Update for meeting with Bosanac
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Connor
110
julia/src/inner_loop/nlp_solver.jl
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110
julia/src/inner_loop/nlp_solver.jl
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using SNOW
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export solve_mission
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struct Result
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converged::Bool
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info::Symbol
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sol::Matrix{Float64}
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end
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"""
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This function will take an initial guess to a mission profile and leverage Ipopt to solve the mission
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"""
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function solve_mission( guess::Mission_Guess;
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tol=1e-8,
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verbose::Bool=false )
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# First we need to define a function that propagates the mission all the way through
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# The function takes in a constraints vector (g) and an input vector (x) and spits out
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# the objective function. In my case, the objective will be constant.
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#
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# First we define our starting point
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x0 = Vector(guess)
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n = size(guess.phases[1].thrust_profile)[1]
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flybys = [ p.planet for p in guess.phases ]
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# And our NLP
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function optimizer!(g,x)
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# Establish initial conditions
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mass = guess.start_mass
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v∞_out = x[2:4]
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current_planet = Earth
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launch_date = Dates.unix2datetime(x[1])
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time = utc2et(Dates.format(launch_date,"yyyy-mm-ddTHH:MM:SS"))
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start = state(current_planet, time, v∞_out, mass)
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# Now, for each phase we must require:
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# - That the ending state matches (unless final leg)
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# - That the v∞_out == v∞_in (unless final leg)
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# - That the minimum height is acceptable (unless final leg)
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# - That the ending position matches (if final leg)
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# - That the ending mass is acceptable (if final leg)
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i = 0
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for flyby in flybys
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phase_params = x[ 5 + (3n+7) * i : 5 + (3n+7) * (i+1) - 1 ]
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v∞_in = phase_params[1:3]
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v∞_out = phase_params[4:6]
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tof = phase_params[7]
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thrusts = reshape(phase_params[8:end], (n,3))
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current_planet = flyby
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time += tof
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goal = state(current_planet, time, v∞_in)
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final = prop(reshape(thrusts, (n,3)), start, guess.sc, tof)[2]
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if flyby != flybys[end]
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g[1+8i:6+8i] .= final[1:6] .- goal[1:6]
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g[7+8i] = norm(v∞_out) - norm(v∞_in)
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δ = acos( ( v∞_in ⋅ v∞_out ) / ( norm(v∞_in) * norm(v∞_out) ) )
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g[8+8i] = (current_planet.μ/(v∞_in ⋅ v∞_in)) * ( 1/sin(δ/2) - 1 )
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else
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g[8length(flybys)+1:8length(flybys)+3] .= final[1:3] .- goal[1:3]
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g[8length(flybys)+4] = final[7]
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end
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start = state(current_planet, time, v∞_out, final[7])
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i += 1
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return 1.0
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end
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end
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g = zeros(8length(flybys)+4)
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optimizer!(g,x0)
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return g
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# n = size(x0)[1]
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# function f!(g,x)
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# try
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# g[1:6] .= prop(reshape(x,(n,3)), start, craft, tof, primary)[2][1:6]
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# return 1.
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# catch e
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# if isa(e,LaGuerreConway_Error) || isa(e, Mass_Error) || isa(e, PropOne_Error)
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# return 10_000.
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# else
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# rethrow
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# end
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# end
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# end
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# lower_x = -1 * ones(3n)
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# upper_x = ones(3n)
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# num_constraints = 6
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# filename = "ipopt_"*string(rand(UInt))
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# ipopt_options = Dict("constr_viol_tol" => tol,
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# "acceptable_constr_viol_tol" => 1e-4,
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# "max_iter" => 10_000,
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# "max_cpu_time" => 30.,
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# "print_level" => 0,
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# "output_file" => filename)
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# options = Options(solver=IPOPT(ipopt_options),
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# derivatives=ForwardAD())
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# x, _, info = minimize(f!, vec(x0), num_constraints, lower_x, upper_x, final[1:6], final[1:6], options)
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# rm(filename)
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# if info in [:Solve_Succeeded, :Solved_To_Acceptable_Level]
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# return Result(true, info, reshape(x,(n,3)))
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# else
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# if verbose println(info) end
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# return Result(false, info, x0)
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# end
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end
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@@ -1,60 +0,0 @@
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using SNOW
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export solve_phase
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struct Result
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converged::Bool
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info::Symbol
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sol::Matrix{Float64}
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end
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"""
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This function will take a single phase (so an initial state, and a final state) and an initial guess
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to the thrust profile and use an NLP solver to find the nearest thrust profile to that initial guess
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that satisfies the final state condition
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"""
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function solve_phase( start::Vector{Float64},
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final::Vector{Float64},
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craft::Sc,
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tof::Float64,
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x0::Matrix{Float64},
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primary::Body=Sun;
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tol=1e-8,
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verbose::Bool=false )
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n = size(x0)[1]
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function f!(g,x)
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try
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g[1:6] .= prop(reshape(x,(n,3)), start, craft, tof, primary)[2][1:6]
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return 1.
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catch e
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if isa(e,LaGuerreConway_Error) || isa(e, Mass_Error) || isa(e, PropOne_Error)
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return 10_000.
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else
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rethrow
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end
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end
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end
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lower_x = -1 * ones(3n)
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upper_x = ones(3n)
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num_constraints = 6
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filename = "ipopt_"*string(rand(UInt))
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ipopt_options = Dict("constr_viol_tol" => tol,
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"acceptable_constr_viol_tol" => 1e-4,
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"max_iter" => 10_000,
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"max_cpu_time" => 30.,
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"print_level" => 0,
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"output_file" => filename)
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options = Options(solver=IPOPT(ipopt_options),
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derivatives=ForwardAD())
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x, _, info = minimize(f!, vec(x0), num_constraints, lower_x, upper_x, final[1:6], final[1:6], options)
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rm(filename)
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if info in [:Solve_Succeeded, :Solved_To_Acceptable_Level]
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return Result(true, info, reshape(x,(n,3)))
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else
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if verbose println(info) end
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return Result(false, info, x0)
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end
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end
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