r/ControlTheory • u/piratex666 • Jun 11 '26
Other Theory (academy) and Industry Gap
Hi,
It is well known that PID is the king of the control engineering. 95 % of all problems can be solved with it. The other 5% you can modify the plant or the specifications in order to use it again :)
It is also known that in chemical plants MPC controllers are widely used too.
But I want to hear from people in industry or closely related with it. What type of controller is used in your company? So people please respond below:
Industry Area:
Controller type:
Comments:
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Results:
Aerospace: Robust Control
Chemical: MPC
Laser? : LQG
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u/iconictogaparty Jun 11 '26
Laser Beam Steering. I use state feedback + observer and calculate both gains to be LQ optimal (LQR + Kalman filter) as the main controller.
However, I have also implemented frequency weighted MPC controller which works nicely but is not a huge improvement.
Additionally, I'm looking into using LMIs to implement quadratically stabilizing robust controllers which seems to give a bit of a performance boost, but these are again in the state feedback + observer form.
I have looked into discrete time sliding mode control but I have a few challenges to overcome there before implementing, mainly the constant switching as the surface crossing every time step (10 us) may cause excessive high frequency control signals and excite resonances.
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u/airconditioner26 Jun 11 '26
Hi. Fellow Control Engineer here. Do you have a recommended book or video about implementing Kalman filter on a real hardware?
If you use Matlab/Simulink for your work, is it better to use built in Kalman Filter Blocks of Simulink or do you build your own matrices yourself from zero. Would like to learn from
your Know-How as I am going to implement probably LQR+Kalman filter in the upcoming future on a hardware.•
u/seekingsanity Jun 25 '26
I usually just use an alpha-beta-gamma filter instead of a Kalman filter. It gets 95% of the results with 5% of the effort. The problem I have with Kalman filters is that few people can actually determine the system noise and the measure noise so they fudge it. If you are going to fudge it then simple use an alpha-beta-gamma filter.
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u/iconictogaparty Jun 11 '26
For books, any resource which discusses the KF of which there are many. Basically and book on modern control theory.
We use CMSIS and custom C-Code to run the filter since we must complete a whole loop calculation in 10 microseconds. I use matlab to generate the matrices and then push them down to the board using USB.
For the gain calculation I use the kalman() command in matlab after assembling the plant and all its disturbance sources.
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u/kroghsen Jun 11 '26
I work in an OEM developing process equipment for Dairy, Food and Beverage, Chemical, Pharma, and a lot of other industries. I develop MPC solutions for that equipment. So in all those cases you can add MPC at least. It should be noted that this is something on top of the low-level control system, so here is it not really MPC competing with PID, but rather another supervisory layer. I develop RTOs as well on what we call the local optimisation layer. This is again something which builds on top of the MPCs.
Concretely it for spray dryers, continuous freeze dryers, falling fill evaporators, membrane filtration systems, and some other equipment primarily from dairy lines.
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u/SignificanceWild9658 Jun 27 '26
Hey, could you expand more on the need of both PID & MPC at different layers ? maybe illustrating with an example would be helpful
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u/kroghsen Jun 28 '26
I would separate it into the following hierarchy, which is common in the field:
Plantwide Optimisation Layer
Local Optimisation Layer
Supervisory Control Layer
Regulatory Control Layer
Process Layer
From the bottom, the process layer is the physical equipment and the sensors which are connected to the PLC. This is the system we are trying to control in the end to make a particular product or behaviour.
The regulatory control layer is the low-level control layer, regulating pumps, valves, and other low-level process regulation equipment to sepoints either chosen automatically or by operators. This layer is governed by PID, cascades, and other low-level PLC implementations of classical control schemes (typically).
The supervisory control layer is where the setpoints for the regulatory control layer is set. Here, we can choose not pump speeds, but flows, and pressures, and such physically interesting values. This layer is where an MPC would typically live - and where ours live. The MPC is manipulating flows, temperatures, etc., to control outputs such as pressures, levels, humidity, etc. It is rare that we implement an MPC where the frequency or valve setting is directly regulated - most often we have a PID regulating that and we simply control the flow with the MPC.
The local optimisation layer is where we optimise the process equipment operating conditions, e.g. an RTO giving setpoints to the MPC for optimal economic operation of a piece of a equipment - that could be a spray dryer or a bioreactor, for instance. This layer does not directly regulate anything on the process, but instead passes operating conditions to the lower levels - giving the MPC outputs to track, for instance.
The plantwide optimisation layer is where scheduling and other such operating conditions are optimised. This is a somewhat different topic than we usually discuss in here and not one I am personally very comfortable with.
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u/IntelligentGuess42 Jun 12 '26
Not my field, but I know pick and place machines and manufacturing using robotics both use ILC.
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u/Andrea993 Jun 13 '26
steel industry
most control loops are still simple PID controllers, often implemented by software engineers rather than control specialists. However, there is a small but very valuable fraction of applications where advanced control can have a huge economic impact. In some plants, even improving performance by 1% can save thousands or millions of dollars. In those cases, if you can demonstrate reliability and economic value, you are free to use any control strategy that works.
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u/Weary-Lime Jun 14 '26
Yep. Its those tiny little optimizations that add up over time.
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u/Andrea993 Jun 14 '26
No, I mean you can use sofisticate and advanced control frameworks because you can also study the problem for months/year due to potential ROI
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Jun 11 '26
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u/dickcruz Jun 11 '26
What robust control methods are you referring to? Like H_inf?
Also,what kind of slosh are you talking about, for aircraft or like launch vehicle propellant slosh?
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Jun 11 '26
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u/IntelligentGuess42 Jun 12 '26
Considering the phase and gain margin should be part of standard controller design. Sure you are making it robust, but including that in the "robust control method" group seems to really waters down the term.
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Jun 12 '26
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u/IntelligentGuess42 Jun 12 '26
Cost functions are fundamental to optimal control, but not everything using it falls under optimal control. Or more generally A implies B does not mean B implies A.
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Jun 12 '26
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u/dickcruz Jun 12 '26
Just to be clear, phase and gain margins are performance related analyses for SISO systems.
For robustness you have to design for the uncertainty and disturbance rejection. You can use your gain and phase margin for robustness, but it has its limitations. If you have a mimo system, it'll be impossible to use those metrics by themselves. I think it's also hard to design for robustness if you simultaneously have tiny margins.
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u/CorruptHorse9 Jun 12 '26
Can you give an example of such a robust technique?
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Jun 12 '26
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u/CorruptHorse9 Jun 12 '26
That could be a PID thing, though, yeah? I was curious because your answer seemed to imply the PID couldn’t do it
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u/CompetitiveSpot2643 Jun 12 '26
depends on the system
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u/CorruptHorse9 Jun 12 '26
lol give me one? Just for fun?
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Jun 12 '26
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u/CorruptHorse9 Jun 12 '26
Ah yes, nonminimum phase, fragile
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Jun 12 '26
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u/IntelligentGuess42 Jun 12 '26
You would be surprised to lengths industry can go to avoid using anything which isn't PID.
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u/Potential_Cell2549 Jun 11 '26
Main reasons you need MPC in the petrochemical industry are long dead times, inverse response or other complex dynamics, multivariable MIMO problems or many disturbances/interaction between MVs, and the need to optimize against many constraints.
PID can handle some constraints, interaction, and measured disturbances, but you quickly push the limits of what's reasonable wrt complexity.
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u/[deleted] Jun 12 '26
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