Modelling Hydraulic Systems

On Thu, 27 Sep 2007 10:33:18 -0700, snipped-for-privacy@gmail.com proclaimed to the world:

Peter, thanks for this response. I want to continue, but will need to do this at a later time. I don't have any major disagreement with anything you have said. It's ironic that your experience quoted is in the lumber industry as the only major problems I have had with hydraulics in one of my designs was in a similar application back in the early 90's. It involved a very similar project, automating an operation that produced fence posts and the company hired me to design and build a system to sort incoming precut timber, debarked and fed via conveyor into a peeling operation where they hand sorted the stock to be fed to the peeler set to the largest post possible for the stock. Eventually the sorting operation was to be automated using visual inspection systems and X-ray scanners. There were to be eight peelers for different size posts. We completed the first stage, which was to automate a single peeler using one of the first PLCs, the Atcom

  1. I actually loved that little PLC and the SNAP programming language. I had little problems with the programming side of the job, but did have lots of hydraulic woes. I spent weeks arguing with the supplier and ended up having to switch to his control valves and working around their limitations.

We never got past the single peeler automation and the conveyor/sort system. The project was scaled back because the company had no infrastructure to move and make use of the chippings being produced by a machine that worked at 10 times the manual rate. The added cost of waste disposal stretched their budget. I felt it was short sighted as they had one of the east coast's major paper mills within 60 miles of the facility and a contract to supply all of the fence posts supplied to Lowes. They could have leveraged that into supplying landscape timbers.

The visual inspection and sorting system was going to be challenging for me in the early 90's. A big gamble that I could produce a working system. I had just started researching what was available at the time. It would have been an interesting gamble.

I have not kept up with the wood industry but I wonder if there is not a lot of opportunity still in my area.

Anyway, give me some time to look at this better and I will comment some more. My major concern with models is this. Students in many fields are now being taught science and engineering using computer models. HS chemistry and physics classes now do not even have the equipment to carry out basic experiments. They learn about inertia, mass and motion on a screen instead of bricks, roller skates and pulleys. Does this new approach develop the mind's ability to model on it's own. I know models help me understand aspects of systems quickly, but without the real life experience, I don't know this would be true.

For me to defend or abandon this position, I need to investigate the modeling being used a little better.

Reply to
Paul M
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-- snip --

Personally, I don't think a model is much use unless I also have an intuitive grasp of how the system is going to work. That intuitive grasp comes from actually working with real hardware. I have successfully closed loops around systems where I only understood the math*, but it's chancy at best -- more often failing to close the loop properly will lead me to that "aha!" moment where I intuitively grasp what is going on (and therefore what's wrong with the model).

  • Always after being backed into it, or where I _thought_ I understood the real thing. Never as a first choice, ever.
Reply to
Tim Wescott

I think an accurate model is always useful. The more accurate the model and the less intuitive feel one has the more useful the model is. I can tell how accuate a model is by looking the mean squared error between the estimated and actual response. In the link to the system ID you can see the error was 0.102183. That is the sum of squared errors. If I divide that by 1500 samples I get a means square error that is very small. It is easy to see estimated velocity is matching the actual velocity very well. I know I can use the gain, damping factor and natural frequency to plug into my gain calculation equations and the results will be very good. Since I am measuring the position with a Temposonic rod with a resolution of about 0.001 inches every millisecond you would think my speed measurements would be very coarse. It makes one wonder what is more accuate, calculating the speed from the Tempsonic rod or using the model to estimate the speed. Which would you rather use for calculating the derivative gain term of the control output, the model velocity or the velocity calculated from the Temposonic rod?

Peter Nachtwey

Reply to
pnachtwey

True, but the less of an intuitive feel I have for the system, the less I trust my ability to make an accurate model. When I have to approach a system this way -- by modeling it, then developing an intuitive understanding from the model -- I make darn sure that I do tests (like your MSE error between estimated and actual response) to verify my model before I go building systems that may be blunders.

Do you mean your derivative _gain_ term or the derivative term itself. Using a model to calculate a velocity is all well and good if the model is accurate, but when reality diverges from the model you can be applying some really wrong control signals if you depend too much on the model.

I'm not putting down using a well-constructed observer here -- just pointing out that you need to take care that it is, indeed, well-constructed and not just a flight of your own imagination.

Reply to
Tim Wescott

On Mon, 01 Oct 2007 09:53:17 -0700, Tim Wescott proclaimed to the world:

This pretty well states a lot of my uneasy feelings about models. I fear that a student that uses models supplied them without any intuitive feel, has no way to know if the model is giving them junk or something useful. Without any experience seeing the control system built and running, how do they get the intuitive understanding?

Reply to
Paul M

"Paul M" schrieb im Newsbeitrag news: snipped-for-privacy@4ax.com...

... by using models. See also how to get the necessary process transfer function:

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Reply to
JCH

I meant the derivative term but I also use the model to calculate the derivative gain.

Obviously the model needs some feedback to keep from going astray. As you pointed out below the result is an well-constructed observer or possibly Kalman or H-infinity filter. The difference between them is small especially if you are talking about steady state filters.

That applies to Kalman filters and H-infinity filters too doesn't it? I have seen the terms Kalman and H Infinity filter used on this and other forums but it is all just big talk unless one can get past the basics and both filters start with the system transition matrix.

I wonder if the astronaut landing on the moon would have had an intuitive feel for landing without models and simulators.

Peter Nachtwey

Reply to
pnachtwey

Unless I'm sadly mistaken a Kalman or H-infinity filter, when used in a control system, is nothing more than a formally constructed time-varying observer. If you don't like (or don't need) the time-varying part, then a steady-state Kalman or H-infinity filter (if done right) pretty much meets the criterion for "well constructed observer".

AFAIK, yes.

Probably not. But the folks working on those models and simulations put a whole lot more effort into them than usually goes into a model for an industrial system. Certainly, if you are _really_ careful with your math then you can squeeze some valid intuition out of it -- but it doesn't just happen automatically.

Reply to
Tim Wescott

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