Monday, June 15, 2009

What is a PID controller and Tuning

What is a PID controller?

A PID (Proportional Integral Derivative) controller is a common instrument used in industrial control applications. A PID controller can be used for regulation of speed, temperature, flow, pressure and other process variables. Field mounted PID controllers can be placed close to the sensor or the control regulation device and be monitored centrally using a SCADA system.
Example: Temperature Control using a Digital PID controller
A typical PID temperature controller application could be to continuously vary a regulator which can alter a process temperature. This may be a pulsed switching device for electrical heaters or by opening and closing a gas valve. A heat only PID temperature controller uses a reverse output action, i.e. more power is applied when the temperature is below the setpoint and less power when above. PID control for injection and extrusion applications often employ additional cooling control outputs and usually require multiple controllers.
A PID controller (sometimes called a three term controller) reads the sensor signal, normally from a thermocouple or RTD, and converts the measurement to engineering units e.g. Degrees C. It then subtracts the measurement from a desired setpoint to determine an error.
The error is acted upon by the three (P, I & D) terms simultaneously:
PID Controller Theory
The following section examines PID controller theory and provides further explanation of the question `how do PID controllers work'.
Proportional (Gain)
The error is multiplied by a negative (for reverse action) proportional constant P, and added to the current output. P represents the band over which a controller's output is proportional to the error of the system. E.g. for a heater, a controller with a proportional band of 10 deg C and a setpoint of 100 deg C would have an output of 100% up to 90 deg C, 50% at 95 Deg C and 10% at 99 deg C. If the temperature overshoots the setpoint value, the heating power would be cut back further. Proportional only control can provide a stable process temperature but there will always be an error between the required setpoint and the actual process temperature.
Integral (Reset)
The error is integrated (averaged) over a period of time, and then multiplied by a constant I, and added to the current control output. I represents the steady state error of the system and will remove setpoint / measured value errors. For many applications Proportional + Integral control will be satisfactory with good stability and at the desired setpoint.
Derivative (Rate)
The rate of change of the error is calculated with respect to time, multiplied by another constant D, and added to the output. The derivative term is used to determine a controller's response to a change or disturbance of the process temperature (e.g. opening an oven door). The larger the derivative term, the more rapidly the controller will respond to changes in the process value.
Tuning of PID Controller Terms
The P, I and D terms need to be "tuned" to suit the dynamics of the process being controlled. Any of the terms described above can cause the process to be unstable, or very slow to control, if not correctly set. These days temperature control using digital PID controllers have automatic auto-tune functions. During the auto-tune period the PID controller controls the power to the process and measures the rate of change, overshoot and response time of the plant. This is often based on the Zeigler-Nichols method of calculating controller term values. Once the auto-tune period is completed the P, I & D values are stored and used by the PID controller.
Joe Crew is the Product Manager at
Data Track Process Instruments Ltd. Data Track manufactures digital panel meters, large number displays, PID controllers, signal conditioners and remote data acquisition systems for the process and control industry. Data Track can also supply HMI touchscreen operator panels and SCADA software. The Tracker 300 series of PID Controllers are fully configurable by PC software and feature universal input, single loop integrity, autotune PID, heat / cool control actions and condition monitoring features.
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Model-based Tuning Methods for Pid Controllers

Author: BIN

The manner in which a measured process variable responds over time to changes in the controller output signal is fundamental to the design and tuning of a PID controller. The best way to learn about the dynamic behavior of a process is to perform experiments, commonly referred to as "bump tests." Critical to success is that the process data generated by the bump test be descriptive of actual process behavior. Discussed are the qualities required for "good" dynamic data and methods for modeling the dynamic data for controller design. Parameters from the dynamic model are not only used in correlations to compute tuning values, but also provide insight into controller design parameters such as loop sample time and whether dead time presents a performance challenge. It is becoming increasingly common for dynamic studies to be performed with the controller in automatic (closed loop). For closed loop studies, the dynamic data is generated by bumping the set point. The method for using closed loop data is illustrated. Concepts in this work are illustrated using a level control simulation.


