A proportional–integral–derivative controller (PID controller) is a generic control loop feedback mechanism (controller) widely used in industrial control systems. A PID controller calculates an "error" value as the difference between a measured process variable and a desired setpoint. The controller attempts to minimize the error by adjusting the process control inputs. The PID parameters used in the calculation must be tuned according to the nature of the system.
The PID controller calculation (algorithm) involves three separate parameters; the proportional, the integral and derivative values. The proportional value determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.
By tuning the three constants in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.
Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.
Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.
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A familiar example of a control loop is the action taken when adjusting hot and cold faucet valves to maintain the faucet water at the desired temperature. This typically involves the mixing of two process streams, the hot and cold water. The person touches the water to sense or measure its temperature. Based on this feedback they perform a control action to adjust the hot and cold water valves until the process temperature stabilizes at the desired value.
Sensing water temperature is analogous to taking a measurement of the process value or process variable (PV). The desired temperature is called the setpoint (SP). The input to the process (the water valve position) is called the manipulated variable (MV). The difference between the temperature measurement and the setpoint is the error (e), that quantifies whether the water is too hot or too cold and by how much.
After measuring the temperature (PV), and then calculating the error, the controller decides when to change the tap position (MV) and by how much. When the controller first turns the valve on, they may turn the hot valve only slightly if warm water is desired, or they may open the valve all the way if very hot water is desired. This is an example of a simple proportional control. In the event that hot water does not arrive quickly, the controller may try to speedup the process by opening up the hot water valve moreandmore as time goes by. This is an example of an integral control. By using only the proportional and integral control methods, it is possible that in some systems the water temperature may oscillate between hot and cold, because the controller is adjusting the valves too quickly and overcompensating or overshooting the setpoint.
In the interest of achieving a gradual convergence at the desired temperature (SP), the controller may wish to damp the anticipated future oscillations. So in order to compensate for this effect, the controller may elect to temper their adjustments. This can be thought of as a derivative control method.
Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. If the oscillations increase with time then the system is unstable, whereas if they decrease the system is stable. If the oscillations remain at a constant magnitude the system is marginally stable. A human would not do this because we are adaptive controllers, learning from the process history; however, simple PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.
If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances. Generally controllers are used to reject disturbances and/or implement setpoint changes. Changes in feedwater temperature constitute a disturbance to the faucet temperature control process.
In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.
PID controllers are the controllers of choice for many of these applications, due to their wellgrounded theory, established history, simplicity, and simple setup and maintenance requirements.
This section describes the parallel or noninteracting form of the PID controller. For other forms please see the Section "Alternative nomenclature and PID forms".
The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence:
where
The proportional term (sometimes called gain) makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant K_{p}, called the proportional gain.
The proportional term is given by:
where
A high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on loop tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.
In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error (droop) that is a function of the proportional gain and the process gain. Specifically, if the process gain – the longterm drift in the absence of control, such as cooling of a furnace towards room temperature – is denoted by G and assumed to be approximately constant in the error, then the droop is when this constant gain equals the proportional term of the output, P_{out}, which is linear in the error, G = K_{p}e, so e = G / K_{p}. This is when the proportional term, which is pushing the parameter towards the set point, is exactly offset by the process gain, which is pulling the parameter away from the set point. If the process gain is down, as in cooling, then the steady state will be below the set point, hence the term "droop".
Only the drift component (longterm average, zerofrequency component) of process gain matters for the droop – regular or random fluctuations above or below the drift cancel out. The process gain may change over time or in the presence of external changes, for example if room temperature changes, cooling may be faster or slower.
Droop is proportional to process gain and inversely proportional to proportional gain, and is an inevitable defect of purely proportional control. Droop can be mitigated by adding a bias term (setting the setpoint above the true desired value), or corrected by adding an integration term (in a PI or PID controller), which effectively computes a bias adaptively.
Despite the droop, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.
The contribution from the integral term (sometimes called reset) is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, K_{i}.
