juliacontrol / modelpredictivecontrol.jl Goto Github PK

@baggepinnen

It seems that dev documentation is no longer automatically built and deployed when I commit on main. It was working perfectly before transferring to JuliaControl. Do you have any idea why ? It may be related to secrets key (in documentation.yml):

env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # If authenticating with GitHub Actions token
          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }} # If authenticating with SSH deploy key

I'm no longer able to access the secrets in repo settings since I'm not an admin.

Also, I just registered a new version. I'm waiting to see if the stable doc will be built and deployed.

edit: yep, it is also broken with the stable doc

The following is a short list of questions I have tried to answer by reading the docs, but haven't found a clear answer to

• Can I set a unique terminal cost that differs from the running cost? If I don't choose a terminal cost, what cost will be used by default? In particular, I'd like o be able to set the solution to the Ricatti equation of the corresponding LQR problem as the terminal cost matrix.
• Can I set a terminal state constraint that differs from the state constraints during the rest of the trajectory?
• Can I use non-diagonal cost matrices? In particular, my weight matrices may come from an initial LQR design, which may or may not be optimization based or use loop-transfer recovery, resulting in non-diagonal matrices.

@baggepinnen
There is a palpable hate against unicode characters, especially in keyword arguments of public functions. Some keyword arguments of this package use them e.g.: σP_0, σQ, σR and x̂_0. I personally like them for the conciseness. It is also well known that σ is a standard deviation, and ^, a modifier for an estimated value. Should we offer an alternative, something like:

function SteadyKalmanFilter(
    model::Linmodel;
    i_ym = 1:model.ny,
    σQ = fill(1/model.nx, model.nx),
    σR = fill(1, length(i_ym)),
    nint_u = 0,
    σQint_u = fill(1, max(sum(nint_u), 0)),
    nint_ym = default_nint(model, i_ym, nint_u),
    σQint_ym = fill(1, max(sum(nint_ym), 0))
    sigmaR = σR,                  # new kwarg 
    sigmaQ = σQ,                  # new kwarg
    sigmaQint_u = σQint_u,        # new kwarg
    sigmaQint_ym = σQint_ym       # new kwarg
)    
    # estimated covariances matrices (variance = σ²) :
    Q̂  = Hermitian(diagm(NT[sigmaQ;  sigmaQint_u;  sigmaQint_ym ].^2), :L)
    R̂  = Hermitian(diagm(NT[sigmaR;].^2), :L)
    return SteadyKalmanFilter{NT, SM}(model, i_ym, nint_u, nint_ym, Q̂ , R̂)
end

and something similar with x̂_0 and xhat_0 ?

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Hi,
I tried some options without success.