PID Control | Technitab Solutions
Delhi | Bangalore | Vadodara | Gr Noida | Dubai

PID and Cascade Control


PID Control are used in most automatic method control applications in trade. they’ll regulate flow, temperature, pressure, level, and lots of alternative process variables. This Application Note reviews the planning of PID controllers and explains the P, I, and D management modes employed in them.

PID controllers have three control modes:

  • Proportional Control
  • Integral Control
  • Derivative Control

Each of the 3 modes reacts otherwise to the error. the quantity of response made by every management mode is adjustable by dynamical the controller’s standardization settings.

Proportional Control Mode

The proportional management mode is that the main driving force during a controller. It changes the controller output in proportion to the error (Figure 3). If the error will increase, the management action will increase proportionately. this is often terribly helpful, since a lot of management action is required to correct massive errors.

The adjustable setting for proportional management is termed the Controller Gain (Kc). a better controller gain can increase the quantity of proportional control action for a given error. If the managementler gain is about too high the control loop can begin periodic and become unstable. If the controller gain is about too low, it’ll not respond adequately to disturbances or point changes.

For most controllers, adjusting the controller gain setting influences the number of response within the integral and derivative control modes. this is often why the parameter is termed controller gain. However, there’s one controller style (called a parallel or freelance gains algorithm) during which adjusting the proportional gain doesn’t have an effect on the opposite modes.

Integral Control Mode

The need for manual reset, as described above, LED to the event of automatic reset or the integral management mode, because it is understood nowadays. The operate of the integral management mode is to increment or decrement the controller’s output over time to reduce the error, as long as there’s any error gift (process variable not at set point). Given enough time, the integral action can drive the controller output till the error is zero.

If the error is massive, the integral mode can increment/ decrement the controller output at a quick rate; if the error is tiny, the changes are slow. For a given error, the speed of the integral action is about by the controller’s integral time setting (Ti). an outsized worth of Ti (long integral time) ends up in a slow integral action, and alittle worth of Ti (short integral time) ends up in a quick integral action (Figure 7). If the integral time is about too long, the controller are sluggish; if it’s set too short, the management loop can oscillate and become unstable.

Most controllers, as well as the MAQ 20, use integral time (Ti) in minutes because the unit of live for integral management, however some use integral time in seconds. many controllers, usually ones with the parallel algorithmic rule, use integral gain (Ki) in repeats per minute. The parallel algorithmic rule is additionally obtainable within the MAQ 20.

Proportional + Integral Controller

Commonly referred to as the PI controller, the proportional + integral controller’s output is created from the total of the proportional and integral control actions (Figure 8).Compared to work 6, it’s clear however integral management continues to drive the controller output till it’s eliminated eliminated all offset.


Derivative Control Mode

The third control mode in a very PID controller is that the derivative control mode. derivative management is rarely utilized in controlling processes, however it’s used typically in motion management. For method management, it’s conditionally needed, is extremely sensitive to mensuration noise, and it makes trialand-error

calibration harder. withal, victimization the derivative management mode of a managementler will certify kinds of management loops respond a bit quicker than with PI management alone (temperature control may be a typical application for PID control).

The derivative control mode produces an output supported the speed of amendment of the error (Figure 10). due to this, derivative mode was originally referred to as rate. The derivative mode produces additional management action if the error changes at a quicker rate. If there’s no change within the error, the derivative action is zero. The derivative mode has associate degree adjustable setting referred to as derivative Time (Td). The larger the derivative time setting, the additional derivative action is created. A derivative time setting of zero effectively turns off this mode. If the derivative time is about too long, oscillations can occur and also the management loop can run unstable.


Two units of measure are used for the derivative setting of a controller: minutes and seconds.

Proportional + Integral + Derivative Controller

Commonly known as the pid controller, the Proportional + Integral + Derivative

controller’s output is created from the add of the proportional, integral, and derivative management actions. Figure eleven shows the noninteractive pid controller formula and Figure twelve shows the parallel controller formula. These are each supported within the MAQ 20 system.

