[Diy_efi] 60-2 Toothwheel - Kalman Filter or ( EKF ) usage

[Alex Ruiz]

Thanks for all the links and experience related to this subject. I'm
convinced that Kalman filtering is a really powerful tool but, it's
something that I better apply in "future" applications.

SIMULINK has a Kalman filter block that I feel like playing with, automatic
code generation can also be used on that block.

Without filter implementation, the controller works pretty nasty when noise
is added into the cam or crank line ( that goes straight into the
capture/compare unit ), specially in low RPM. Now I must work on this
filtering process you described. Looking forward to run simulation with the
filter algorithm. I have about 97% of the system fully functional on
simulation.

Regards,
Alex


2007/4/9, cobraman at insightbb.com <cobraman at insightbb.com>:
>
> Bruce,
>
> I'm off the wall again. Is it feasable to fudge the correction factors
> from throttle position or TPS and intake vacuum? or delta TPS?  Accel
> enrichment? Steady state could use 0 factors and others estimate
> acceleration rate. Whadayathink?   Tom
>
> ----- Original Message -----
> From: Bruce A Bowling <bbowling at earthlink.net>
> Date: Friday, April 6, 2007 15:35
> Subject: Re: [Diy_efi] 60-2 Toothwheel - Kalman Filter or ( EKF ) usage
> To: Mike <niche at iinet.net.au>, diy_efi at diy-efi.org
>
> > >
> > >mmm, I am guessing that a linear interpolation and table lookup
> > for accel/decel add/sub would be far quicker and use less
> > >register space & memory etc ?
> > >
> >
> > Guess again. Here is the actual implementation:
> > errt = dt3[ICint] - dtpred;
> >  ....
> > // alpha-beta-gamma filter prediction
> > dts = dtpred + ((inpram.alpha * errt));
> > tddts = tddtpred + ((inpram.beta * errt));
> > t2dddts = t2dddts + ((inpram.gamma * errt));
> > dtpred = dts + tddts + t2dddts ;
> > tddtpred = tddts + t2dddts;
> >
> > Three multiplies, 6 additions, one subtraction. One temporary
> > storage location. No division.
> >
> >
> > The 'alternative' method is to use a Mx + B form, with a table
> > search lookup to find a acceleration factor to apply.
> > First build up "M" (time derivative). Can be done with time
> > period calculation subtaction, current point from previous. Save
> > result. Next, take another subtraction to determine the change
> > between the last delta_t and this one, and use to perform table
> > lookup function. This means a loop and comparing a value against
> > a list of values (note that this list lives in memory, taking up
> > space). When the value is bracketed, grab the factor and either
> > add or multiply, depending on implementation. This "list" needs
> > to be sufficiently populated to deal with the wide range of
> > possible correction values required for the range of delta-t's
> > experienced. Anywhere from a few microseconds up. The more
> > values the more potential checks and the more memory space
> > required to hold the compared values and the factors.
> >
> > Go ahead and implement this. Then deal with the fact that either
> > the "factors" are coarsely-spaced to save memory and hence the
> > acceleration error will build up (integral windup, been there,
> > done that), or the table becomes huge with lots of comparisons.
> >
> > Also - in an earlier you stated that a predictor-corrector setup
> > was not needed. Guess what? The "Mx + B" is really an linear
> > interpolation, using two measured points to predict an event
> > into the future. And the two last obtained points are the
> > correction update. You have just come up with a predictor-
> > corrector scheme. But then you said it is not needed. But here
> > it is nonetheless.
> >
> > - Bruce
> >
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