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Merge pull request #238 from control-toolbox/234-remove-ctproblemsjl-dependence
remove CTProblems dependence
2 parents 64e0904 + 862251d commit 1ba10a4

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8 files changed

+210
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‎test/Goddard.jl‎

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function Goddard()
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# parameters
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Cd = 310
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Tmax = 3.5
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β = 500
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b = 2
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t0 = 0
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r0 = 1
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v0 = 0
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vmax = 0.1
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m0 = 1
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mf = 0.6
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x0 = [ r0, v0, m0 ]
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# the model
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@def ocp begin
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# parameters
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Cd = 310
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Tmax = 3.5
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β = 500
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b = 2
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t0 = 0
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r0 = 1
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v0 = 0
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vmax = 0.1
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m0 = 1
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mf = 0.6
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x0 = [ r0, v0, m0 ]
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# variables
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tf ∈ R, variable
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t ∈ [ t0, tf ], time
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x ∈ R³, state
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u ∈ R, control
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r = x₁
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v = x₂
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m = x₃
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# constraints
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0 ≤ u(t) ≤ 1, (u_con)
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r(t) ≥ r0, (x_con_rmin)
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0 ≤ v(t) ≤ vmax, (x_con_vmax)
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x(t0) == x0, (initial_con)
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m(tf) == mf, (final_con)
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# dynamics
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ẋ(t) == F0(x(t)) + u(t)*F1(x(t))
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# objective
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r(tf) → max
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end
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# dynamics
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function F0(x)
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r, v, m = x
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D = Cd * v^2 * exp(-β*(r - 1))
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return [ v, -D/m - 1/r^2, 0 ]
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end
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function F1(x)
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r, v, m = x
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return [ 0, Tmax/m, -b*Tmax ]
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end
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#
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objective = 1.0125762700748699
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return ocp, objective
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end

‎test/Project.toml‎

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[deps]
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CTProblems = "45d9ea3f-a92f-411f-833f-222dd4fb9cd8"
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DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
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NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
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OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
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SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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‎test/runtests.jl‎

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using OptimalControl
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using Test
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using LinearAlgebra
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using CTProblems
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using SciMLBase
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using NonlinearSolve
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using DifferentialEquations
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using OrdinaryDiffEq
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using NLPModelsIpopt
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include("Goddard.jl")
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#
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@testset verbose = true showtiming = true "Optimal control tests" begin
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for name ∈ (

‎test/test_basic.jl‎

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@@ -10,7 +10,7 @@ function test_basic()
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∫( 0.5u(t)^2 ) → min
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end
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sol = solve(ocp)
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sol = solve(ocp; display=false)
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@test sol.objective ≈ 6 atol=1e-2
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end

‎test/test_goddard_direct.jl‎

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function test_goddard_direct()
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# goddard with state constraint - maximize altitude
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prob = Problem(:goddard, :classical, :altitude, :x_dim_3, :u_dim_1, :mayer, :x_cons, :u_cons, :singular_arc)
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ocp = prob.model
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ocp, objective = Goddard()
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# initial guess (constant state and control functions)
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init = (state=[1.01, 0.05, 0.8], control=0.1, variable=0.2)
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sol = solve(ocp, grid_size=10, print_level=0, init=init)
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@test sol.objective ≈ prob.solution.objective atol=5e-3
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sol = solve(ocp; grid_size=10, display=false, init=init)
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@test sol.objective ≈ objective atol=5e-3
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end

