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changed: Cwt=1e4 in custom constraints case study #384

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franckgaga merged 1 commit into from
Jun 22, 2026
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changed: Cwt=1e4 in custom constraints case study #384

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2 changes: 1 addition & 1 deletion benchmark/3_bench_predictive_control.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -575,7 +575,7 @@ function gc!(LHS, Ue, Ŷe, _, p, ϵ)
end
return nothing
end
Cwt, Pmax, nc = 1e5, 3, Hp+1
Cwt, Pmax, nc = 1e4, 3, Hp+1
x_0 = [0, 0]; x̂_0 = [0, 0, 0]; ry = [180; 0]

optim = JuMP.Model(optimizer_with_attributes(Ipopt.Optimizer,"sb"=>"yes"), add_bridges=false)
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4 changes: 2 additions & 2 deletions docs/src/manual/nonlinmpc.md
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Expand Up @@ -288,7 +288,7 @@ We construct the controller by enabling relaxation with the `Cwt` argument, and
specifying the number of custom inequality constraints `nc`:

```@example man_nonlin
Cwt, Pmax, nc = 1e5, 3, Hp+1
Cwt, Pmax, nc = 1e4, 3, Hp+1
nmpc2 = NonLinMPC(estim2; Hp, Hc, Nwt=Nwt, Mwt=[0.5, 0], Cwt, gc!, nc, p=Pmax)
nmpc2 = setconstraint!(nmpc2; umin, umax)
using JuMP; unset_time_limit_sec(nmpc2.optim) # hide
Expand Down Expand Up @@ -499,4 +499,4 @@ savefig("plot13_NonLinMPC.svg"); nothing # hide
![plot13_NonLinMPC](plot13_NonLinMPC.svg)

The computations of the successive linearization MPC are about 75 times faster than the
nonlinear MPC on average, an impressive gain for similar closed-loop performances!
nonlinear MPC on average, an impressive gain for similar closed-loop performances!
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