types of volcanic eruptions and List of largest volcanic eruptions

Volcanoes can cause widespread destruction and consequent disaster in several ways. One hazard is the volcanic eruption itself, with the force of the explosion and falling rocks able to cause harm. Lava may also be released during the eruption of a volcano; as it leaves the volcano, it can destroy buildings, plants and animals due to its extreme heat. In addition, volcanic ash may form a cloud (generally after cooling) and settle thickly in nearby locations. When mixed with water, this forms a concrete-like material. In sufficient quantities, ash may cause roofs to collapse under its weight. Even small quantities will harm humans if inhaled — it has the consistency of ground glass and therefore causes laceration to the throat and lungs. Volcanic ash can also cause abrasion damage to moving machinery such as engines. The main killer of humans in the immediate surroundings of a volcanic eruption is pyroclastic flows, consisting of a cloud of hot ash which builds up in the air above the volcano and rushes down the slopes when the eruption no longer supports the lifting of the gases. It is believed that Pompeii was destroyed by a pyroclastic flow. A lahar is a volcanic mudflow or landslide. The 1953 Tangiwai disaster was caused by a lahar, as was the 1985 Armero tragedy in which the town of Armero was buried and an estimated 23,000 people were killed.

Volcanoes rated at 8 (the highest level) on the Volcanic Explosivity Index are known as supervolcanoes. According to the Toba catastrophe theory, 75,000 to 80,000 years ago, a supervolcanic eruption at what is now Lake Toba in Sumatra reduced the human population to 10,000 or even 1,000 breeding pairs, creating a bottleneck in human evolution, and killed three-quarters of all plant life in the northern hemisphere. However, there is considerable debate regarding the veracity of this theory. The main danger from a supervolcano is the immense cloud of ash, which has a disastrous global effect on climate and temperature for many years.

Disasters caused by water hazards

The Limpopo River during the 2000 Mozambique flood

A hydrological disaster is a violent, sudden and destructive change either in the quality of Earth's water or in the distribution or movement of water on land below the surface or in the atmosphere.

Floods

Main article: Flood

See also: List of floods

A flood is an overflow of water that 'submerges' land. The EU Floods Directive defines a flood as a temporary covering of land that is usually dry with water. In the sense of 'flowing water', the word may also be applied to the inflow of the tides. Flooding may result from the volume of a body of water, such as a river or lake, becoming higher than usual, causing some of the water to escape its usual boundaries. While the size of a lake or other body of water will vary with seasonal changes in precipitation and snow melt, a flood is not considered significant unless the water covers land used by humans, such as a village, city or other inhabited area, roads or expanses of farmland.

Tsunami

1755 copper engraving depicting Lisbon in ruins and in flames after the 1755 Lisbon earthquake. A tsunami overwhelms the ships in the harbor.

 

Main article: Tsunami

See also: List of historical tsunamis

A tsunami (plural: tsunamis or tsunami; from Japanese: 津波, lit. "harbour wave"; English pronunciation: /tsuːˈnɑːmi/), also known as a seismic sea wave or tidal wave, is a series of waves in a water body caused by the displacement of a large volume of water, generally in an ocean or a large lake. Tsunamis can be caused by undersea earthquakes such as the 2004 Boxing Day tsunami, or by landslides such as the one in 1958 at Lituya Bay, Alaska, or by volcanic eruptions such as the ancient eruption of Santorini. On March 11, 2011, a tsunami occurred near Fukushima, Japan and spread through the Pacific Ocean.

Limnic eruptions

Main article: Limnic eruption

A limnic eruption, also known as a lake overturn, occurs when a gas, usually CO₂, suddenly erupts from deep lake water, posing the threat of suffocating wildlife, livestock and humans. Such an eruption may also cause tsunamis in the lake as the rising gas displaces water. Scientists believe that landslides, explosions or volcanic activity can trigger such an eruption. To date, only two limnic eruptions have been observed and recorded. In 1984, in Cameroon, a limnic eruption in Lake Monoun caused the deaths of 37 nearby residents; at nearby Lake Nyos in 1986, a much larger eruption killed between 1,700 and 1,800 people by asphyxiation.

Disasters caused by extreme weather hazards

Hot and dry conditions

Heat waves

Main article: Heat wave

See also: List of heat waves

A heat wave is a period of unusually and excessively hot weather. The worst heat wave in recent history was the European Heat Wave of 2003. A summer heat wave in Victoria, Australia, created conditions which fuelled the massive bushfires in 2009. Melbourne experienced three days in a row of temperatures exceeding 40 °C (104 °F), with some regional areas sweltering through much higher temperatures. The bushfires, collectively known as "Black Saturday", were partly the act of arsonists. The 2010 Northern Hemisphere summer resulted in severe heat waves which killed over 2,000 people. The heat caused hundreds of wildfires which led to widespread air pollution and burned thousands of square kilometers of forest.

Droughts

Main article: Drought

See also: List of droughts

Drought is the unusual dryness of soil caused by levels of rainfall significantly below average over a prolonged period. Hot and dry winds, shortage of water, high temperatures and consequent evaporation of moisture from the ground can also contribute to conditions of drought. Droughts result in crop failure and shortages of water.

Well-known historical droughts include the 1997–2009 Millennium Drought in Australia which led to a water supply crisis across much of the country. As a result, many desalination plants were built for the first time. In 2011, the State of Texas lived under a drought emergency declaration for the entire calendar year and suffered severe economic losses. The drought caused the Bastrop fires.

