added: custom linear inequality constraints for LinMPC and NonLinMPC#310
Merged
franckgaga merged 44 commits intomainfrom Feb 6, 2026
Merged
added: custom linear inequality constraints for LinMPC and NonLinMPC#310franckgaga merged 44 commits intomainfrom
LinMPC and NonLinMPC#310franckgaga merged 44 commits intomainfrom
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This is the letter that has the lowest amount of confusion. It is used in the MHE/KF but only with a hat.
It is not supported for now, I will add the support in a following PR.
The error message is clearer if the tests do not pass.
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #310 +/- ##
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+ Coverage 98.33% 98.35% +0.02%
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Files 29 29
Lines 5155 5296 +141
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+ Hits 5069 5209 +140
- Misses 86 87 +1 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Benchmark Results (Julia v1)Time benchmarks
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This PR introduces custom nonlinear inequality constraints for
LinMPCandNonLinMPCobjects. The constructor of both objects now accepts 4 new keyword arguments:Wy=nothing: custom linear constraint matrix for output.Wu=nothing: custom linear constraint matrix for manipulated input.Wd=nothing: custom linear constraint matrix for meas. disturbance.Wr=nothing: custom linear constraint matrix for output setpoint.and the
setconstraint!method now accept these keyword arguments:wmin=fill(-Inf,nw)/wmax=fill(+Inf,nw): custom linear constraint boundc_wmin=fill(1.0,nw)/c_wmax=fill(1.0,nw):wmin/wmaxsoftness weightWmaxorC_wmin: for time-varying constraintsThe custom nonlinear inequality constraints are defined as a linear combination of the output prediction$\mathbf{\hat{y}}$ , manipulated input $\mathbf{u}$ , measured disturbance prediction $\mathbf{\hat{d}}$ and setpoint prediction $\mathbf{\hat{r}_y}$ , as described in the extended help section of
setconstraint!:I chose to incorporate the output prediction$\mathbf{\hat{y}}$ instead of state prediction $\mathbf{\hat{x}}$ in them mainly because its associated prediction matrices are already constructed and available inside $\mathbf{C}$ and $\mathbf{D_d}$ matrices, and adjusting the
LinMPCandNonLinMPCobjects. This is non-limiting in practice since you can always output the states that are needed by modifying the state-spacei_ymargument of the state estimator if these states are not measured.Note that C code generation is not supported for now but I will add the support in the following PR.