‎test/test_goddard_indirect.jl‎

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function test_goddard_indirect()
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3-
prob = Problem(:goddard, :classical, :altitude, :x_dim_3, :u_dim_1, :mayer, :x_cons, :u_cons, :singular_arc)
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ocp = prob.model
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sol = prob.solution
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title = prob.title
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# parameters
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Cd = 310
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Tmax = 3.5
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β = 500
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b = 2
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t0 = 0
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r0 = 1
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v0 = 0
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vmax = 0.1
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m0 = 1
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mf = 0.6
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x0 = [ r0, v0, m0 ]
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#
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remove_constraint!(ocp, :x_con_rmin)
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g(x) = vmax-constraint(ocp, :x_con_vmax)(x, Real[]) # g(x, u) ≥ 0 (cf. nonnegative multiplier)
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final_mass_cons(xf) = constraint(ocp, :final_con)(x0, xf, Real[])-mf
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26-
function F0(x)
27-
r, v, m = x
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D = Cd * v^2 * exp(-β*(r - 1))
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return [ v, -D/m - 1/r^2, 0 ]
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end
31-
32-
function F1(x)
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r, v, m = x
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return [ 0, Tmax/m, -b*Tmax ]
35-
end
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37-
# bang controls
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u0 = 0
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u1 = 1
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# singular control
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H0 = Lift(F0)
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H1 = Lift(F1)
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H01 = @Lie {H0, H1}
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H001 = @Lie {H0, H01}
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H101 = @Lie {H1, H01}
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us(x, p) = -H001(x, p) / H101(x, p)
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# boundary control
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ub(x) = -Lie(F0, g)(x) / Lie(F1, g)(x)
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μ(x, p) = H01(x, p) / Lie(F1, g)(x)
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53-
# flows
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f0 = Flow(ocp, (x, p, v) -> u0)
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f1 = Flow(ocp, (x, p, v) -> u1)
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fs = Flow(ocp, (x, p, v) -> us(x, p))
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fb = Flow(ocp, (x, p, v) -> ub(x), (x, u, v) -> g(x), (x, p, v) -> μ(x, p))
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59-
# shooting function
60-
function shoot!(s, p0, t1, t2, t3, tf) # B+ S C B0 structure
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62-
x1, p1 = f1(t0, x0, p0, t1)
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x2, p2 = fs(t1, x1, p1, t2)
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x3, p3 = fb(t2, x2, p2, t3)
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xf, pf = f0(t3, x3, p3, tf)
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s[1] = final_mass_cons(xf)
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s[2:3] = pf[1:2] - [ 1, 0 ]
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s[4] = H1(x1, p1)
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s[5] = H01(x1, p1)
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s[6] = g(x2)
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s[7] = H0(xf, pf) # free tf
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end
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# tests
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t1 = 0.025246759388000528
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t2 = 0.061602092906721286
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t3 = 0.10401664867856217
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tf = 0.20298394547952422
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p0 = [3.9428857983400074, 0.14628855388160236, 0.05412448008321635]
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# test shooting function
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s = zeros(eltype(p0), 7)
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shoot!(s, p0, t1, t2, t3, tf)
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s_guess_sol = [-0.02456074767656735,
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-0.05699760226157302,
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0.0018629693253921868,
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-0.027013078908634858,
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-0.21558816838342798,
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-0.0121146739026253,
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0.015713236406057297]
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@test s ≈ s_guess_sol atol=1e-6
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#
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ξ0 = [ p0 ; t1 ; t2 ; t3 ; tf ]
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#
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foo!(s, ξ, λ) = shoot!(s, ξ[1:3], ξ[4], ξ[5], ξ[6], ξ[7])
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#sol = fsolve(foo!, ξ0, show_trace=true); println(sol)
100-
prob = NonlinearProblem(foo!, ξ0)
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#sol = NonlinearSolve.solve(prob)
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sol = solve(prob)
