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AayushSabharwalAayushSabharwal
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test: add downstream analysis points tests
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‎test/downstream/analysis_points.jl‎

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using ModelingToolkit, OrdinaryDiffEq, LinearAlgebra, ControlSystemsBase
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using ModelingToolkitStandardLibrary.Mechanical.Rotational
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using ModelingToolkitStandardLibrary.Blocks
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using ModelingToolkit: connect, AnalysisPoint, t_nounits as t, D_nounits as D
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import ControlSystemsBase as CS
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@testset "Complicated model" begin
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# Parameters
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m1 = 1
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m2 = 1
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k = 1000 # Spring stiffness
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c = 10 # Damping coefficient
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@named inertia1 = Inertia(; J = m1)
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@named inertia2 = Inertia(; J = m2)
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@named spring = Spring(; c = k)
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@named damper = Damper(; d = c)
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@named torque = Torque()
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function SystemModel(u = nothing; name = :model)
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eqs = [connect(torque.flange, inertia1.flange_a)
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connect(inertia1.flange_b, spring.flange_a, damper.flange_a)
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connect(inertia2.flange_a, spring.flange_b, damper.flange_b)]
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if u !== nothing
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push!(eqs, connect(torque.tau, u.output))
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return ODESystem(eqs, t;
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systems = [
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torque,
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inertia1,
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inertia2,
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spring,
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damper,
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u
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],
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name)
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end
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ODESystem(eqs, t; systems = [torque, inertia1, inertia2, spring, damper], name)
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end
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@named r = Step(start_time = 0)
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model = SystemModel()
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@named pid = PID(k = 100, Ti = 0.5, Td = 1)
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@named filt = SecondOrder(d = 0.9, w = 10)
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@named sensor = AngleSensor()
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@named er = Add(k2 = -1)
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connections = [connect(r.output, :r, filt.input)
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connect(filt.output, er.input1)
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connect(pid.ctr_output, :u, model.torque.tau)
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connect(model.inertia2.flange_b, sensor.flange)
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connect(sensor.phi, :y, er.input2)
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connect(er.output, :e, pid.err_input)]
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closed_loop = ODESystem(connections, t, systems = [model, pid, filt, sensor, r, er],
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name = :closed_loop, defaults = [
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model.inertia1.phi => 0.0,
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model.inertia2.phi => 0.0,
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model.inertia1.w => 0.0,
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model.inertia2.w => 0.0,
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filt.x => 0.0,
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filt.xd => 0.0
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])
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sys = structural_simplify(closed_loop)
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prob = ODEProblem(sys, unknowns(sys) .=> 0.0, (0.0, 4.0))
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sol = solve(prob, Rodas5P(), reltol = 1e-6, abstol = 1e-9)
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matrices, ssys = linearize(closed_loop, AnalysisPoint(:r), AnalysisPoint(:y))
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lsys = ss(matrices...) |> sminreal
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@test lsys.nx == 8
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stepres = ControlSystemsBase.step(c2d(lsys, 0.001), 4)
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@test Array(stepres.y[:])≈Array(sol(0:0.001:4, idxs = model.inertia2.phi)) rtol=1e-4
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matrices, ssys = get_sensitivity(closed_loop, :y)
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So = ss(matrices...)
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matrices, ssys = get_sensitivity(closed_loop, :u)
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Si = ss(matrices...)
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@test tf(So) ≈ tf(Si)
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end
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@testset "Analysis points with subsystems" begin
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@named P = FirstOrder(k = 1, T = 1)
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@named C = Gain(; k = 1)
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@named add = Blocks.Add(k2 = -1)
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eqs = [connect(P.output, :plant_output, add.input2)
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connect(add.output, C.input)
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connect(C.output, :plant_input, P.input)]
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# eqs = [connect(P.output, add.input2)
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# connect(add.output, C.input)
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# connect(C.output, P.input)]
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sys_inner = ODESystem(eqs, t, systems = [P, C, add], name = :inner)
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@named r = Constant(k = 1)
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@named F = FirstOrder(k = 1, T = 3)
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eqs = [connect(r.output, F.input)
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connect(F.output, sys_inner.add.input1)]
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sys_outer = ODESystem(eqs, t, systems = [F, sys_inner, r], name = :outer)
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# test first that the structural_simplify works correctly
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ssys = structural_simplify(sys_outer)
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prob = ODEProblem(ssys, Pair[], (0, 10))
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@test_nowarn solve(prob, Rodas5())
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matrices, _ = get_sensitivity(sys_outer, sys_outer.inner.plant_input)
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lsys = sminreal(ss(matrices...))
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@test lsys.A[] == -2
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@test lsys.B[] * lsys.C[] == -1 # either one negative
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@test lsys.D[] == 1
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matrices_So, _ = get_sensitivity(sys_outer, sys_outer.inner.plant_output)
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lsyso = sminreal(ss(matrices_So...))