The methods discussed here apply to the complete family of PID algorithms. Examples presented will explore the most popular controller of the PID family, the Proportional-Integral (PI) controller:

In this controller, u(t) is the controller output and is the controller bias. The tuning parameters are controller gain, , and reset time, . Because is in the denominator, smaller values of reset time provide a larger weight to (increase the influence of) the integral term.


Designing any controller from the family of PID algorithms entails the following steps:

specifying the design level of operation,
collecting dynamic process data as near as practical to this design level,
fitting a first order plus dead time (FOPDT) model to the process data, and
using the resulting model parameters in a correlation to obtain initial controller tuning values.
The form of the FOPDT dynamic model is:
where y(t) is the measured process variable and u(t) is the controller output signal. When Eq. 2 is fit to the test data, the all-important parameters that describe the dynamic behavior of the process result:

Steady State Process Gain,

Overall Process Time Constant,

Apparent Dead Time,

These three model parameters are important because they are used in correlations to compute initial tuning values for a variety of controllers [1]. The model parameters are also important because:

the sign of indicates the sense of the controller (+ reverse acting; – direct acting)

the size of indicates the maximum desirable loop sample time (be sure sample time )

the ratio indicates whether a Smith predictor would show benefit (useful if )

the dynamic model itself can be employed within the architecture of feed forward, Smith predictor, decoupling and other model-based controller strategies.


As discussed above, the collection and analysis of dynamic process data are critical steps in controller design and tuning. A "good" set of data contains controller output to measured process variable data that is descriptive of the dynamic character of the process. To obtain such a data set, the answer to all of these questions about your data should be "yes" [1]. Ultimately, it is your responsibility to consider these steps to ensure success.

Was the process at steady state before data collection started?
Suppose a controller output change forces a dynamic response in a process, but the data file only shows the tail end of the response without showing the actual controller output change that caused the dynamics in the first place. Popular modeling tools will indeed fit a model to this data, but it will skew the fit in an attempt to account for an unseen "invisible force." This model will not be descriptive of your actual process and hence of little value for control. To avoid this problem, it is important that data collection begin only after the process has settled out. The modeling tool can then properly account for all process variations when fitting the model.

Did the test dynamics clearly dominate the process noise?
When generating dynamic process data, it is important that the change in controller output cause a
response in the process that clearly dominates the measurement noise. A rule of thumb is to define a
noise band of ±3 standard deviations of the random error around the process variable during steady
operation. Then, when during data collection, the change in controller output should force the process variable to move at least ten times this noise band (the signal to noise ratio should be greater than ten). If you meet or exceed this requirement, the resulting process data set will be rich in the dynamic information needed for controller design.

Were the disturbances quiet during the dynamic test?
It is essential that the test data contain process variable dynamics that have been clearly (and in the ideal world exclusively) forced by changes in the controller output as discussed in step 2. Dynamics caused by unmeasured disturbances can seriously degrade the accuracy of an analysis because the modeling tool will model those behaviors as if they were the result of changes in the controller output signal. In fact, a model fit can look perfect, yet a disturbance that occurred during data collection can cause the model fit to be nonsense. If you suspect that a disturbance event has corrupted test data, it is conservative to rerun the test.

Did the model fit appear to visually approximate the data plot?
It is important that the modeling tool display a plot that shows the model fit on top of the data. If the two lines don't look similar, then the model fit is suspect. Of course, as discussed in step 3, if the data has been corrupted by unmeasured disturbances, the model fit can look great yet the usefulness of the analysis can be compromised.

When generating dynamic process data, it is important that the change in the controller output signal causes a response in the measured process variable that clearly dominates the measurement noise. One way to quantify the amount of noise in the measured process variable is with a noise band. As illustrated in Fig. 1, a noise band is based on the standard deviation of the random error in the measurement signal when the controller output is constant and the process is at steady state. Here the noise band is defined as ±3 standard deviations of the measurement noise around the steady state of the measured process variable (99.7% of the signal trace is contained within the noise band). While other definitions of the noise band have been proposed, this definition is conservative when used for controller design.