Duststorms

A dust storm, also called a sandstorm, is a meteorological phenomenon common in arid and semi-arid regions. Dust storms arise when a gust front or other strong wind blows loose sand and dirt from a dry surface. Fine particles are transported by saltation and suspension, a process that moves soil from one place and deposits it in another.

Firestorms

A firestorm is a conflagration which attains such intensity that it creates and sustains its own wind system. It is most commonly a natural phenomenon, created during some of the largest bushfires and wildfires. Although the term has been used to describe certain large fires, the phenomenon's determining characteristic is a fire with its own storm-force winds from every point of the compass towards the storm's center, where the air is heated and then ascends.

Wildfires

A wildfire in California.

 

Main article: Wildfire

See also: List of forest fires

Wildfires are large fires which often start in wildland areas. Common causes include lightning and drought but wildfires may also be started by human negligence or arson. They can spread to populated areas and thus be a threat to humans and property, as well as wildlife. Notable wildfires include the 1871 Peshtigo Fire in the United States, which killed at least 1700 people, and the 2009 Victorian bushfires in Australia.

Storms

Tropical cyclone

See also: Tropical cyclones and climate change

Typhoon, cyclone, cyclonic storm and hurricane are different names for the same phenomenon: a tropical storm that forms over an ocean. It is characterized by strong winds, heavy rainfall and thunderstorms. The determining factor on which term is used is based on where the storm originates. In the Atlantic and Northeast Pacific, the term "hurricane" is used; in the Northwest Pacific, it is referred to as a "typhoon"; a "cyclone" occurs in the South Pacific and Indian Ocean.

The deadliest hurricane ever was the 1970 Bhola cyclone; the deadliest Atlantic hurricane was the Great Hurricane of 1780, which devastated Martinique, St. Eustatius and Barbados. Another notable hurricane is Hurricane Katrina, which devastated the Gulf Coast of the United States in 2005. Hurricanes may become more intense and produce more heavy rainfall as a consequence of human-induced climate change.

Thunderstorms

A classic anvil-shaped, and clearly-developed Cumulonimbus incus

 

Main article: Thunderstorm

Severe storms, dust clouds and volcanic eruptions can generate lightning. Apart from the damage typically associated with storms, such as winds, hail and flooding, the lightning itself can damage buildings, ignite fires and kill by direct contact. Especially deadly lightning incidents include a 2007 strike in Ushari Dara, a remote mountain village in northwestern Pakistan, that killed 30 people; the crash of LANSA Flight 508 which killed 91 people; and a fuel explosion in Dronka, Egypt, caused by lightning in 1994 which killed 469 people. Most deaths from lightning occur in the poorer countries of the Americas and Asia, where lightning is common and adobe mud brick housing provides little protection.

Tornadoes

A rope tornado in its dissipating stage, Tecumseh, Oklahoma.

 

See also: List of tornadoes and tornado outbreaks

A tornado is a violent and dangerous rotating column of air that is in contact with both the surface of the Earth and a cumulonimbus cloud, or, in rare cases, the base of a cumulus cloud. It is also referred to as a twister or a cyclone, although the word cyclone is used in meteorology in a wider sense to refer to any closed low pressure circulation. Tornadoes come in many shapes and sizes but typically take the form of a visible condensation funnel, the narrow end of which touches the Earth and is often encircled by a cloud of debris and dust. Most tornadoes have wind speeds of less than 180 km/h (110 mph), are approximately 75 m (250 ft) across, and travel a few kilometers before dissipating. The most extreme tornadoes can attain wind speeds of more than 480 km/h (300 mph), stretch more than 3 km (2 mi) across, and stay on the ground for perhaps more than 100 km (60 mi).

Cold-weather events

Blizzards

A blizzard in Maryland in 2009

 

Main article: Blizzard

Blizzards are severe winter storms characterized by heavy snow and strong winds. When high winds stir up snow that has already fallen, it is known as a ground blizzard. Blizzards can impact local economic activities, especially in regions where snowfall is rare. The Great Blizzard of 1888 affected the United States, when many tons of wheat crops were destroyed. In Asia, the 1972 Iran blizzard and the 2008 Afghanistan blizzard, were the deadliest blizzards in history; in the former, an area the size of Wisconsin was entirely buried in snow. The 1993 Superstorm originated in the Gulf of Mexico and traveled north, causing damage in 26 American states as well as in Canada and leading to more than 300 deaths.

Hailstorms

A large hailstone, about 6 cm (2+1⁄2 in) in diameter

 

Main article: Hail

See also: List of costly or deadly hailstorms

Hail is precipitation in the form of ice that does not melt before it hits the ground. Hailstones usually measure between 5 and 150 mm (1⁄4 and 6 in) in diameter. A particularly damaging hailstorm hit Munich, Germany, on July 12, 1984, causing about $2 billion in insurance claims.

Ice storms

Main article: Ice storm

An ice storm is a type of winter storm characterized by freezing rain. The U.S. National Weather Service defines an ice storm as a storm which results in the accumulation of at least 1⁄4 inch (6.35 mm) of ice on exposed surfaces.

Cold waves

Main article: Cold wave

A cold wave, known in some regions as a cold snap or cold spell, is a weather phenomenon that is distinguished by a cooling of the air. Specifically, as used by the U.S. National Weather Service, a cold wave is a rapid fall in temperature within a 24-hour period, requiring substantially increased protection to agriculture, industry, commerce and social activities. The precise criterion for a cold wave is determined by the rate at which the temperature falls and the minimum to which it falls. This minimum temperature is dependent on the geographical region and time of year.