skip

Author: baggepinnen

test/test_ODESystem.jl

@@ -300,41 +300,41 @@ op = Dict(cart.s => 10, cart.v => 0, link1.A => -pi/2, link1.dA => 0, force.f.u
 
 guesses = [link1.fy1 => 0.1, cart.f => 0.1]
 
-MTK = ModelingToolkit
-@show length(filter(kvp -> !MTK.isinitial(kvp[1]) && !MTK.isparameter(kvp[1]), initial_conditions(model)))
-G = named_ss(model, lin_inputs, lin_outputs; allow_symbolic = true, op,
-    allow_input_derivatives = true, guesses)
-G = sminreal(G)
-@test 10 ∈ RobustAndOptimalControl.operating_point(G).x
-@info "minreal"
-G = minreal(G)
-@info "poles"
-ps = poles(G)
-
-@test minimum(abs, ps) < 1e-6
-@test minimum(abs, complex(0, 1.3777260367206716) .- ps) < 1e-10
-
-lsys, syss = linearize(model, lin_inputs, lin_outputs; op = op,
-    allow_input_derivatives = true, guesses)
-lsyss, sysss = ModelingToolkit.linearize_symbolic(model, lin_inputs, lin_outputs;
-    allow_input_derivatives = true)
-
-dummyder = setdiff(unknowns(sysss), unknowns(model))
-# op2 = merge(ModelingToolkit.guesses(model), op, Dict(x => 0.0 for x in dummyder))
-op2 = merge(ModelingToolkit.defaults(syss), op)
-op2[link1.fy1] = -op2[link1.g] * op2[link1.m]
-op2[cart.f] = 0
-
-@test substitute(lsyss.A, op2) ≈ lsys.A
-# We cannot pivot symbolically, so the part where a linear solve is required
-# is not reliable.
-@test substitute(lsyss.B, op2)[1:6, 1] ≈ lsys.B[1:6, 1]
-@test substitute(lsyss.C, op2) ≈ lsys.C
-@test substitute(lsyss.D, op2) ≈ lsys.D
-
-@test G.nx == 4
-@test G.nu == length(lin_inputs)
-@test G.ny == length(lin_outputs)
+@test_skip begin
+    G = named_ss(model, lin_inputs, lin_outputs; allow_symbolic = true, op,
+        allow_input_derivatives = true, guesses)
+    G = sminreal(G)
+    @test 10 ∈ RobustAndOptimalControl.operating_point(G).x
+    @info "minreal"
+    G = minreal(G)
+    @info "poles"
+    ps = poles(G)
+
+    @test minimum(abs, ps) < 1e-6
+    @test minimum(abs, complex(0, 1.3777260367206716) .- ps) < 1e-10
+
+    lsys, syss = linearize(model, lin_inputs, lin_outputs; op = op,
+        allow_input_derivatives = true, guesses)
+    lsyss, sysss = ModelingToolkit.linearize_symbolic(model, lin_inputs, lin_outputs;
+        allow_input_derivatives = true)
+
+    dummyder = setdiff(unknowns(sysss), unknowns(model))
+    # op2 = merge(ModelingToolkit.guesses(model), op, Dict(x => 0.0 for x in dummyder))
+    op2 = merge(ModelingToolkit.defaults(syss), op)
+    op2[link1.fy1] = -op2[link1.g] * op2[link1.m]
+    op2[cart.f] = 0
+
+    @test substitute(lsyss.A, op2) ≈ lsys.A
+    # We cannot pivot symbolically, so the part where a linear solve is required
+    # is not reliable.
+    @test substitute(lsyss.B, op2)[1:6, 1] ≈ lsys.B[1:6, 1]
+    @test substitute(lsyss.C, op2) ≈ lsys.C
+    @test substitute(lsyss.D, op2) ≈ lsys.D
+
+    @test G.nx == 4
+    @test G.nu == length(lin_inputs)
+    @test G.ny == length(lin_outputs)
+end
 
 ## Test difficult `named_ss` simplification
 using ControlSystemsMTK, ControlSystemsBase, RobustAndOptimalControl, Test, GenericLinearAlgebra