PID vs. other control methods: which is the best choice

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https://www.realpars.com/blog/pid-vs-advanced-control-methods

Timestamps:
00:00 – Introduction
01:35 – PID control
03:13 – Components of PID control
04:27 – Fuzzy Logic control
07:12 – Model predictive checking
09:25 – Summary

Almost anyone who has worked in automated systems and manufacturing industries will probably tell you that PID control is the gold standard for process control applications. Most industrial control circuits use a combination of PID control.

In this video we discuss PID control and also introduce you to two advanced techniques: Fuzzy Logic Control and Model Predictive Control (MPC).

Let's start with a discussion of a very basic process control technique called ON/OFF or Bang-Bang Control.

This technique is very common and is found in applications such as home heating where a furnace is ON or OFF. What we ultimately have to deal with is a continuous temperature fluctuation around the desired set point.

Next on the list is a feedback control algorithm called PID control.
The 3 main components are proportional, integral and derivative.

PID control is very versatile and ensures that the actual process is kept as close to the set point as possible, regardless of disturbances or changes in the set point.

Controller tuning involves a procedure of adjusting each component of the PID algorithm to produce the desired response to setpoint changes or disturbances.

The proportional component applies an effort in proportion to how far the process is from the set point.

The integral component does its best to return the process to the set point after proportional control is stopped.

The derivative component looks at the rate at which the process moves away from the set point.

Each component contributes a unique signal that is added together to create the controller's output signal.

Let's move on to advanced process control techniques.

We start with Fuzzy Logic Control (FLC).

Fuzzification is the process of converting specific input values into some measure of membership in fuzzy sets based on how well they fit. Membership functions describe the degree of membership of a particular input or output variable with linguistic variables such as temperature and fan speed.

These membership functions can be represented graphically, with each fuzzy set having a degree of membership in a temperature range based on the room temperature.

What is a fuzzy set?

A fuzzy set relates to membership language variables. For example, a linguistic variable Temperature may have fuzzy sets such as hot, hot, and cold, each with its membership function.

The next topic of discussion is MPC.

MPC is a feedback control technique that uses a mathematical model to predict the behavior of the process variable.

Let's look at a block diagram of MPC for a robotic system.

We'll start with the components of the MPC controller.

The MPC controller uses the robot model, kinematics and dynamics to calculate optimal control inputs over a predetermined, limited period. The output of the MPC controller is the calculated control input trajectory for the robot.

The reference block represents the desired robot behavior, including things like gripper positions, orientations and movements to be followed, also called trajectories.

The Kinematics and Dynamics block provides a mathematical description of how control input affects the robot's movements, rotations, and joint angles.

The Optimization block represents the algorithm within the MPC controller.

Finally, the control input block represents the actual control inputs applied to the robot as determined by the optimization algorithm.

If you would like to learn more about PID control concepts, be sure to check out our PID Controller Basics course: https://www.realpars.com/courses/pid-controller

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