update pendcart test

Author: baggepinnen

test/downstream/linearization_dd.jl

@@ -29,33 +29,34 @@ lin_outputs = [cart.s, cart.v, link1.A, link1.dA]
 lin_inputs = [force.f.u]
 
 # => nothing to remove extra defaults
-op = Dict(cart.s => 10, cart.v => 0, link1.A => -pi/2, link1.dA => 0, force.f.u => 0, link1.x1 => nothing, link1.y1 => nothing, link1.x2 => nothing, link1.x_cm => nothing)
-@test_broken begin
-    @info "named_ss"
-    G = named_ss(model, lin_inputs, lin_outputs, allow_symbolic = true, op = def,
-        allow_input_derivatives = true, zero_dummy_der = true)
-    G = sminreal(G)
-    @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, allow_symbolic = true, op = op,
-        allow_input_derivatives = true, zero_dummy_der = true)
-    lsyss, sysss = ModelingToolkit.linearize_symbolic(model, lin_inputs, lin_outputs;
-        allow_input_derivatives = true)
-
-    dummyder = setdiff(unknowns(sysss), unknowns(model))
-    def = merge(ModelingToolkit.guesses(model), def, Dict(x => 0.0 for x in dummyder))
-    def[link1.fy1] = -def[link1.g] * def[link1.m]
-
-    @test substitute(lsyss.A, def) ≈ lsys.A
-    # We cannot pivot symbolically, so the part where a linear solve is required
-    # is not reliable.
-    @test substitute(lsyss.B, def)[1:6, 1] ≈ lsys.B[1:6, 1]
-    @test substitute(lsyss.C, def) ≈ lsys.C
-    @test substitute(lsyss.D, def) ≈ lsys.D
-end
+op = Dict(cart.s => 10, cart.v => 0, link1.A => -pi / 2, link1.dA => 0, force.f.u => 0,
+    link1.x1 => nothing, link1.y1 => nothing, link1.x2 => nothing, link1.x_cm => nothing)
+@info "named_ss"
+G = named_ss(model, lin_inputs, lin_outputs; allow_symbolic = true, op,
+    allow_input_derivatives = true, zero_dummy_der = true)
+G = sminreal(G)
+@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, allow_symbolic = true, op = op,
+    allow_input_derivatives = true, zero_dummy_der = true)
+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