[Dprglist] PID-tuned Clock in Python?

Thu Feb 18 18:42:16 PST 2021

An option we are looking at for a work project is the STM32MP1XX family 
of processors.

It has a Arm A7 and Arm M4 in the same chip. The A7 runs Linux, and the 
M4 is for the real-time stuff. They have a bus between the two 
processors so they can look at the other's peripherals and memory.

They have a development board that is under $69 you could base a Robot 
around.
https://www.mouser.com/ProductDetail/STMicroelectronics/STM32MP157A-DK1/?qs=9r4v7xj2LnkgRwGGD4%252BIjQ%3D%3D

It is relatively new, and not for the faint of heart. I would recommend 
getting to understand the M4, the ST CubeIDE development tools, and how 
ST does things with the HAL before you jump into this beast. To program 
the M4, the IDE connects to Linux with Ethernet copies it over, and the 
A7 writes to the M4 memory.

When I was doing a resonant power supply we had a PID loop that was 
executed every clock, between 25 and 100 kHz.  Our goal was to adjust 
the input voltage (on a slower loop) so the frequency was 50 kHz 
(resonance most efficient) and the clock by clock loop would deal with 
abrupt current changes by changing the frequency higher to shed power 
and make the frequency lower to provide more power. We had to compensate 
for the sample time in the PID equation.  It is possible to do. But 
Python would not do it.

We were dealing with a power supply for a laptop PC. Where to save power 
they would shut parts of the processor and peripherals completely off 
when not needed, lowering the current, and turning things on quickly 
when needed causing the current to increase. These events would happen 
randomly sometimes multiple times a second.