When generating dynamic process data, the change in controller output should cause the measured process variable to move at least ten times the size of the noise band. Expressed concisely, the signal to noise ratio should be greater than ten. In Fig. 1, the noise band is 0.25°C. Hence, the controller output should be moved far and fast enough during a test to cause the measured exit temperature to move at least 2.5°C. This is a minimum specification. In practice it is conservative to exceed this value.

Figure 1 – Noise Band Encompasses ± 3 Standard Deviations Of The Measurement Noise

The recommended tuning correlations for controllers from the PID family are the Internal Model Control (IMC) relations [1]. These are an extension of the popular lambda tuning correlations and include the added sophistication of directly accounting for dead time in the tuning computations.

The first step in using the IMC (lambda) tuning correlations is to compute, , the closed loop time constant. All time constants describe the speed or quickness of a response. The closed loop time constant describes the desired speed or quickness of a controller in responding to a set point change. Hence, a small (a short response time) implies an aggressive or quickly responding controller. The closed loop time constants are computed as:

Aggressive Tuning: (See online version for picture of formula)

Moderate Tuning: ("")

Conservative Tuning: ("")

Final tuning is verified on-line and may require tweaking. If the process is responding sluggishly to disturbances and changes in the set point, the controller gain is too small and/or the reset time is too large. Conversely, if the process is responding quickly and is oscillating to a degree that makes you uncomfortable, the controller gain is too large and/or the reset time is too small.

EXAMPLEs: In online copy

PI Controller Tuning Map

Figure 6 – How PI controller tuning parameters impact set point tracking performance

Understanding the dynamic behavior of a process is essential to the proper design and tuning of a PID controller. The recommended design and tuning methodology is to: step, pulse or otherwise perturb the controller output near the design level of operation, record the controller output and measured process variable data as the process responds, and fit a first order plus dead time (FOPDT) dynamic model to this process data, use the dynamic model parameters in a correlation to compute P-Only, PI, PID and PID with Filter test your controller to ensure satisfactory performance.


1. Cooper, Douglas, "Practical Process Control Using Control Station," Published by Control Station,Inc, Storrs, CT (2004).

For more information about model-based tuning techniques and technologies, please see our other resources below:

PID Control – Practical Process Control Training (2 Day Workshop)

Complete list of authors:
Jeffrey Arbogast – Department of Chemical Engineering
Douglas J. Cooper, PhD – Control Station, Inc.
Robert C. Rice, PhD – Control Station, Inc.
To see the full online version with pictures, please visit

About the Author:

More from these authors and much more. please see ”More PID TRaining resources”...

Article Source: - Model-based Tuning Methods for Pid Controllers

Saturday, June 13, 2009

Control Systems and 5 Key Points to Effective Troubleshooting

Control Systems

Author: Matt Ridler

Copyright (c) 2008 Matt Ridler

A HVAC control system is a device or set of devices to manage, command, direct or regulate the behavior of other devices or systems.

There are two common classes of HVAC control systems, with many variations and combinations: logic or sequential controls, and feedback or linear controls. There is also fuzzy logic, which attempts to combine some of the design simplicity of logic with the utility of linear control. Some devices or systems are inherently not controllable.

The term "control system" may be applied to the essentially manual controls that allow an operator to, for example, close and open a hydraulic press, where the logic requires that it cannot be moved unless safety guards are in place.

An automatic sequential control system may trigger a series of mechanical actuators in the correct sequence to perform a task. For example various electric and pneumatic transducers may fold and glue a cardboard box, fill it with product and then seal it in an automatic packaging machine.

In the case of linear feedback systems, a control loop, including sensors, control algorithms and actuators, is arranged in such a fashion as to try to regulate a variable at a setpoint or reference value. An example of this may increase the fuel supply to a furnace when a measured temperature drops. PID controllers are common and effective in cases such as this. Control systems that include some sensing of the results they are trying to achieve are making use of feedback and so can, to some extent, adapt to varying circumstances. Open-loop control systems do not directly make use of feedback, but run only in pre-arranged ways.