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@test lsys == lsyso || lsys == -1 * lsyso * (-1) # Output and input sensitivities are equal for SISO systems
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end
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@testset "multilevel system with loop openings" begin
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@named P_inner = FirstOrder(k = 1, T = 1)
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@named feedback = Feedback()
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@named ref = Step()
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@named sys_inner = ODESystem(
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[connect(P_inner.output, :y, feedback.input2)
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connect(feedback.output, :u, P_inner.input)
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connect(ref.output, :r, feedback.input1)],
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t,
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systems = [P_inner, feedback, ref])
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P_not_broken, _ = linearize(sys_inner, AnalysisPoint(:u), AnalysisPoint(:y))
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@test P_not_broken.A[] == -2
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P_broken, _ = linearize(sys_inner, AnalysisPoint(:u), AnalysisPoint(:y),
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loop_openings = [AnalysisPoint(:u)])
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@test P_broken.A[] == -1
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P_broken, _ = linearize(sys_inner, AnalysisPoint(:u), AnalysisPoint(:y),
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loop_openings = [AnalysisPoint(:y)])
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@test P_broken.A[] == -1
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Sinner = sminreal(ss(get_sensitivity(sys_inner, :u)[1]...))
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@named sys_inner = ODESystem(
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[connect(P_inner.output, :y, feedback.input2)
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connect(feedback.output, :u, P_inner.input)],
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t,
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systems = [P_inner, feedback])
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@named P_outer = FirstOrder(k = rand(), T = rand())
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@named sys_outer = ODESystem(
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[connect(sys_inner.P_inner.output, :y2, P_outer.input)
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connect(P_outer.output, :u2, sys_inner.feedback.input1)],
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t,
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systems = [P_outer, sys_inner])
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Souter = sminreal(ss(get_sensitivity(sys_outer, :sys_inner_u)[1]...))
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Sinner2 = sminreal(ss(get_sensitivity(
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sys_outer, :sys_inner_u, loop_openings = [:y2])[1]...))
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@test Sinner.nx == 1
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@test Sinner == Sinner2
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@test Souter.nx == 2
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end
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@testset "sensitivities in multivariate signals" begin
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A = [-0.994 -0.0794; -0.006242 -0.0134]
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B = [-0.181 -0.389; 1.1 1.12]
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C = [1.74 0.72; -0.33 0.33]
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D = [0.0 0.0; 0.0 0.0]
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@named P = Blocks.StateSpace(A, B, C, D)
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Pss = CS.ss(A, B, C, D)
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A = [-0.097;;]
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B = [-0.138 -1.02]
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C = [-0.076; 0.09;;]
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D = [0.0 0.0; 0.0 0.0]
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@named K = Blocks.StateSpace(A, B, C, D)
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Kss = CS.ss(A, B, C, D)
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eqs = [connect(P.output, :plant_output, K.input)
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connect(K.output, :plant_input, P.input)]
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sys = ODESystem(eqs, t, systems = [P, K], name = :hej)
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matrices, _ = Blocks.get_sensitivity(sys, :plant_input)
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S = CS.feedback(I(2), Kss * Pss, pos_feedback = true)
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@test CS.tf(CS.ss(matrices...)) ≈ CS.tf(S)
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matrices, _ = Blocks.get_comp_sensitivity(sys, :plant_input)
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T = -CS.feedback(Kss * Pss, I(2), pos_feedback = true)
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# bodeplot([ss(matrices...), T])
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@test CS.tf(CS.ss(matrices...)) ≈ CS.tf(T)
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matrices, _ = Blocks.get_looptransfer(
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sys, :plant_input)
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L = Kss * Pss
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@test CS.tf(CS.ss(matrices...)) ≈ CS.tf(L)
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matrices, _ = linearize(sys, :plant_input, :plant_output)
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G = CS.feedback(Pss, Kss, pos_feedback = true)
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@test CS.tf(CS.ss(matrices...)) ≈ CS.tf(G)
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end
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@testset "multiple analysis points" begin
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@named P = FirstOrder(k = 1, T = 1)
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@named C = Gain(; k = 1)
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@named add = Blocks.Add(k2 = -1)
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eqs = [connect(P.output, :plant_output, add.input2)
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connect(add.output, C.input)
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connect(C.output, :plant_input, P.input)]
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sys_inner = ODESystem(eqs, t, systems = [P, C, add], name = :inner)
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@named r = Constant(k = 1)
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@named F = FirstOrder(k = 1, T = 3)
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eqs = [connect(r.output, F.input)
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connect(F.output, sys_inner.add.input1)]
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sys_outer = ODESystem(eqs, t, systems = [F, sys_inner, r], name = :outer)
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matrices, _ = get_sensitivity(sys_outer, [:, :inner_plant_output])
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Ps = tf(1, [1, 1]) |> ss
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Cs = tf(1) |> ss
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G = CS.ss(matrices...) |> sminreal
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Si = CS.feedback(1, Cs * Ps)
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@test tf(G[1, 1]) ≈ tf(Si)
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end

‎test/runtests.jl‎

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@safetestset "Linearization Tests" include("downstream/linearize.jl")
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@safetestset "Linearization Dummy Derivative Tests" include("downstream/linearization_dd.jl")
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@safetestset "Inverse Models Test" include("downstream/inversemodel.jl")
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@safetestset "Analysis Points Test" include("downstream/analysis_points.jl")
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end
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if GROUP == "All" || GROUP == "Extensions"

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