Merge pull request #294 from JuliaControl/nl_con_getinfo

added: optimal nonlinear constraint vectors in `getinfo`

Author: franckgaga

src/ModelPredictiveControl.jl

@@ -10,7 +10,8 @@ using RecipesBase
 using DifferentiationInterface: ADTypes.AbstractADType, AutoForwardDiff
 using DifferentiationInterface: AutoSparse, SecondOrder
 using DifferentiationInterface: gradient, jacobian, hessian
-using DifferentiationInterface: value_and_gradient, value_gradient_and_hessian
+using DifferentiationInterface: value_and_gradient, value_and_jacobian
+using DifferentiationInterface: value_gradient_and_hessian
 using DifferentiationInterface: gradient!, value_and_gradient!, prepare_gradient
 using DifferentiationInterface: jacobian!, value_and_jacobian!, prepare_jacobian 
 using DifferentiationInterface: hessian!, value_gradient_and_hessian!, prepare_hessian

src/controller/execute.jl

@@ -112,8 +112,10 @@ are also available:
 - `:gc`: custom nonlinear constraints values at the optimum, ``\mathbf{g_c}``
 - `:∇J` or *`:nablaJ`* : optimal gradient of the objective function, ``\mathbf{\nabla} J``
 - `:∇²J` or *`:nabla2J`* : optimal Hessian of the objective function, ``\mathbf{\nabla^2}J``
+- `:g` : optimal nonlinear inequality constraint values, ``\mathbf{g}``
 - `:∇g` or *`:nablag`* : optimal Jacobian of the inequality constraint, ``\mathbf{\nabla g}``
 - `:∇²ℓg` or *`:nabla2lg`* : optimal Hessian of the inequality Lagrangian, ``\mathbf{\nabla^2}\ell_{\mathbf{g}}``
+- `:geq` : optimal nonlinear equality constraint values, ``\mathbf{g_{eq}}``
 - `:∇geq` or *`:nablageq`* : optimal Jacobian of the equality constraint, ``\mathbf{\nabla g_{eq}}``
 - `:∇²ℓgeq` or *`:nabla2lgeq`* : optimal Hessian of the equality Lagrangian, ``\mathbf{\nabla^2}\ell_{\mathbf{g_{eq}}}``
 

src/controller/nonlinmpc.jl

@@ -526,8 +526,8 @@ end
 """
     addinfo!(info, mpc::NonLinMPC) -> info
 
-For [`NonLinMPC`](@ref), add `:sol`, the custom nonlinear objective `:JE`, the custom
-constraint `:gc`, and the various derivatives.
+For [`NonLinMPC`](@ref), add `:sol`, the custom nonlinear objective `:JE`, the nonlinear
+constraint vectors and the various derivatives.
 """
 function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
     # --- variables specific to NonLinMPC ---
@@ -585,7 +585,7 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
         update_predictions!(ΔŨ, x̂0end, Ue, Ŷe, U0, Ŷ0, Û0, K0, X̂0, gc, g, geq, mpc, Z̃)
         return nothing
     end
-    ∇g = jacobian(g!, g, mpc.jacobian, mpc.Z̃, ∇g_cache...)
+    g, ∇g = value_and_jacobian(g!, g, mpc.jacobian, mpc.Z̃, ∇g_cache...)
     if !isnothing(mpc.hessian) && any(old_i_g)
         @warn(
             "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
@@ -618,7 +618,7 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
         update_predictions!(ΔŨ, x̂0end, Ue, Ŷe, U0, Ŷ0, Û0, K0, X̂0, gc, g, geq, mpc, Z̃)
         return nothing
     end
-    ∇geq = jacobian(geq!, geq, mpc.jacobian, mpc.Z̃, geq_cache...)
+    geq, ∇geq = value_and_jacobian(geq!, geq, mpc.jacobian, mpc.Z̃, geq_cache...)
     if !isnothing(mpc.hessian) && con.neq > 0
         @warn(
             "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
@@ -642,8 +642,10 @@ function addinfo!(info, mpc::NonLinMPC{NT}) where NT<:Real
     end
     info[:∇J] = ∇J
     info[:∇²J] = ∇²J
+    info[:g] = g
     info[:∇g] = ∇g
     info[:∇²ℓg] = ∇²ℓg
+    info[:geq] = geq
     info[:∇geq] = ∇geq
     info[:∇²ℓgeq] = ∇²ℓgeq
     # --- non-Unicode fields ---

src/estimator/mhe/execute.jl

@@ -94,12 +94,11 @@ following fields:
 - `:D`   : measured disturbances over ``N_k``, ``\mathbf{D}``
 - `:sol` : solution summary of the optimizer for printing
 
-For [`NonLinModel`](@ref), it also includes the following derivative fields:
+For [`NonLinModel`](@ref), it also includes the following fields:
 
-- `:JE`: economic cost value at the optimum, ``J_E``
-- `:gc`: custom nonlinear constraints values at the optimum, ``\mathbf{g_c}``
 - `:∇J` or *`:nablaJ`* : optimal gradient of the objective function, ``\mathbf{\nabla} J``
 - `:∇²J` or *`:nabla2J`* : optimal Hessian of the objective function, ``\mathbf{\nabla^2}J``
+- `:g` : optimal nonlinear inequality constraint values, ``\mathbf{g}``
 - `:∇g` or *`:nablag`* : optimal Jacobian of the inequality constraint, ``\mathbf{\nabla g}``
 - `:∇²ℓg` or *`:nabla2lg`* : optimal Hessian of the inequality Lagrangian, ``\mathbf{\nabla^2}\ell_{\mathbf{g}}``
 
@@ -220,7 +219,7 @@ function addinfo!(
         update_prediction!(V̂, X̂0, û0, k0, ŷ0, g, estim, Z̃)
         return nothing
     end
-    ∇g = jacobian(g!, g, estim.jacobian, estim.Z̃, ∇g_cache...)
+    g, ∇g = value_and_jacobian(g!, g, estim.jacobian, estim.Z̃, ∇g_cache...)
     if !isnothing(estim.hessian) && any(old_i_g)
         @warn(
             "Retrieving optimal Hessian of the Lagrangian is not fully supported yet.\n"*
@@ -241,6 +240,7 @@ function addinfo!(
     estim.con.i_g .= old_i_g # restore original finite/infinite constraint indices
     info[:∇J] = ∇J
     info[:∇²J] = ∇²J
+    info[:g] = g
     info[:∇g] = ∇g
     info[:∇²ℓg] = ∇²ℓg
     # --- non-Unicode fields ---