improve coverage

Author: franckgaga

Project.toml

@@ -1,7 +1,7 @@
 name = "ModelPredictiveControl"
 uuid = "61f9bdb8-6ae4-484a-811f-bbf86720c31c"
 authors = ["Francis Gagnon"]
-version = "0.14.1"
+version = "0.15.0"
 
 [deps]
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
@@ -24,15 +24,15 @@ LinearAlgebra = "1.6"
 OSQP = "0.8"
 PreallocationTools = "0.4"
 PrecompileTools = "1"
-RecipesBase = "1"
 Random = "1.6"
+RecipesBase = "1"
 julia = "1.6"
 
-
 [extras]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+DAQP = "c47d62df-3981-49c8-9651-128b1cd08617"
 
 [targets]
-test = ["Test", "Documenter", "Plots"]
+test = ["Test", "Documenter", "Plots", "DAQP"]

src/plot_sim.jl

@@ -66,7 +66,7 @@ function SimResult(
     NU, NY, NX, NX̂ = size(U_data, 2), size(Y_data, 2), size(X_data, 2), size(X̂_data, 2)
     NRy, NRu = size(Ry_data, 2), size(Ru_data, 2)
     if !(NU == NY == NX == NX̂ == NRy == NRu)
-        error("All arguments must have the same number of columns (time steps)")
+        throw(ArgumentError("All arguments must have the same number of columns (time steps)"))
     end
     size(Y_data, 2) == N || error("Y_data must be of size ($ny, $N)")
     return SimResult{NT, O}(obj, T_data, Y_data, Ry_data, Y_data, U_data, U_data, 

test/runtests.jl

@@ -4,11 +4,10 @@ using ModelPredictiveControl
 using ControlSystemsBase
 using Documenter
 using LinearAlgebra
-using JuMP, OSQP, Ipopt, ForwardDiff
+using JuMP, OSQP, Ipopt, DAQP, ForwardDiff
 using Plots
 using Test
 
-
 @testset "ModelPredictiveControl.jl" begin
 include("test_sim_model.jl")
 include("test_state_estim.jl")

test/test_plot_sim.jl

@@ -5,6 +5,7 @@ sys = [ tf(1.90,[18.0,1])   tf(1.90,[18.0,1])   tf(1.90,[18.0,1]);
 @testset "SimModel quick simulation" begin
     model = LinModel(sys, Ts, i_d=[3])
     res = sim!(model, 15)
+    display(res)
     @test isa(res.obj, LinModel)
     @test length(res.T_data) == 15
     @test res.U_data[:, 1] ≈ model.uop .+ 1
@@ -16,6 +17,8 @@ sys = [ tf(1.90,[18.0,1])   tf(1.90,[18.0,1])   tf(1.90,[18.0,1]);
     @test res_man.Y_data ≈ res.Y_data
     @test res_man.D_data ≈ res.D_data
     @test res_man.X_data ≈ res.X_data
+
+    @test_throws ArgumentError SimResult(model, [res.U_data model.uop], res.Y_data, res.D_data)
 end
 
 @testset "SimModel Plots" begin

test/test_predictive_control.jl

@@ -18,8 +18,8 @@ sys = [ tf(1.90,[18.0,1])   tf(1.90,[18.0,1])   tf(1.90,[18.0,1]);
     @test mpc5.Ñ_Hc ≈ Diagonal(diagm([repeat(Float64[3, 4], 5); [1e3]]))
     mpc6 = LinMPC(model, Lwt=[0,1], Hp=15)
     @test mpc6.L_Hp ≈ Diagonal(diagm(repeat(Float64[0, 1], 15)))
-    mpc7 = LinMPC(model, optim=JuMP.Model(Ipopt.Optimizer))
-    @test solver_name(mpc7.optim) == "Ipopt"
+    mpc7 = LinMPC(model, optim=JuMP.Model(DAQP.Optimizer))
+    @test solver_name(mpc7.optim) == "DAQP"
     kf = KalmanFilter(model)
     mpc8 = LinMPC(kf)
     @test isa(mpc8.estim, KalmanFilter)