test: new test NMPC and MHE tests with FiniteDiff

Author: franckgaga

Project.toml

@@ -41,10 +41,11 @@ julia = "1.10"
 [extras]
 DAQP = "c47d62df-3981-49c8-9651-128b1cd08617"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 TestItems = "1c621080-faea-4a02-84b6-bbd5e436b8fe"
 
 [targets]
-test = ["Test", "TestItems", "TestItemRunner", "Documenter", "Plots", "DAQP"]
+test = ["Test", "TestItems", "TestItemRunner", "Documenter", "Plots", "DAQP", "FiniteDiff"]

docs/src/index.md

@@ -12,7 +12,7 @@ The objective is to provide a simple, clear and modular framework to quickly des
 predictive controllers (MPCs) in Julia, while preserving the flexibility for advanced
 real-time optimization. Modern MPCs based on closed-loop state estimators are the main focus
 of the package, but classical approaches that rely on internal models are also possible. The
-`JuMP` amd `DifferentiationInterface` dependencies allows the user to test different
+`JuMP` and `DifferentiationInterface` dependencies allows the user to test different
 optimizers and automatic differentiation (AD) backends easily if the performances of the
 default settings are not satisfactory.
 

test/3_test_predictive_control.jl

@@ -555,7 +555,9 @@ end
 end
 
 @testitem "NonLinMPC construction" setup=[SetupMPCtests] begin
-    using .SetupMPCtests, ControlSystemsBase, LinearAlgebra, JuMP, Ipopt
+    using .SetupMPCtests, ControlSystemsBase, LinearAlgebra
+    using JuMP, Ipopt, DifferentiationInterface
+    import FiniteDiff
     linmodel1 = LinModel(sys,Ts,i_d=[3])
     nmpc0 = NonLinMPC(linmodel1, Hp=15)
     @test isa(nmpc0.estim, SteadyKalmanFilter)
@@ -612,6 +614,10 @@ end
     @test nmpc17.transcription == MultipleShooting()
     @test length(nmpc17.Z̃) == linmodel1.nu*nmpc17.Hc + nmpc17.estim.nx̂*nmpc17.Hp + nmpc17.nϵ
     @test size(nmpc17.con.Aeq, 1) == nmpc17.estim.nx̂*nmpc17.Hp
+    @test_nowarn nmpc18 = NonLinMPC(nonlinmodel, Hp=10, 
+        gradient=AutoFiniteDiff(), 
+        jacobian=AutoFiniteDiff()
+    )
 
     nonlinmodel2 = NonLinModel{Float32}(f, h, Ts, 2, 4, 2, 1, solver=nothing)
     nmpc15  = NonLinMPC(nonlinmodel2, Hp=15)
@@ -629,7 +635,9 @@ end
 end
 
 @testitem "NonLinMPC moves and getinfo" setup=[SetupMPCtests] begin
-    using .SetupMPCtests, ControlSystemsBase, LinearAlgebra, ForwardDiff
+    using .SetupMPCtests, ControlSystemsBase, LinearAlgebra
+    using DifferentiationInterface
+    import FiniteDiff
     linmodel = setop!(LinModel(tf(5, [2000, 1]), 3000.0), yop=[10])
     Hp = 100
     nmpc_lin = NonLinMPC(linmodel, Nwt=[0], Hp=Hp, Hc=1)