Kip

On 2/18/21 3:58 PM, David Anderson via DPRGlist wrote:
> Howdy,
>
> Not to be overly pedantic...
>
> But another solution to these timing problems is to use a micro 
> controller that is not running Linux to do your real time stuff. The 
> STM32 Nucleo board I'm using with an RTOS has a context switch time 
> which is reliably less than 10 us (which is actually the least I can 
> measure).
>
> I understand the arguments for using Linux.  I've been using it both 
> at work and home since the early 1990s.  But not on robots. If the 
> argument is that it makes robotly things easier, I'm not sure anyone's 
> experience bears that out.
>
> Right tool for the job, as the old guys use to say.
>
> cheers!
> dpa
>
>
> On 2/18/21 11:03 AM, John Swindle via DPRGlist wrote:
>>
>> */[EXTERNAL SENDER]/*
>>
>> Going back to a previous topic regarding jitter in timing intervals.
>>
>> Why can't parameters used in calculations be scaled by the actual 
>> sample interval? I understand 50ms is chosen because it gives optimum 
>> control without undue overhead. When the actual interval is, say, 
>> 47ms, why not scale the time-related parameters to 47/50 of what they 
>> nominally are, just for that interval? If the next interval is 74ms, 
>> scale the parameters to 74/50. Is this impractical? Is the 
>> uncertainty of measuring the time interval too large? This is, if 
>> Python says the time interval is 47ms, is the error, say, +/- 10ms?
>>
>> Related to this: If the sample-to-sample timing error is large or 
>> jittery, but if you have confidence that the long-term timing is 50ms 
>> per interval, then why can't the current calculations be smoothed, 
>> assuming that each calculation is jittery due to timing inaccuracy? 
>> Does the robot really make snap decisions based on a single interval?
>>
>> Most CPU datasheets require the clock to have 50 to 100ppm accuracy. 
>> That sounds good, but for audio processing, it means streams coming 
>> from different sources will beat with one another. I have to resample 
>> the streams to match the actual sample rate of one of the streams. I 
>> use a metronome signal that each gadget processes. That doesn't mean 
>> any of the timings are correct (including the timing of the 
>> metronome). It just means they are all forced to look like they are 
>> the same. I say this because resampling is like what I'm suggesting: 
>> Scale the calculations to the actual interval.
>>
>> Am I missing something here?
>>
>> John Swindle
>>
>> -----Original Message-----
>> From: Murray Altheim via DPRGlist <dprglist at lists.dprg.org>
>> To: Chris N <netterchris at gmail.com>; dprglist at lists.dprg.org 
>> <dprglist at lists.dprg.org>
>> Sent: Thu, Feb 11, 2021 4:06 am
>> Subject: Re: [Dprglist] PID-tuned Clock in Python?
>>
>> On 11/02/21 3:13 pm, Chris N wrote:> I don’t think you have a “clock 
>> accuracy” issue.  I’m pretty sure
>> > the hardware clocks, as in crystal + PLL etc., in things like the
>> > Pi, are plenty accurate for our needs.
>>
>> Hi Chris,
>>
>> First, thanks very much, I appreciate your thoughtful reply.
>>
>> Yes, I understand that it's not the accuracy of the Raspberry Pi's
>> system clock that is in question, but the accuracy of time loops
>> when implemented in a Python application using a single or multiple
>> threads, executing within a time-sharing operating system like Linux.
>>
>> This is clearly where a microcontroller (like an Arduino, STM32,
>> ESP32, Pico RP2040, etc.) has its advantages. As I noted, my
>> MacBook was actually worse than my Raspberry Pi, which in this
>> light is perhaps not so surprising, i.e., the MacBook's processor
>> is a lot busier.
>>
>> My 3.5GHz multi-core i7 workstation is clearly giving at least a
>> whole core to the Python application and not struggling, so its
>> performance is very close to expected norms. But that's not the
>> case on the Pi, where I'll actually be using this timing loop.
>>
>> [...]
>> > Now regarding the PID idea:
>> >
>> > Say we have a loop that we want to run at a fixed rate. Say 20Hz / 
>> 50.0ms.
>> >
>> > Using PID to improve the timing accuracy of such loops is certainly
>> > an interesting idea, but I believe PID makes things worse in this case.
>>
>> I'm not clear that this is the case yet. My observation was the the PID
>> loop was improving things somewhat significantly, but more on that below.
>>
>> > See output from your clock_test.py below.   I ran this on my Pi 3.
>> > I deliberately used a tough background load to amplify the effect
>> > (stress –vm 4 –vm-bytes 128M) , but even with normal loads the
>> > negative effect of the PID can be observed, just the errors would
>> > be much smaller.
>>
>> Agreed, understood.
>>
>> > In line 6 you can see the 27ms error.
>> > [...]
>> > The reason I think PID is a bad idea here is because the nature of
>> > the disturbance is simply too random and its very intermittent.
>> > The best you can do really is to use basic loop timing logic to
>> > ensure that the next iteration starts at the right time, despite
>> > the fact that this iteration took an unusual amount of time or
>> > sleep() took an unusual amount of time. With PID you end up  > 
>> over-compensating and you are effectively hurting the timing of
>> > subsequent iterations.
>>
>> I certainly agree based on both logic and my observations that the
>> disturbances are intermittent, and from the perspective of the
>> Python program (i.e., it not knowing what else is running), would
>> appear entirely randomly.
>>
>> But I'm not clear the PID loop is over-compensating in those cases.
>> When a disturbance is within one 20Hz clock cycle, there is no real
>> fix for that in software. I entirely agree with you in those cases.
>> But when the CPU is really busy with some longer-term process and
>> that activity extends say over several seconds, the PID (I believe)
>> would tend to compensate better than with no PID.
>>
>> > There are straight forward ways to deal with the fact that
>> > time.sleep(x) doesn’t sleep for exactly x amount of time, and the
>> > fact that the amount of work which needs to be done every iteration
>> > is not 100% constant.  A python version of such a fixed-rate loop > 
>> is here: https://github.com/nettercm/timing 
>> <https://github.com/nettercm/timing%C2%A0%C2%A0>I typically use
>> > similar loop timing logic in other languages and sometimes even
>> > on a microcontroller.
>> >
>> > In pseudo python it looks as follows. [...]
>> If I'm reading your code correctly, this is kinda what I was doing
>> before I adopted the PID into the mix. I had a fixed trim value on
>> the Rate, and when the 50ms loop was consistently a bit slow or a
>> bit fast I'd just modify the trim value. It was trying to auto-correct
>> that trim value that led me to using a PID loop. Now, a fixed trim
>> would obviously not take into account those nasty intermittent surges
>> or lags, but is certainly simpler to implement and a lot less
>> mysterious in use than a PID loop.
>>
>> So if the PID turns out to be causing actual problems as you suggest,
>> I can set the flag to disable it, and maybe re-implement the fixed
>> trim. I've been setting the PID's kp constant from an RGB LED
>> potentiometer like:
>>
>> https://shop.pimoroni.com/products/rgb-potentiometer-breakout
>>
>> so if I end up disabling the PID I could manually set the trim from
>> the potentiometer, then just fix it in my YAML configuration. This
>> would be a "best guess" on a fixed, constant trim value.
>>
>> I think we both agree that there's nothing anyone can do to get a
>> truly consistent loop using Python on Linux, but if I'm willing to
>> accept that those intermittent <50ms disturbances are also unfixable,
>> then the PID does still at least (IMO) manage the longer lags and
>> surges of the clock. This *seems* to be what I'm seeing in the
>> console outputs.
>>
>> The whole subject of PID is all a bit dream-catcher, Voynich Manuscript,
>> ouija board, patchouli incense kind of thing... verra-mysterious.
>>
>>
>> Cheers,
>>
>> Murray
>>
>>
>> ...........................................................................
>> Murray Altheim <murray18 at altheim dot com>                     = =  ===
>> http://www.altheim.com/murray/                                     
>> ===  ===
>>                 = =  ===
>>     In the evening
>>     The rice leaves in the garden
>>     Rustle in the autumn wind
>>     That blows through my reed hut.
>>             -- Minamoto no Tsunenobu
>>
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