The integral term is given by:
where
The integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steadystate error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning.
The rate of change of the process error is calculated by determining the slope of the error over time (i.e., its first derivative with respect to time) and multiplying this rate of change by the derivative gain K_{d}. The magnitude of the contribution of the derivative term (sometimes called rate) to the overall control action is termed the derivative gain, K_{d}.
The derivative term is given by:
where
The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral component and improve the combined controllerprocess stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large. Hence an approximation to a differentiator with a limited bandwidth is more commonly used. Such a circuit is known as a PhaseLead compensator.
The proportional, integral, and derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:
where the tuning parameters are:
Tuning a control loop is the adjustment of its control parameters (gain/proportional band, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response. Stability (bounded oscillation) is a basic requirement, but beyond that, different systems have different behavior, different applications have different requirements, and some desiderata conflict. Further, some processes have a degree of nonlinearity and so parameters that work well at fullload conditions don't work when the process is starting up from noload; this can be corrected by gain scheduling (using different parameters in different operating regions). PID controllers often provide acceptable control even in the absence of tuning, but performance can generally be improved by careful tuning, and performance may be unacceptable with poor tuning.
PID tuning is a difficult problem, even though there are only three parameters and in principal is simple to describe, because it must satisfying complex criteria within the limitations of PID control. There are accordingly various methods for loop tuning, and more sophisticated techniques are the subject of patents; this section describes some traditional manual methods for loop tuning.
If the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. Instability is caused by excess gain, particularly in the presence of significant lag.
Generally, stability of response (the reverse of instability) is required and the process must not oscillate for any combination of process conditions and setpoints, though sometimes marginal stability (bounded oscillation) is acceptable or desired.
The optimum behavior on a process change or setpoint change varies depending on the application.
Two basic desiderata are regulation (disturbance rejection – staying at a given setpoint) and command tracking (implementing setpoint changes) – these refer to how well the controlled variable tracks the desired value. Specific criteria for command tracking include rise time and settling time. Some processes must not allow an overshoot of the process variable beyond the setpoint if, for example, this would be unsafe. Other processes must minimize the energy expended in reaching a new setpoint.
There are several methods for tuning a PID loop. The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively inefficient, particularly if the loops have response times on the order of minutes or longer.
The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters.
Choosing a Tuning Method  
Method  Advantages  Disadvantages 
Manual Tuning  No math required. Online method.  Requires experienced personnel. 
Ziegler–Nichols  Proven Method. Online method.  Process upset, some trialanderror, very aggressive tuning. 
Software Tools  Consistent tuning. Online or offline method. May include valve and sensor analysis. Allow simulation before downloading.  Some cost and training involved. 
CohenCoon  Good process models.  Some math. Offline method. Only good for firstorder processes. 
If the system must remain online, one tuning method is to first set K_{i} and K_{d} values to zero. Increase the K_{p} until the output of the loop oscillates, then the K_{p} should be set to approximately half of that value for a "quarter amplitude decay" type response. Then increase K_{i} until any offset is correct in sufficient time for the process. However, too much K_{i} will cause instability. Finally, increase K_{d}, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much K_{d} will cause excessive response and overshoot. A fast PID loop tuning usually overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an overdamped closedloop system is required, which will require a K_{p} setting significantly less than half that of the K_{p} setting causing oscillation.
Effects of increasing a parameter independently  
Parameter  Rise time  Overshoot  Settling time  Steadystate error  Stability^{[1]} 
K_{p}  Decrease  Increase  Small change  Decrease  Degrade 
K_{i}  Decrease^{[2]}  Increase  Increase  Decrease significantly  Degrade 
K_{d}  Minor decrease  Minor decrease  Minor decrease  No effect in theory  Improve if K_{d} small 
Another heuristic tuning method is formally known as the Ziegler–Nichols method, introduced by John G. Ziegler and Nathaniel B. Nichols in the 1940s. As in the method above, the K_{i} and K_{d} gains are first set to zero. The P gain is increased until it reaches the critical gain, K_{c}, at which the output of the loop starts to oscillate. K_{c} and the oscillation period P_{c} are used to set the gains as shown:
Ziegler–Nichols method  
Control Type  K_{p}  K_{i}  K_{d}  
P  0.50K_{c}      
PI  0.45K_{c}  1.2K_{p} / P_{c}    
PID  0.60K_{c}  2K_{p} / P_{c}  K_{p}P_{c} / 8 
Most modern industrial facilities no longer tune loops using the manual calculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These software packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes.
Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled system's frequency response to design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can literally take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a selftuning feature in which very small setpoint changes are sent to the process, allowing the controller itself to calculate optimal tuning values.
Other formulas are available to tune the loop according to different performance criteria. Many patented formulas are now embedded within PID tuning software and hardware modules.
The basic PID algorithm presents some challenges in control applications that have been addressed by minor modifications to the PID form.
One common problem resulting from the ideal PID implementations is integral windup, where a large change in setpoint occurs (say a positive change) and the integral terms accumulates a significant error during the rise (windup), thus overshooting and continuing to increase as this accumulated error is unwound. This problem can be addressed by:
Many PID loops control a mechanical device (for example, a valve). Mechanical maintenance can be a major cost and wear leads to control degradation in the form of either stiction or a deadband in the mechanical response to an input signal. The rate of mechanical wear is mainly a function of how often a device is activated to make a change. Where wear is a significant concern, the PID loop may have an output deadband to reduce the frequency of activation of the output (valve). This is accomplished by modifying the controller to hold its output steady if the change would be small (within the defined deadband range). The calculated output must leave the deadband before the actual output will change.
The proportional and derivative terms can produce excessive movement in the output when a system is subjected to an instantaneous step increase in the error, such as a large setpoint change. In the case of the derivative term, this is due to taking the derivative of the error, which is very large in the case of an instantaneous step change. As a result, some PID algorithms incorporate the following modifications:
While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or even tuning, they can perform poorly in some applications, and do not in general provide optimal control. The fundamental difficulty with PID control is that it is a feedback system, with constant parameters, and no direct knowledge of the process, and thus overall performance is reactive and a compromise. The most significant improvement is to incorporate feedforward control with knowledge about the system, and using the PID only to control error. Alternatively, PIDs can be modified in more minor ways, such as by changing the parameters (either gain scheduling in different use cases or adaptively modifying them based on performance), improving measurement (higher sampling rate, precision, and accuracy, and lowpass filtering if necessary), or cascading multiple PID controllers.
PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or hunt about the control setpoint value. They also have difficulties in the presence of nonlinearities, may trade off regulation versus response time, do not react to changing process behavior (say, the process changes after it has warmed up), and have lag in responding to large disturbances.
Another problem faced with PID controllers is that they are linear, and in particular symmetric. Thus, performance of PID controllers in nonlinear systems (such as HVAC systems) is variable. For example, in temperature control, a common use case is active heating (via a heating element) but passive cooling (heating off, but no cooling), so overshoot can only be corrected slowly – it cannot be forced downward. In this case the PID should be tuned to be overdamped, to prevent or reduce overshoot, though this reduces performance (it increases settling time).
A problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a lowpass filter in order to remove higherfrequency noise components. However, lowpass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means is a much better choice. Alternatively, a nonlinear median filter may be used, which improves the filtering efficiency and practical performance ^{[3]}. In some case, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PI controller.
The control system performance can be improved by combining the feedback (or closedloop) control of a PID controller with feedforward (or openloop) control. Knowledge about the system (such as the desired acceleration and inertia) can be fed forward and combined with the PID output to improve the overall system performance. The feedforward value alone can often provide the major portion of the controller output. The PID controller can be used primarily to respond to whatever difference or error remains between the setpoint (SP) and the actual value of the process variable (PV). Since the feedforward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.