The derivative mode of the PID controller provides more control action sooner than is possible with P or PI control. This reduces the effect of a disturbance and shortens the time it takes for the level to return to its set point (Figure 13).



Cascade control can significantly improve the control quality. This applies in particular to the dynamic action of the control loop.

A simple control system drawn in block diagram form looks like this:

Information from the measuring device (e.g. transmitter) goes to the controller, then to the ultimate management device (e.g. management valve), influencing the method that is perceived once more by the instrument. The controller’s task is to inject the correct quantity of feedback such the method variable stabilizes over time. This flow of data is conjointly spoken as a feedback management loop.
To cascade controllers means that to attach the signaling of 1 controller to the setpoint of another controller, with every controller sensing a distinct side of identical method. the primary controller (called the first, or master ) basically “gives orders” to the second controller (called the secondary or slave) via an overseas setpoint signal.

Thus, a cascade control system consists of two feedback control loops, one nested inside the other:

A very common example of cascade control could be a valve positioner, that receives a command signal from a daily method controller, and in turn works to confirm the valve stem position exactly matches that command signal. The management valve’s stem position is that the method variable (PV) for the positioner, even as the command signal is that the positioner’s setpoint (SP). Valve positioners so act as “slave” controllers to “master” method controllers controlling pressure, temperature, flow, or another method variable.

The aim of cascade control is to attain bigger stability of the first method variable by control a secondary method variable in accordance with the requirements of the primary. a vital demand of cascaded management is that the secondary method variable be faster-responding (i.e. shorter lag and dead times) than the first method variable.
an analogy for understanding cascade management is that of delegation in a very work setting. If a supervisor delegates some task to a subordinate, which subordinate performs the task while not more want of guidance or help from the supervisor, the supervisor’s job is formed easier. The subordinate takes care of all the insufficient details that might otherwise burden the supervisor if the supervisor had nobody to delegate to.

This analogy additionally makes it clear why the secondary method variable should be faster-responding than the first method variable: the supervisor-subordinate management structure fails to figure if the supervisor doesn’t maintain specialise in long-run goals (i.e. longer-term than the completion time of the tasks given to subordinates). If a supervisor focuses on achieving goals that are shorter-term than the time needed for subordinates to finish their assignments, the supervisor can inevitably incorporate “course changes” that are too fast for the subordinates to execute. this may cause the subordinates “lagging” behind the supervisor’s orders, to the damage of everyone’s satisfaction.

Important Points :

A necessary step in implementing cascade control is to confirm the secondary (“slave”) controller is well-tuned before any try is created to tune the first (“master”) controller. simply a moment’s thought is all that’s required to understand why this precedence in tuning should be: it’s an easy matter of dependence. The slave controller doesn’t rely on sensible standardization within the master controller so as to regulate the slave loop. If the master controller were placed in manual (effectively turning off its automatic response), the slave managementler would merely control to a relentless setpoint. However, the master controller most positively depends on the slave controller being well-tuned so as to meet the master’s “expectations.” If the slave controller were placed in manual mode, the master managementler wouldn’t be ready to exert any control over its method variable some. Clearly then, the slave controller’s response is crucial to the master controller having the ability to regulate its method variable, so the slave controller ought to be tuned initial once at first empowerment or optimizing a cascade system.
Just like superordinate management systems wherever a method controller receives a “remote” setpoint signal from another system, the secondary (“slave”) controller in a very cascade system generally has 3 completely different operative modes:

  • Manual mode:Controller takes no automatic action. Output value set by human operator.
  • Automatic mode:Controller automatically adjusts its output to try to keep PV = SP. Setpoint value set “locally” by human operator.
  • Cascade mode:Controller automatically adjusts its output to try to keep PV = SP. Setpoint value set “remotely” by primary (master) controller.

This means it’s possible to defeat a cascade system by inserting the secondary controller within the wrong mode (automatic) even as it’s possible to defeat any system by inserting the controller in manual mode. If a controller is “slaved” to a different controller, it should be left in cascade mode so as for the control strategy to operate as designed.

Leave a comment