added: test for nonnegative values for diagonal weights

Author: franckgaga

src/controller/construct.jl

@@ -817,9 +817,12 @@ function validate_weights(model, Hp, Hc, M_Hp, N_Hc, L_Hp, C, E=nothing)
     size(M_Hp) ≠ (nM,nM) && throw(ArgumentError("M_Hp size $(size(M_Hp)) ≠ (ny*Hp, ny*Hp) ($nM,$nM)"))
     size(N_Hc) ≠ (nN,nN) && throw(ArgumentError("N_Hc size $(size(N_Hc)) ≠ (nu*Hc, nu*Hc) ($nN,$nN)"))
     size(L_Hp) ≠ (nL,nL) && throw(ArgumentError("L_Hp size $(size(L_Hp)) ≠ (nu*Hp, nu*Hp) ($nL,$nL)"))
-    !ishermitian(M_Hp) && throw(ArgumentError("M_Hp should be a positive semidefinite hermitian"))
-    !ishermitian(N_Hc) && throw(ArgumentError("N_Hc should be a positive semidefinite hermitian"))
-    !ishermitian(L_Hp) && throw(ArgumentError("L_Hp should be a positive semidefinite hermitian"))
+    (isdiag(M_Hp) && any(diag(M_Hp) .< 0)) && throw(ArgumentError("Mwt values should be nonnegative"))
+    (isdiag(N_Hc) && any(diag(N_Hc) .< 0)) && throw(ArgumentError("Nwt values should be nonnegative"))
+    (isdiag(L_Hp) && any(diag(L_Hp) .< 0)) && throw(ArgumentError("Lwt values should be nonnegative"))
+    !ishermitian(M_Hp) && throw(ArgumentError("M_Hp should be hermitian"))
+    !ishermitian(N_Hc) && throw(ArgumentError("N_Hc should be hermitian"))
+    !ishermitian(L_Hp) && throw(ArgumentError("L_Hp should be hermitian"))
     size(C) ≠ ()    && throw(ArgumentError("Cwt should be a real scalar"))
     C < 0           && throw(ArgumentError("Cwt weight should be ≥ 0"))
     !isnothing(E) && size(E) ≠ () && throw(ArgumentError("Ewt should be a real scalar"))

src/controller/linmpc.jl

@@ -103,8 +103,9 @@ in which the weight matrices are repeated ``H_p`` or ``H_c`` times by default:
     \mathbf{L}_{H_p} &= \text{diag}\mathbf{(L,L,...,L)}     
 \end{aligned}
 ```
-Time-varying weights over the horizons are also supported. The ``\mathbf{ΔU}`` includes the 
-input increments ``\mathbf{Δu}(k+j) = \mathbf{u}(k+j) - \mathbf{u}(k+j-1)`` from ``j=0`` to
+Time-varying and non-diagonal weights are also supported. Modify the last block in 
+``\mathbf{M}_{H_p}`` to specify a terminal weight. The ``\mathbf{ΔU}`` includes the input 
+increments ``\mathbf{Δu}(k+j) = \mathbf{u}(k+j) - \mathbf{u}(k+j-1)`` from ``j=0`` to
 ``H_c-1``, the ``\mathbf{Ŷ}`` vector, the output predictions ``\mathbf{ŷ}(k+j)`` from
 ``j=1`` to ``H_p``, and the ``\mathbf{U}`` vector, the manipulated inputs ``\mathbf{u}(k+j)``
 from ``j=0`` to ``H_p-1``. The slack variable ``ϵ`` relaxes the constraints, as described
@@ -120,9 +121,9 @@ arguments.
 - `Mwt=fill(1.0,model.ny)` : main diagonal of ``\mathbf{M}`` weight matrix (vector).
 - `Nwt=fill(0.1,model.nu)` : main diagonal of ``\mathbf{N}`` weight matrix (vector).
 - `Lwt=fill(0.0,model.nu)` : main diagonal of ``\mathbf{L}`` weight matrix (vector).
-- `M_Hp=diagm(repeat(Mwt,Hp))` : positive semidefinite symmetric weight ``\mathbf{M}_{H_p}``.
-- `N_Hc=diagm(repeat(Nwt,Hc))` : positive semidefinite symmetric weight ``\mathbf{N}_{H_c}``.
-- `L_Hp=diagm(repeat(Lwt,Hp))` : positive semidefinite symmetric weight ``\mathbf{L}_{H_p}``.
+- `M_Hp=diagm(repeat(Mwt,Hp))` : positive semidefinite symmetric matrix ``\mathbf{M}_{H_p}``.
+- `N_Hc=diagm(repeat(Nwt,Hc))` : positive semidefinite symmetric matrix ``\mathbf{N}_{H_c}``.
+- `L_Hp=diagm(repeat(Lwt,Hp))` : positive semidefinite symmetric matrix ``\mathbf{L}_{H_p}``.
 - `Cwt=1e5` : slack variable weight ``C`` (scalar), use `Cwt=Inf` for hard constraints only.
 - `optim=JuMP.Model(OSQP.MathOptInterfaceOSQP.Optimizer)` : quadratic optimizer used in
   the predictive controller, provided as a [`JuMP.Model`](https://jump.dev/JuMP.jl/stable/api/JuMP/#JuMP.Model)

src/controller/nonlinmpc.jl

@@ -131,9 +131,9 @@ This method uses the default state estimator :
 - `Mwt=fill(1.0,model.ny)` : main diagonal of ``\mathbf{M}`` weight matrix (vector).
 - `Nwt=fill(0.1,model.nu)` : main diagonal of ``\mathbf{N}`` weight matrix (vector).
 - `Lwt=fill(0.0,model.nu)` : main diagonal of ``\mathbf{L}`` weight matrix (vector).
-- `M_Hp=diagm(repeat(Mwt,Hp))` : positive semidefinite symmetric weight ``\mathbf{M}_{H_p}``.
-- `N_Hc=diagm(repeat(Nwt,Hc))` : positive semidefinite symmetric weight ``\mathbf{N}_{H_c}``.
-- `L_Hp=diagm(repeat(Lwt,Hp))` : positive semidefinite symmetric weight ``\mathbf{L}_{H_p}``.
+- `M_Hp=diagm(repeat(Mwt,Hp))` : positive semidefinite symmetric matrix ``\mathbf{M}_{H_p}``.
+- `N_Hc=diagm(repeat(Nwt,Hc))` : positive semidefinite symmetric matrix ``\mathbf{N}_{H_c}``.
+- `L_Hp=diagm(repeat(Lwt,Hp))` : positive semidefinite symmetric matrix ``\mathbf{L}_{H_p}``.
 - `Cwt=1e5` : slack variable weight ``C`` (scalar), use `Cwt=Inf` for hard constraints only.
 - `Ewt=0.0` : economic costs weight ``E`` (scalar). 
 - `JE=(_,_,_)->0.0` : economic function ``J_E(\mathbf{U}_E, \mathbf{Ŷ}_E, \mathbf{D̂}_E)``.