PID vs. Other Control Methods: What's the Best Choice
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- Опубликовано: 2 июн 2024
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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.
If you want to learn more about PID control concepts in-depth, be sure to explore our PID Controller Basics course: www.realpars.com/courses/pid-...
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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.
Wow, very nice ideas.
I remember back before Kinetix 5xxx brought decent default tuning and adaptive tuning. We altered the PID values during operation when we knew the load was changing.
Tell me, what kind of processes are you tuning that made you develop your techniques?
Interesting..... Thanks for sharing!
Howdy. Yeah.
During my days as a training planner in a paper mill. Operator experience.
Shifting to another paper grade only changing the main set value would take almost a whole reel if the PID:s were left to do the adjustments. One whole reel to be repulped.
The experienced operators knew the output values of the most important PID:s. They manually set them close to the final values. Then they input the new grade to the master controller and switched back to automatic. The new grade was achieved in about an hour. The shifting reel only needed to be rerolled cutting out the transition part. Enormous time saving and way less to be repulped.
Regards.
Recommend looking into PDF (psuedo derivative feedback) where the derivative term is replaced leveraging feedback and the integral. Limiting windup on the integral accumulator is also important.
Theoretical results show you can get faster convergence with little or no overshoot which can be very important for some control situations.
What about NON-Linear control. PID suck at that. Actually PID sucks at many processes. Because the world is not linear.
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.
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.
Hi there! Thanks for sharing. If I remember correctly, this concept was introduced over 20 years ago.
LQ-PID has worked for me quite well over the years
It's so rare to watch comparative control methods, thanks a lot!
You're very welcome!
Nice introductory to these concepts.
Glad you liked it! Thank you for sharing.
A superb video!!! Thank you for the clear and easy to digest explanation of the various processes.
Glad it was helpful!
Excellent and informative presentation. Thank you.
Glad you enjoyed it!
Very nice! Excelent Explanation! Thanks!
Glad you enjoyed it!
MPC sounds like feed forward control with extra steps.
Always good and nice video quality, also i hope the teams talk about linear matrix inequality LMI it quite new automation control type
Thank you very much for your kind support! I will happily go ahead and add that topic to the list.
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?
Thanks for the refresher video. It would be nice to make a video about Adaptive control.
You're very welcome! Thank you for the topic suggestion, I will happily go ahead and add this to the list.
Happy learning!
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.
Hi @David_Bruton, Thanks for reaching out. We have not yet developed any videos on advanced applications for PID Control. Stay tuned though as we are planning more videos and courses on such topics.
That's great and brilliant 👏
Glad you like it! Thank you for sharing
Thanks!
You're very welcome!
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"
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.
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?
Thanks for your question. Fuzzy logic doesn’t actually add directly to the proportional part of the PID response. FLC helps in determining the proportional, integral, and derivative gains dynamically by using linguistic variables, fuzzy sets, and a set of rules
Excellent RealPars from Brazil.
Many thanks!
Amazing information of PID vs other control method..very helpful
Glad you liked it! Thank you for sharing.
Thankyou for the knowledge
Our pleasure, happy learning!
Please make a tutorial on GE FANUC PLC
Thank you for your topic suggestion, I will happily go ahead and forward this to our course developers.
Thank you very much for sharing, and happy learning!
its always funny at work when I see the integrator and they some pid loops to ne tuned and you have 5 guys telling the programmer what you put as p i d😅
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?
Hi there. That's a new one for me. Thanks for sharing.
Which control methods used in Boston Dynamics robots? example Boston Dynamics Atlas humanoid robot?
Hi there, thanks for reaching out. Sorry, we can’t answer that question definitively as Boston Dynamics incorporates a combination of control methods, some may not be publicly disclosed. For availalbe information, refer to Boston Dynamics' website at bostondynamics.com
there is sliding mode control too..
"Wait it’s all PID?"
"Always has been"
Agree. The fuzzy logic could be a sort of voting block to select a subset of PID values for particular ranges. And the predictive model sounds like a Kalman filter based PID thing. Those used in weaponry and rocket science. (and advanced CNC as it seems). And even bang-bang is a redux of PID, where P is infinite and I, D are zero, with command output saturated to "one" ceiling and "zero" floor. All are PID still.
@@VK-tv1eq "sounds like a Kalman filter" - there you got off track a bit. The MPC is way more general, because everywhere the video says the word "algorithm" it can compute many more complex functions than multiply or integrate or derive. Higher order derivatives? Sure. (No they aren't D, they are functions that you can't generally get by tuning the gain on D.) Shortest path algorithms? Sure. All kinds of things implied under MPC. Turing-complete computations. Examples: figure the next engine gimbal for Starship flip, balance a double inverted pendulum, return a ping pong ball using three drones carrying a net, ...
Thanks god bless
Always welcome!
Yea furnaces cuntrull, in other videos that touch on that subject it allways struck me as strange the the furnace wos either on or if an not controlled in a more fine graned manner , well I 'm no hvac engineer and certainly no gas fitter, but why can't ng furnace have it 's gas supply controlled ( thus controlling the ammount of heat actually beeing created) is it justba question of cost or is my lack of knowlage sowing wet clearly?
Hiya...... I'm not an HVAC SME or a gas fitter either. Your question is reasonable indeed. Maybe you'll get a response from somebody in the know. In many large industrial systems, the temperature is controlled by duct louvres and vanes that move when commanded. Also not a terribly efficient way to control. Does anybody else want to chime in?
I think that typical home heating systems have, historically, been designed to run at an "optimum" level of "burn", and have quite simple fixed jetting and air supply... It's quite possible to run a system that has enough "mass" with proportional control, for a smoother response, and even put in an offset value to make the output appear to be the commanded one, especially where the expected target temperature is normally in a quite narrow range.
Howdy. Great.
There are systems that use two PID:s in a Master - Slave configuration.
1. A servo valve has a built in flow sensor and a built in PID controller. The servo valve's Set Value is the desired flow. For regular control valves the Set Value is the throttle position. The master PID is an algorithm in the plant automation computer. The master PID sends the desired flow to the servo valve.
2. The master PID sends varying P, I, and D variable values to the slave PID. The master PID senses the diffrence between Set Value and Process Value. If the difference is large the master sends aggressive P, I, D values to the slave PID. Small Proportional value (=large k=large gain), large D value and small I value.
When the Process Value comes close to the Set Value the master sends mild P, I, D values to the slave. Large Proportional value (=small k= low gain), small D value and large I value for smooth approach of Process value to the Set value.
The system will provide fast approach yet with no overshooting when tuned to optimum. It much resembles Fuzzy control.
Regards.
Good stuff! Thanks for sharing...... I've heard of this as well. Bottom line - The Master controller provides setpoint values, and the Slave controller adjusts the actual process to match these setpoints.
the fact that PID and fuzzy logic is used means that the control world has failed at creating good algorithms because these are the most basic algorithms based on zero theory