Pure logic control systems were historically implemented by electricians with networks of relays, and designed with a notation called ladder logic. Today, most such systems are constructed with programmable logic devices.

Logic controllers may respond to switches, light sensors, pressure switches etc and cause the machinery to perform some operation. Logic systems are used to sequence mechanical operations in many applications. Examples include elevators, washing machines and other systems with interrelated stop-go operations.

Logic systems are quite easy to design, and can handle very complex operations. Some aspects of logic system design make use of Boolean logic.

For example, a thermostat is a simple negative-feedback control: when the temperature (the "measured variable" or MV) goes below a set point (SP), the heater is switched on. Another example could be a pressure-switch on an air compressor: when the pressure (MV) drops below the threshold (SP), the pump is powered. Refrigerators and vacuum pumps contain similar mechanisms operating in reverse, but still providing negative feedback to correct errors.

Simple on-off feedback control systems like these are cheap and effective. In some cases, like the simple compressor example, they may represent a good design choice.

In most applications of on-off feedback control, some consideration needs to be given to other costs, such as wear and tear of control valves and maybe other start-up costs when power is reapplied each time the MV drops. Therefore, practical on-off control systems are designed to include hysteresis, usually in the form of a deadband, a region around the setpoint value in which no control action occurs. The width of deadband may be adjustable or programmable.

Linear control systems use linear negative feedback to produce a control signal mathematically based on other variables, with a view to maintaining the controlled process within an acceptable operating range.

The output from a linear control system into the controlled process may be in the form of a directly variable signal, such as a valve that may be 0 or 100% open or anywhere in between. Sometimes this is not feasible and so, after calculating the current required corrective signal, a linear control system may repeatedly switch an actuator, such as a pump, motor or heater, fully on and then fully off again, regulating the duty cycle using pulse-width modulation.

About the Author:

Control Systems are used for all types business big or small. For more information vist Pulse Services Ltd.

Article Source: - Control Systems

The 5 Key Points to Effective Troubleshooting

Author: Terry Howsham

You don't realize it, but in the next few minutes you're going to learn to the important skill of troubleshooting an Industrial Control system.

Industrial control equipment can malfunction for a diversity of reasons- that’s life. No matter how well a system is maintained, you cannot prevent all failures. Mechanical contacts, pilot lamps and moving parts such as switches can wear out; on poorly designed systems wires can overheat and burn open or short out. Some parts can even be damaged by the environment. When certain components in a system are damaged equipment may operate in a manner far different than it was designed to, or not at all.

Typically, when process system fails there is a sense of importance to get it fixed and working again as soon as possible. If the defective equipment is part of an assembly line, the whole assembly line could be down causing unforeseen “stoppages” with loss of revenue. If you are at a customer’s site to repair equipment, the customer’s staff may watch you, knowing that they are paying for every minute you spend troubleshooting and repairing their control system. The pressure on you now to solve the problem as quickly as possible! You are now the expert- even though you may have no clue as to what their process does!

So what is troubleshooting?

It is the process of analyzing the behavior of a system to determine what is wrong with it, if anything, and then work out which piece of equipment is not functioning correctly. Now, depending on the type of equipment, troubleshooting can be a very challenging task.

Sometimes problems are easily diagnosed and the problem component is easily visible; such as a blown fuse. Other times the symptoms as well as the faulty component can be difficult to identify. A blown fuse with a visual indicator is easy to spot, whereas an intermittent problem caused by a high resistance connection or loose terminal can be much more difficult to find.

So what makes an expert Troubleshooter?

One quality of expert troubleshooters is that they are able to find virtually any fault in a practical amount of time. By using a basic common sense approach, they find them all. Another quality they have is the knack for finding out exactly what is wrong. No trial and error here. So what is their secret?