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p0 = sol.u[1:3]
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t1 = sol.u[4]
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t2 = sol.u[5]
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t3 = sol.u[6]
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tf = sol.u[7];
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110-
shoot!(s, p0, t1, t2, t3, tf)
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112-
#@test sol.converged
113-
@test SciMLBase.successful_retcode(sol)
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@test norm(s) < 1e-6
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#
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include("Goddard.jl")
5+
ocp, obj = Goddard()
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7+
# parameters
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Cd = 310
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Tmax = 3.5
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β = 500
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b = 2
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t0 = 0
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r0 = 1
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v0 = 0
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vmax = 0.1
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m0 = 1
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mf = 0.6
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x0 = [ r0, v0, m0 ]
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20+
#
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remove_constraint!(ocp, :x_con_rmin)
22+
g(x) = vmax-constraint(ocp, :x_con_vmax)(x, Real[]) # g(x, u) ≥ 0 (cf. nonnegative multiplier)
23+
final_mass_cons(xf) = constraint(ocp, :final_con)(x0, xf, Real[])-mf
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25+
function F0(x)
26+
r, v, m = x
27+
D = Cd * v^2 * exp(-β*(r - 1))
28+
return [ v, -D/m - 1/r^2, 0 ]
29+
end
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31+
function F1(x)
32+
r, v, m = x
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return [ 0, Tmax/m, -b*Tmax ]
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end
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# bang controls
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u0 = 0
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u1 = 1
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# singular control
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H0 = Lift(F0)
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H1 = Lift(F1)
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H01 = @Lie {H0, H1}
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H001 = @Lie {H0, H01}
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H101 = @Lie {H1, H01}
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us(x, p) = -H001(x, p) / H101(x, p)
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# boundary control
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ub(x) = -Lie(F0, g)(x) / Lie(F1, g)(x)
50+
μ(x, p) = H01(x, p) / Lie(F1, g)(x)
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# flows
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f0 = Flow(ocp, (x, p, v) -> u0)
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f1 = Flow(ocp, (x, p, v) -> u1)
55+
fs = Flow(ocp, (x, p, v) -> us(x, p))
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fb = Flow(ocp, (x, p, v) -> ub(x), (x, u, v) -> g(x), (x, p, v) -> μ(x, p))
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# shooting function
59+
function shoot!(s, p0, t1, t2, t3, tf) # B+ S C B0 structure
60+
61+
x1, p1 = f1(t0, x0, p0, t1)
62+
x2, p2 = fs(t1, x1, p1, t2)
63+
x3, p3 = fb(t2, x2, p2, t3)
64+
xf, pf = f0(t3, x3, p3, tf)
65+
s[1] = final_mass_cons(xf)
66+
s[2:3] = pf[1:2] - [ 1, 0 ]
67+
s[4] = H1(x1, p1)
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s[5] = H01(x1, p1)
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s[6] = g(x2)
70+
s[7] = H0(xf, pf) # free tf
71+
72+
end
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# tests
75+
t1 = 0.025246759388000528
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t2 = 0.061602092906721286
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t3 = 0.10401664867856217
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tf = 0.20298394547952422
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p0 = [3.9428857983400074, 0.14628855388160236, 0.05412448008321635]
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81+
# test shooting function
82+
s = zeros(eltype(p0), 7)
83+
shoot!(s, p0, t1, t2, t3, tf)
84+
s_guess_sol = [-0.02456074767656735,
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-0.05699760226157302,
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0.0018629693253921868,
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-0.027013078908634858,
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-0.21558816838342798,
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-0.0121146739026253,
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0.015713236406057297]
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@test s ≈ s_guess_sol atol=1e-6
92+
93+
#
94+
ξ0 = [ p0 ; t1 ; t2 ; t3 ; tf ]
95+
96+
#
97+
foo!(s, ξ, λ) = shoot!(s, ξ[1:3], ξ[4], ξ[5], ξ[6], ξ[7])
98+
#sol = fsolve(foo!, ξ0, show_trace=true); println(sol)
99+
prob = NonlinearProblem(foo!, ξ0)
100+
#sol = NonlinearSolve.solve(prob)
101+
sol = solve(prob)
102+
103+
p0 = sol.u[1:3]
104+
t1 = sol.u[4]
105+
t2 = sol.u[5]
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t3 = sol.u[6]
107+
tf = sol.u[7];
108+
109+
shoot!(s, p0, t1, t2, t3, tf)
110+
111+
#@test sol.converged
112+
@test SciMLBase.successful_retcode(sol)
113+
@test norm(s) < 1e-6
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116115
end

‎test/test_grid.jl‎

Lines changed: 1 addition & 1 deletion
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@@ -13,7 +13,7 @@ function test_grid()
1313
objective!(ocp, :lagrange, (x, u) -> (u[1]+u[2])^2)
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1515
time_grid = [0,0.1,0.3,0.6,0.98,0.99,1]
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sol = solve(ocp, time_grid=time_grid, print_level=0)
16+
sol = solve(ocp; time_grid=time_grid, display=false)
1717
@test sol.objective ≈ 0.309 rtol=1e-2
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end

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