[hamdani yusuf (OP)]

It's been so long since I made video  about process control system. I finally finished editing another one. 

It tries to explain the concept of control gain in an open loop control system. I found some sources explaining PID controller directly to the control gain in closed loop system, while skipping this important preliminary step.

[hamdani yusuf (OP)]

In this video we will explain about open loop gain in a process control system. The explanation starts with a bare minimum system to emphasize the effect of controller gain, and eliminate other contributing parameters.

[hamdani yusuf (OP)]

Before continuing to PID control in closed loop systems, I'd like to introduce the concept of goal oriented process control systems. I found many introductions of PID control jump straight to the mathematical details without considering practical constraints and scale of priority. Costs of action should also be taken into consideration.

I hope my next video can fill the gaps.

[hamdani yusuf (OP)]

It's been so long. I just finished a video for unintended close loop system.
Stay tuned.

Spoiler: show

The system is an automatic LED bulbs equipped with a photosensor which is supposed to turn on at night and off at daytime.

[hamdani yusuf (OP)]

Here it is

A real life example of unintended close loop system.
The system is an automatic LED bulbs equipped with a photosensor which is supposed to turn on at night and off at daytime.

[hamdani yusuf (OP)]

There are many videos on Youtube trying to introduce PID control. But many of them are not good enough as a reference to be applied in a real life control system. Some are too theoretical and discuss nothing but math equations. Some are simply summarizing word definitions of the terms. And some contains erroneous concepts which could hamper viewers' effort to understand and apply the PID control.

I find this video is pretty good as an introduction, although some additional information are still necessary if you need to apply PID control in real physical systems.

Introduction to PID Control

In this video we introduce the concept of proportional, integral, derivative (PID) control.  PID controllers are perhaps the most popular and widely used control scheme in history.  While they are relatively simple, they are surprisingly robust and provide excellent performance in most situations.  This video introduces the core concepts in PID controller and sets the stage for various future videos where we will discuss their nuances and details in greater depth.

Topics and timestamps:
0:00 ? Introduction
9:04 ? Proportional control
15:03 ? Integral control
24:49 ? Derivative control
30:41 ? Physical demonstration of PID control
44:16 - Conclusions

My comment on the conclusion of the video.
The derivative action can improve the stability if the system has low noise to signal ratio, especially in high frequency spectrum, relative to the natural frequency of the system. Otherwise, the control output would be dominated by this noise and become unstable.

[hamdani yusuf (OP)]

What is a PID Controller?

PID controller explained! Learn what a PID (Proportional Integral Derivative) controller is and how it works in an easy to follow video.

This video introduces PID controller in a more practical way by using a PID simulation software. Unfortunately, it only explore the controller part of the PID system, while the simulated process part is inadequately explained, which makes it hard to get a complete understanding of how PID controllers work. Applying a PID control in real life scenario also needs understanding of limitations and constraints of the hardware, like how the sensors and actuators work, their minimum and maximum working range, saturation points, hysteresis, non-linearity, consider cost of actions, prioritization of control parameters, control structure and control options which might be vendor dependent.

I have a comment on the video.

3:48 When proportional gain is too low, the controller output becomes too stable or less reactive, hence it will let the error too big for too long.

[hamdani yusuf (OP)]

The video below provides some additional information, especially around minute 4:00 where each parameter of PID controller is explained individually.

PID Controller Explained

⌚Timestamps:
00:00 - Intro
00:49 - Examples
02:21 - PID Controller
03:28 - PLC vs. stand-alone PID controller
03:59 - PID controller parameters
05:29 - Controller tuning
06:20 - Controller tuning methods

=============================

In this video, we?re going to talk about the PID Controller and its transformation from a single station device to what it has evolved into today. We?re going to explain why PID Controllers are used in industrial processes.

We?ll illustrate how Controller settings affect different processes under control. We?ll also provide an overview of Controller Tuning.

Let?s start with a discussion about home temperature control.
If the room temperature is below the setpoint, the furnace is turned ON. When the room temperature increases above the setpoint, the furnace turns OFF.

This type of control is referred to as ON/OFF or Bang-Bang Control. The temperature is not exactly held at the setpoint of 70?F, but cycles above and below the setpoint.

ON/OFF control may be ok for your house, but it is not ok for industrial processes or motion control. Let?s look at an example of tank level control to explain why.

The Valve fills the tank as the pump drains it. If the valve is operated with ON/OFF control, the water will fluctuate around the 50% setpoint. For our purpose, let?s say the fluctuation is ?10%. In most industrial applications, this fluctuation around the setpoint is not acceptable.

What if it?s possible to throttle the valve and place it in any position between ON and OFF?

Let?s look at how a PID Controller fits into a feedback control loop. The Controller is responsible for ensuring that the Process remains as close to the desired value as possible regardless of various disruptions.

The controller compares the Transmitter Process Variable (PV) signal, and the Setpoint.
Let?s refer to the difference between the Process Variable and the Setpoint as the Error signal.

Based on that comparison, the controller produces an output signal to operate the Final Control Element. This PID Controller output is capable of operating the Final Control Element over its entire 100% range.

The PID controller determines how much and how quickly correction is applied by using varying amounts of Proportional, Integral, and Derivative action. Each block contributes a unique signal that is added together to create the controller output signal.

The proportional block creates an output signal proportional to the magnitude of the Error Signal.
Unfortunately, the closer you get to the setpoint, the less it pushes. Eventually, the process just runs continuously close to the setpoint, but not quite there.

The integral block creates an output proportional to the duration and magnitude of the Error Signal. The longer the error and the greater the amount, the larger the integral output.
As long as an Error exists, Integral action will continue.

The derivative block creates an output signal proportional to the rate of change of the error signal. The faster the error changes, the larger the derivative output.

Derivative control looks ahead to see what the error will be in the future and contributes to the controller output accordingly. That brings us to a term called Controller Tuning.

There are many different manual methods for tuning a controller that involves observing the process response after inflicting controller setpoint changes.

One method involves increasing the amount of setpoint change and repeating the procedure until the process enters a state of steady-state oscillation.

Most process controllers, PLC, and DCS loop controllers sold today have Autotuning capability.
The PID controller learns how the process responds to a change in setpoint, and suggested PID settings.

I also point out a minor issue in the explanation of the video which might cause unnecessary confusion when experimenting in simulation or experimental setup.

4:40 The steady state error is usually between the initial process value and set point, not beyond it. The integral control is used to remove that error.