[franckgaga]

FOH for disturbances d in MPC/MHE with ShootingMethods

The NonLinMPC and the MovingHorizonEstimator transcribed with SingleShooting and MultipleShooting et solved with RungeKutta now discretize the measured disturbances $\mathbf{d}$ with first-order hold (FOH, or piecewise linear), instead of the simpler zero-order hold (ZOH). It is more adapted for sampled continuous signal, which is usually the case for measured disturbances. The feedforward compensation can be therefore slightly improved by this.

FOH for inputs u in MPC/MHE with ShootingMethods

It is also possible to discretized the manipulated inputs $\mathbf{u}$ with FOH in MPC and MHE by using the SingleShooting(1) or MultipleShooting(1). The default is still ZOH for manipulated inputs.

FOH for updatestate!(::SimModel)

It's now possible to simulate a NonLinModel and NonLinModelDAE with FOH discretization on inputs $\mathbf{u}$ and $\mathbf{d}$, using the new unext and dnext keyword argument. Note that it's information about the future, so this is non-causal. It is still useful in practice since FOH measured disturbances is slightly more realistic that the default ZOH, even if it is non-causal (no discontinuities in the signal). Doing so also remove the annoying plant-model mistmatch between the MovingHorizonEstimator and NonLinMPC with a FOH discretization and the simulated plant.

Warning

FOH discretization for updatestate!(::StateEstimator) is not supported (there are no keyword arguments in this method). First, it leads to a non-causal filter, so not so useful in practice. Second, it significantly complexify the codebase, especially for MovingHorizonEstimator so I left this out of the scope for now. Still not sure if it is worth the effort.

Closes #469

[codecov-commenter]

Codecov Report

❌ Patch coverage is 98.83721% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 97.33%. Comparing base (697d08b) to head (2ce9c89).
⚠️ Report is 77 commits behind head on main.

Files with missing lines	Patch %	Lines
src/estimator/execute.jl	90.00%	1 Missing ⚠️
src/estimator/mhe/transcription.jl	97.29%	1 Missing ⚠️
src/model/nonlinmodel.jl	80.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #495      +/-   ##
==========================================
+ Coverage   97.25%   97.33%   +0.08%     
==========================================
  Files          28       28              
  Lines        6995     7093      +98     
==========================================
+ Hits         6803     6904     +101     
+ Misses        192      189       -3     

Flag	Coverage Δ
unittests	97.33% <98.83%> (+0.08%)	⬆️

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[franckgaga]

Okay, all the MPC/MHE benchmark with hessian=true are marginally slower. It's probably because the number of colors in one of the 3 Hessians increased. The root cause is likely new RungeKutta implementation that support both ZOH and FOH. I will see if I can improve this by creating a special path for the simpler ZOH in RungeKutta(1) and RungeKutta(4) solvers.

[franckgaga]

I added a special path for ZOH in the two RungeKutta solvers. The benchmark are still slightly slower on CI and I'm not sure why. I'm not able to replicate this result locally on Win11... It's not related to the number of color since it affects the SingleShooting NMPC case on the pendulum, and there are no sparse Hessians in this case study. It maybe noise on the CI runner. Let's do one more run.

[franckgaga]

I'm not able to reproduce locally on fedora... Seems like noise.

edit: Let's try to remove the Ûi allocations at each call of the objective and constraint functions, this is something new compared to main. The are needed to compute the interpolated value of the manipulated input when h=1.