PID vs. Other Control Methods: What's the Best Choice

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⌚Timestamps:
00:00 - Intro
01:35 - PID Control
03:13 - Components of PID control
04:27 - Fuzzy Logic Control
07:12 - Model Predictive Control
09:25 - Summary

Almost everyone who has worked in automated systems and manufacturing industries will likely tell you that the gold plate standard for process control applications is PID Control. Most industrial control loops utilize some combination of PID control.

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

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

This technique is very common and found in applications such as home heating where a furnace is either ON or OFF. What we end up with is a continuous temperature fluctuation around the desired setpoint.

Next up 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 goes a long way to ensure that the actual process under control is held as closely as possible to the setpoint regardless of disturbances, or setpoint changes.

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

The Proportional component applies an effort in proportion to how far the process is away from the setpoint.

The Integral component applies an effort to return the process to the setpoint after the Proportional control quits.

The Derivative component looks at the speed at which the process is moving away from the setpoint.

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

Let’s move on to advanced process control techniques.

We’ll start with Fuzzy Logic Control (FLC).

Fuzzification is the process of converting specific input values into some degree of membership of fuzzy sets based on how well they fit. Membership functions describe the degree of membership of a particular input or output variable to linguistic variables such as Temperature and Fan Speed.

These membership functions can be represented graphically where each fuzzy set has a degree of membership to a temperature range based on the room temperature.

What is a fuzzy set?

A fuzzy set relates to membership linguistic variables. For example, a linguistic variable Temperature might have fuzzy sets like hot, warm, and cold, each with its membership function.

Next up for 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 MPC controller components.

The MPC Controller uses the robot model, kinematics, and dynamics to calculate the 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 motions to follow also referred to as Trajectories.

The Kinematics and dynamics block provides a mathematical description of how control inputs affect the robot's movements, rotations, and joint angles.

The Optimization block represents the algorithm within the MPC controller.

Finally, the Control Inputs Block represents the actual control inputs that are applied to the robot as determined by the optimization algorithm.

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[guatagel]

I use PID with some fuzzy logic: if the error is close to ideal, I turn off the D part of PID. The result is incredible, the noise is very low. Also, being the I accumulator a measure of the system loses (drift, heat loss, etc.), I preload the I accumulator value with the P value as soon as it is not saturated, and periodically. If I have historical data, I preload the I accumulator at start with the typical value of I fir that setpoint. That way I save time loading the I accumulator, and I reach the setpoint faster. With these two techniques combined my controllers behaves beautifully.

[MrSaemichlaus]

The shocks in your car's suspension can be thought of as PID controllers: the spring is the proportional part, the damper is the derivative part, and your manual height setting is the integral part. The shocks in the suspension stabilize the ride height of the car against the constantly changing height of the ground (setpoint) while allowing minimal overshoot and oscillation.

[odissey]

There is also another type of bang-bang control, called "Take Half Back" by Steven Woodward. It offers settling time comparable to PID and useful for on/off systems like AC or home heaters.

[dvdvideo]

LQ-PID has worked for me quite well over the years

[蓝狐]

It's so rare to watch comparative control methods, thanks a lot!

[sennabullet]

A superb video!!! Thank you for the clear and easy to digest explanation of the various processes.

[borisdelaine]

I love this refresher. I am not working in controls but I loved the study. Where are my eigenvalues and eigenvector? Does anybody know root locus or Bode diagram anymore?

[David_Bruton]

I wonder will you have videos about advanced applications for PID control?

For example these applications are commonly used in most DCS: split range, gain scheduling, ratio control, cascade control, and feed forward.

[Beregorn]

Long story short, a properly tuned PID controller IS the best solution, when feasible; if not, due to changing dynamics or excessive degrees of freedom, an MPC wil hack together a solution that is "good enough"

[VECORlt]

MPC sounds like feed forward control with extra steps.

[manyirons]

Excellent and informative presentation. Thank you.

[moriscnam]

The most important condition to use the MPC is having an accurate model. If the model has a low precision, the outcome would be disastrous.

[DayaneRodriguesNeves]

Very nice! Excelent Explanation! Thanks!

[Flankymanga]

Thanks for the refresher video. It would be nice to make a video about Adaptive control.

[pierrekilgoretrout]

Very interesting the FLC part, but to which extent the fuzzy logic gradation is like the proportional part of the PID? Or does it add a quadratic value?

[walidazouz]

Always good and nice video quality, also i hope the teams talk about linear matrix inequality LMI it quite new automation control type

[Karthik-utvo]

Which control methods used in Boston Dynamics robots? example Boston Dynamics Atlas humanoid robot?

[anasyousaf]

Please make a tutorial on GE FANUC PLC

[syufrijal]

Amazing information of PID vs other control method..very helpful

[lohikarhu]

I wonder if any of you-all have seen the introduction of pseudorandom inputs into a system that allows a kind of real-time model building, where the pseudorandom inputs are small relative to the setpoint?