For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller. This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between the process setpoint and the feedback value. Working together, the combined openloop feedforward controller and closedloop PID controller can provide a more responsive, stable and reliable control system.
In addition to feedforward, PID controllers are often enhanced through methods such as PID gain scheduling (changing parameters in different operating conditions) or fuzzy logic. Further practical application issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.
One distinctive advantage of PID controllers is that two PID controllers can be used together to yield better dynamic performance. This is called cascaded PID control. In cascade control there are two PIDs arranged with one PID controlling the set point of another. A PID controller acts as outer loop controller, which controls the primary physical parameter, such as fluid level or velocity. The other controller acts as inner loop controller, which reads the output of outer loop controller as set point, usually controlling a more rapid changing parameter, flowrate or acceleration. It can be mathematically proven^{[citation needed]} that the working frequency of the controller is increased and the time constant of the object is reduced by using cascaded PID controller..
In the early history of automatic process control the PID controller was implemented as a mechanical device. These mechanical controllers used a lever, spring and a mass and were often energized by compressed air. These pneumatic controllers were once the industry standard.
Electronic analog controllers can be made from a solidstate or tube amplifier, a capacitor and a resistance. Electronic analog PID control loops were often found within more complex electronic systems, for example, the head positioning of a disk drive, the power conditioning of a power supply, or even the movementdetection circuit of a modern seismometer. Nowadays, electronic controllers have largely been replaced by digital controllers implemented with microcontrollers or FPGAs.
Most modern PID controllers in industry are implemented in programmable logic controllers (PLCs) or as a panelmounted digital controller. Software implementations have the advantages that they are relatively cheap and are flexible with respect to the implementation of the PID algorithm.
Variable voltages may be applied by the time proportioning form of Pulsewidth modulation (PWM) – a cycle time is fixed, and variation is achieved by varying the proportion of the time during this cycle that the controller outputs +1 (or −1) instead of 0. On a digital system the possible proportions are discrete – e.g., increments of .1 second within a 2 second cycle time yields 20 possible steps: percentage increments of 5% – so there is a discretization error, but for high enough time resolution this yields satisfactory performance.
The form of the PID controller most often encountered in industry, and the one most relevant to tuning algorithms is the standard form. In this form the K_{p} gain is applied to the I_{out}, and D_{out} terms, yielding:
where
In the ideal parallel form, shown in the controller theory section
the gain parameters are related to the parameters of the standard form through and . This parallel form, where the parameters are treated as simple gains, is the most general and flexible form. However, it is also the form where the parameters have the least physical interpretation and is generally reserved for theoretical treatment of the PID controller. The standard form, despite being slightly more complex mathematically, is more common in industry.
Sometimes it is useful to write the PID regulator in Laplace transform form:
Having the PID controller written in Laplace form and having the transfer function of the controlled system makes it easy to determine the closedloop transfer function of the system.
Another representation of the PID controller is the series, or interacting form
where the parameters are related to the parameters of the standard form through
with
This form essentially consists of a PD and PI controller in series, and it made early (analog) controllers easier to build. When the controllers later became digital, many kept using the interacting form.
The analysis for designing a digital implementation of a PID controller in a Microcontroller (MCU) or FPGA device requires the standard form of the PID controller to be discretised ^{[4]}. Approximations for firstorder derivatives are made by backward finite differences. The integral term is discretised, with a sampling time Δt,as follows,
The derivative term is approximated as,
Thus, a velocity algorithm for implementation of the discretised PID controller in a MCU is obtained by differentiating u(t), using the numerical definitions of the first and second derivative and solving for u(t_{k}) and finally obtaining:
Here is a simple software loop that implements the PID algorithm in its 'ideal, parallel' form:
previous_error = 0 integral = 0 start: error = setpoint  actual_position integral = integral + (error*dt) derivative = (error  previous_error)/dt output = (Kp*error) + (Ki*integral) + (Kd*derivative) previous_error = error wait(dt) goto start