Expert troubleshooters have a good understanding of the operation of electrical components, mechanical systems and their components, process controls and control theory. They have an approach that allows them to logically and systematically analyze a system and determine exactly what is wrong. They also understand and effectively use tools such as electrical diagrams, mechanical process diagrams and test instruments to identify defective parts.

Here is a list of skills that you need to troubleshoot a control system.

(1) Work safely! Be aware of your surroundings. This sounds easy, but under pressure to fault find quickly, mistakes can be made. Ask yourself these questions as you work: Are there high voltages in this control panel? Do I need a hard hat or safety glasses to work safe? Are there any dangerous chemicals or processes under high pressure near me?

Arrive on site with an effective amount of tools to help you troubleshoot. Take with you any hand tools, Multimeters, loop calibrators, PC with PLC programming software that you feel will be needed. It is more professional to arrive prepared than to have to keep going back off site for more tools, or even worse, asking the customer to ‘borrow’ his tools.

(2) Listen with an open mind! Ask the operators of the control system what the symptoms are, and also ask any maintenance workers what they think the problem is. How does the system function normally? What has changed? When did it start? You may not be a doctor, but you are diagnosing problem. Only ask pertinent questions.

(3) You need to understand how process controls work. This consists of understanding the operation of components in the system such as PID loops, Industrial ventilation, fans, pumps, valves, PLC systems, Instrumentation such as temperature transmitters, push buttons, contactors, pilot lights, switches, relays, sensors, motors, and much more.

PLC control systems operate mechanical systems such as motors and valves. Could you tell an electrically actuated ball valve from a mechanical check valve? Can you recognize if you are looking at a relay or a contactor in a control panel?

(3) Use a logical, systematic approach to analyze the system’s behavior. This is critical. There are several approaches that troubleshooters use. They may have different steps or processes but they have the following in common: They approach problems systematically and logically thus minimizing the steps and ruling out trial and error.

One such approach used to teach troubleshooting is called the “5 Step Approach”. Here is a summary of the key steps are:

* Observe. A good number of faults provide clues as to their cause. There could be visual clues such as signs of damage, improper operation, lack of control, or no response. Don’t forget to use your other senses; sounds and smells can also provide valuable clues.

* Define Problem Area(s). This is where you apply logic and reasoning to your observations to determine the problem area of the control system.

* Identify Possible Causes. Once you have the problem area(s) defined, you need to identify all the possible causes of the failure.

* Determine The Most Probable Cause. Once the list of possible causes has been made you can prioritize the items as to the possibility of them being the root cause of the system failure.

* Test and Repair. Once you have determined the most probable cause, do some tests to prove it to be the problem or not.

(4) The knowledge of how to use tools. Do you understand how to read prints and diagrams? Can you operate test equipment such as Multimeters, loop calibrators and current probes?

Some of the key things you should be able to determine from electrical prints and process diagrams are:

-How the control system should operate.

-What voltages should you expect at various points on the control system.

-Where components are physically located. Remember, process automation transmitters such as temperature, pressure and flow are located throughout a process control system. They maybe at ground level, up near the roof, or even inside of a large tank!

-Various types of test equipment are available for testing electrical control systems. The ones that you choose depend on the type of system you are working on. A Multimeter is capable of measuring current, voltage and resistance. A loop Calibrator can measure the current signal (4-20Ma) coming from a field device such as a Temperature transmitter or it can simulate the 4-20Ma signal to test analog inputs.

(5) Practice! Troubleshooting, like any other skill, requires practice for you to become proficient at it. Practice can be difficult to get. Until you become reasonably experienced, it is best to practice troubleshooting in a controlled, offline environment.

In summary, troubleshooting a control system takes a high level of knowledge of control systems, patience to handle customers and a keen eye for detail.

About the Author:

Terry Howsham is a Senior Electrical Engineer for Unified Theory Inc, Camarillo, CA. Specializing in Process Control.

For more information please visit our main websiteUnified Theory Inc . We are a full service engineering firm specializing in facility and process design for industrial clients.

Article Source: - The 5 Key Points to Effective Troubleshooting