update tests to reflect new discrete update order

Author: baggepinnen

Project.toml

@@ -92,8 +92,8 @@ RecursiveArrayTools = "2.3"
 Reexport = "0.2, 1"
 RuntimeGeneratedFunctions = "0.5.9"
 SciMLBase = "2.0.1"
-Setfield = "0.7, 0.8, 1"
 Serialization = "1"
+Setfield = "0.7, 0.8, 1"
 SimpleNonlinearSolve = "0.1.0"
 SparseArrays = "1"
 SpecialFunctions = "0.7, 0.8, 0.9, 0.10, 1.0, 2"
@@ -110,6 +110,7 @@ julia = "1.9"
 AmplNLWriter = "7c4d4715-977e-5154-bfe0-e096adeac482"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 BifurcationKit = "0f109fa4-8a5d-4b75-95aa-f515264e7665"
+ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 ControlSystemsMTK = "687d7614-c7e5-45fc-bfc3-9ee385575c88"
 DeepDiffs = "ab62b9b5-e342-54a8-a765-a90f495de1a6"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
@@ -132,4 +133,4 @@ Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["AmplNLWriter", "BenchmarkTools", "BifurcationKit", "ControlSystemsMTK", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials", "StochasticDelayDiffEq"]
+test = ["AmplNLWriter", "BenchmarkTools", "BifurcationKit", "ControlSystemsBase", "ControlSystemsMTK", "NonlinearSolve", "ForwardDiff", "Ipopt", "Ipopt_jll", "ModelingToolkitStandardLibrary", "Optimization", "OptimizationOptimJL", "OptimizationMOI", "Random", "ReferenceTests", "SafeTestsets", "StableRNGs", "Statistics", "SteadyStateDiffEq", "Test", "StochasticDiffEq", "Sundials", "StochasticDelayDiffEq"]

test/clock.jl

@@ -115,7 +115,7 @@ z(k + 1)  ~ z′(k)
 ss = structural_simplify(sys);
 if VERSION >= v"1.7"
     prob = ODEProblem(ss, [x => 0.0, y => 0.0], (0.0, 1.0),
-        [kp => 1.0; z => 2.0; z(k + 1) => 3.0])
+        [kp => 1.0; z => 3.0; z(k + 1) => 2.0])
     sol = solve(prob, Tsit5(), kwargshandle = KeywordArgSilent)
     # For all inputs in parameters, just initialize them to 0.0, and then set them
     # in the callback.
@@ -127,17 +127,20 @@ if VERSION >= v"1.7"
         du[1] = -x + ud
     end
     function affect!(integrator, saved_values)
-        kp = integrator.p[1]
+        z_t, z = integrator.p[3], integrator.p[4]
         yd = integrator.u[1]
-        z_t = integrator.p[3]
-        z = integrator.p[4]
+        kp = integrator.p[1]
         r = 1.0
         ud = kp * (r - yd) + z
-        push!(saved_values.t, integrator.t)
-        push!(saved_values.saveval, [integrator.p[3], integrator.p[4]])
         integrator.p[2] = ud
-        integrator.p[3] = z + yd
-        integrator.p[4] = z_t
+
+        push!(saved_values.t, integrator.t)
+        push!(saved_values.saveval, [z_t, z])
+
+        # Update the discrete state
+        z_t, z = z + yd, z_t
+        integrator.p[3] = z_t
+        integrator.p[4] = z
         nothing
     end
     saved_values = SavedValues(Float64, Vector{Float64})
@@ -381,6 +384,7 @@ end
     end
 end
 
+##
 @named model = ClosedLoop()
 model = complete(model)
 
@@ -407,31 +411,35 @@ ci, varmap = infer_clocks(expand_connections(model))
 
 ssys = structural_simplify(model)
 
-timevec = 0:(d.dt):20
+timevec = 0:(d.dt):10
 
-if VERSION >= v"1.7"
-    using ControlSystems
-    P = c2d(tf(0.3, [1, 1]), d.dt)
-    C = ControlSystems.ss([1], [2], [1], [2], d.dt)
-
-    # Test the output of the continuous partition
-    G = feedback(P * C)
-    res = lsim(G, (x, t) -> [0.5], timevec)
-    y = res.y[:]
-
-    prob = ODEProblem(ssys,
-        [model.plant.x => 0.0],
-        (0.0, 20.0),
-        [model.controller.kp => 2.0; model.controller.ki => 2.0])
-    sol = solve(prob, Tsit5(), kwargshandle = KeywordArgSilent)
-    @test sol(timevec, idxs = model.plant.output.u)≈y rtol=1e-10 # The output of the continuous partition is delayed exactly one sample
-    # plot([sol(timevec .+ 1e-12, idxs=model.plant.output.u)  y])
+using ControlSystemsBase
+P = c2d(tf(0.3, [1, 1]), d.dt)
+C = ControlSystems.ss([1], [2], [1], [2], d.dt)
 
-    # Test the output of the discrete partition
-    G = feedback(C, P)
-    res = lsim(G, (x, t) -> [0.5], timevec)
-    y = res.y[:]
+# Test the output of the continuous partition
+G = feedback(P * C)
+res = lsim(G, (x, t) -> [0.5], timevec)
+y = res.y[:]
 
-    @test sol(timevec .+ 1e-10, idxs = model.controller.output.u)≈y rtol=1e-10
-    # plot([sol(timevec .+ 1e-12, idxs=model.controller.output.u)  y])
-end
+prob = ODEProblem(ssys,
+    [model.plant.x => 0.0],
+    (0.0, 10.0),
+    [model.controller.kp => 2.0; model.controller.ki => 2.0])
+
+@test_broken prob.p[9] == 1 # constant output * kp issue https://github.com/SciML/ModelingToolkit.jl/issues/2356
+prob.p[9] = 1 # constant output * kp
+sol = solve(prob, Tsit5(), kwargshandle = KeywordArgSilent)
+# plot([sol(timevec .+ 1e-12, idxs=model.plant.output.u)  y])
+
+##
+
+@test sol(timevec, idxs = model.plant.output.u)≈y rtol=1e-8 # The output of the continuous partition is delayed exactly one sample
+
+# Test the output of the discrete partition
+G = feedback(C, P)
+res = lsim(G, (x, t) -> [0.5], timevec)
+y = res.y[:]
+
+@test_broken sol(timevec .+ 1e-10, idxs = model.controller.output.u)≈y rtol=1e-8 # Broken due to discrete observed
+# plot([sol(timevec .+ 1e-12, idxs=model.controller.output.u)  y])