What is PID Control : Proportional Integral Derivative

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PID (Proportional-integral-derivative) control is the most common used closed loop control system in industrial automation. It has applications where a variable parameter need to be controlled continuously. In this article we will discuss what is PID control, it’s applications and how it works?

What is PID Control

To drive bike at a constant speed, the driver needs to change the throttle continuously. He/She takes input from speedometer, predict & analyse the error in speed (error) and change throttle accordingly.  PID controller works in similar way. Firstly It compares set-point and measured process variable. Predict future error and change the input to reduce error value to zero.

In technical terms a PID controller is a closed loop control system. It senses the output value and changes the input according to error value. Error value is the difference between set-point and measured process variable.

Everyday example of PID control includes car cruise control, quad-copter flight control etc.

Why we Need PID Controller
Traditional ON-OFF controller works by only fully on or fully off the process. For example to control water temperature with traditional controller, power to heater is turned on or turned off. But this process has the disadvantage of process oscillating behavior and accuracy limit is also very high.

Whereas PID controller can control a process within very small process set limits. Therefore PID controller are used to control critical process. 

For example, to control water temperature with PID controller. Controller will calculate the rate of change of temperature with time, predict the future error value and will change the heater power accordingly.  

How PID Controller Works
PID controller utilizes proportional, Integral and derivative gain to control a process variable ( system output). They get input from process output and compare it with setpoint value to calculate error signal.

Proportional Gain

Proportional response changes the process according to proportional to error value. It can be calculated by multiplying error with a constant Kp (Proportional Gain Constant)Higher the value of proportional gain constant. Higher will be the change in process based on error value. That can make the system unstable as well.

Whereas lower the value of proportional gain constant. Process will not be responsive to error value. Therefore optimized value for proportional gain constant should be used.

Integration Gain

Integral controller integrates the error value until error becomes zero. Therefore it helps in achieving steady state error zero. Integral controller limits any rapid change in system response. As a result it helps in proving system stability.

Derivative Gain

Derivative controller have the capability to predict the future behavior of the error. It’s value depends on the rate of change of error value. 

Derivative control helps in decrease in process output if process variable is increasing rapidly. Contribution of derivative control in PID controller is very less because it is very sensitive to noise in the system.

PID Controller Response

Lets consider a scenario where we are using PID controller to control the temperature of a liquid. We will try to find how Kp, Ki and Kd values will affect the controlled parameters.  

when integral and derivative controller are added to proportional control. Oscillation in system response reduces.
  • When only proportional control is used. Controlled parameter value will vary drastically. If we try to achieve accuracy, variation increases. 
  • If we integral control along with proportional control is used. It helps in achieving required controlled parameter more accurately and variation is also reduced.
  • When PID controller is used. Accuracy is improved and variations are reduced. It helps in controlling more quickly. 
PID Controller Tuning

PID controller tuning is required to achieve desired performance from control system. Various types of PID controller tuning methods are developed to find optimized values of P, I and D gain. Now a days most of PID controls are optimized by tuning and loop optimizing software.

Trial and Error Method

In this method P, I and D gain values are optimized with trial and error. To achieve this, first integral gain and derivative gain values are kept zero and proportional gain value is adjusted to achieve system oscillating behavior. After that Integral gain value is adjusted to stop oscillation behavior. Once system is stabilized, derivative gain is adjusted to improve response time.

Zeigler-Nichols Method

This method is similar to trial and error method but in this oscillation behavior is different. In this method I and D gains are set to zero and P gain value is adjusted to achieve system oscillating behavior. Once oscillation starts, the critical gain Kc and the period of oscillations Pc are noted. The P, I and D are then adjusted as per below table. 

Ziegler-Nichols Tuning
Control Kp Ki Kd
P 0.5Kc
PI 0.45Kc Pc/1.2
PID 0.60Kc 0.5Pc Pc/8

To sum up, PID control algorithms are the simplest way to design a robust control system. Tuning and loop optimizing software are used to improve control system performance. We suggest you to read this article on various types of proximity sensors.

Got Questions?  We will be happy to help.

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