@@ -726,7 +726,7 @@ Predict the current `ŷs` and future `Ŷs` stochastic model outputs over `Hp`.
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See [`init_stochpred`](@ref) for details on `Ŷs` and `Ks` matrices.
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"""
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- predict_stoch (mpc, estim:: StateEstimator , x̂s, d , _ ) = (estim. Cs* x̂s, mpc. Ks* x̂s)
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+ predict_stoch (mpc, estim:: StateEstimator , x̂s, _ , _ ) = (estim. Cs* x̂s, mpc. Ks* x̂s)
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"""
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predict_stoch(mpc, estim::InternalModel, x̂s, d, ym )
@@ -809,7 +809,7 @@ Optimize the `mpc` quadratic objective function for [`LinMPC`](@ref) type.
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function optim_objective! (mpc:: LinMPC , b, q̃, p)
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optim = mpc. optim
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model = mpc. estim. model
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- ΔŨ = optim[:ΔŨ ]
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+ ΔŨ:: Vector{VariableRef} = optim[:ΔŨ ]
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lastΔŨ = mpc. info. ΔŨ
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set_objective_function (optim, obj_quadprog (ΔŨ, mpc. P̃, q̃))
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set_normalized_rhs .(optim[:linconstraint ], b)
@@ -834,9 +834,9 @@ function optim_objective!(mpc::LinMPC, b, q̃, p)
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@warn " MPC termination status not OPTIMAL or LOCALLY_SOLVED ($status )"
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@debug solution_summary (optim)
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end
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- ΔŨ = isfatal (status) ? ΔŨ0 : value .(ΔŨ) # fatal status : use last value
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- J = objective_value (optim) + p # optimal objective value by adding constant p
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- return ΔŨ, J
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+ ΔŨ_val = isfatal (status) ? ΔŨ0 : value .(ΔŨ) # fatal status : use last value
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+ J_val = objective_value (optim) + p # optimal objective value by adding constant p
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+ return ΔŨ_val, J_val
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end
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"""
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