Space shuttle

The space shuttle reentry problem is a classical benchmark in aerospace optimal control, originating from reentry trajectory studies (see Betts 2010, Bulirsch 1971, Dickmanns 1972). It describes the atmospheric descent of the space shuttle from high altitude to the Terminal Area Energy Management (TAEM) interface. The aim is to maximise the crossrange, i.e. the final latitude at TAEM, subject to nonlinear dynamics, control bounds, and path constraints.

The problem can be written as

\[\max_{x,\,u} J(x,u) = \theta(t_f),\]

or equivalently

\[\min_{x,\,u} J(x,u) = -\theta(t_f),\]

subject to the dynamics

\[\begin{aligned} \dot{h}(t) &= v(t)\,\sin \gamma(t), \\ \dot{\phi}(t) &= \tfrac{v(t)}{r(t)} \cos \gamma(t) \sin \psi(t) / \cos \theta(t), \\ \dot{\theta}(t) &= \tfrac{v(t)}{r(t)} \cos \gamma(t) \cos \psi(t), \\ \dot{v}(t) &= -\tfrac{D}{m} - g(t)\,\sin \gamma(t), \\ \dot{\gamma}(t) &= \tfrac{L}{m v(t)} \cos \beta(t) + \cos \gamma(t)\Big(\tfrac{v(t)}{r(t)} - \tfrac{g(t)}{v(t)}\Big), \\ \dot{\psi}(t) &= \tfrac{L}{m v(t)\cos \gamma(t)} \sin \beta(t) + \tfrac{v(t)}{r(t)\cos \theta(t)} \cos \gamma(t)\sin \psi(t)\sin \theta(t), \end{aligned}\]

with aerodynamic lift and drag

\[D = \tfrac{1}{2} c_D S \rho v^2, \qquad L = \tfrac{1}{2} c_L S \rho v^2,\]

where the coefficients are given by

\[c_D = b_0 + b_1 \alpha^\circ + b_2 (\alpha^\circ)^2, \qquad c_L = a_0 + a_1 \alpha^\circ, \qquad \alpha^\circ = \tfrac{180}{\pi} \alpha,\]

and

\[\rho = \rho_0 e^{-h/h_r}, \qquad r = R_e + h, \qquad g = \tfrac{\mu}{r^2}.\]

Boundary conditions

At reentry interface ($t=0$):

\[h(0) = 260{,}000 \ \text{ft}, \quad v(0) = 25{,}600 \ \text{ft/s}, \quad \phi(0)=0, \quad \theta(0)=0, \quad \gamma(0)=-1^\circ, \quad \psi(0)=90^\circ.\]

At TAEM interface ($t=t_f$):

\[h(t_f) = 80{,}000 \ \text{ft}, \quad v(t_f) = 2{,}500 \ \text{ft/s}, \quad \gamma(t_f) = -5^\circ.\]

Final time is free within

\[500\Delta t_{\min} \le t_f \le 500\Delta t_{\max}, \qquad \Delta t_{\min}=3.5, \quad \Delta t_{\max}=4.5.\]

Constraints

State bounds:

\[h(t) \ge 0, \qquad -89^\circ \le \theta(t) \le 89^\circ, \qquad v(t) \ge 0, \qquad -89^\circ \le \gamma(t) \le 89^\circ.\]

Control bounds:

\[-90^\circ \le \alpha(t) \le 90^\circ, \qquad -89^\circ \le \beta(t) \le 1^\circ.\]

Qualitative behaviour

The optimal trajectory balances lift and drag to control heating and deceleration while extending crossrange.
The bank angle $\beta$ determines heading changes and crossrange capability.
The solution typically includes a combination of steep reentry to dissipate energy and crossrange manoeuvres to reach the target latitude.

Characteristics

Nonlinear, six–state dynamics with two controls.
Strongly nonlinear aerodynamic coefficients.
Free final time with bounded range.
Path constraints on both states and controls.
Widely used as a benchmark in optimal control and trajectory optimisation.

References

Betts, J.T. (2010). Practical Methods for Optimal Control and Estimation Using Nonlinear Programming. SIAM.
Bulirsch, R. (1971). Numerical solution of optimal control problems with state constraints by direct methods. Numerische Mathematik.
Dickmanns, E.D. (1972). Numerical solution methods for nonlinear optimal control problems with state constraints. Automatica.
Ascher, U.M., Mattheij, R.M.M., & Russell, R.D. (1988). Numerical Solution of Boundary Value Problems for Ordinary Differential Equations.

Packages

Import all necessary packages and define DataFrames to store information about the problem and resolution results.

using OptimalControlProblems    # to access the Beam model
using OptimalControl            # to import the OptimalControl model
using NLPModelsIpopt            # to solve the model with Ipopt
import DataFrames: DataFrame    # to store data
using NLPModels                 # to retrieve data from the NLP solution
using Plots                     # to plot the trajectories
using Plots.PlotMeasures        # for leftmargin, bottommargin
using JuMP                      # to import the JuMP model
using Ipopt                     # to solve the JuMP model with Ipopt

data_pb = DataFrame(            # to store data about the problem
    Problem=Symbol[],
    Grid_Size=Int[],
    Variables=Int[],
    Constraints=Int[],
)

data_re = DataFrame(            # to store data about the resolutions
    Model=Symbol[],
    Flag=Any[],
    Iterations=Int[],
    Objective=Float64[],
)

Initial guess

The initial guess (or first iterate) can be visualised by running the solver with max_iter=0. Here is the initial guess.

Click to unfold and see the code for plotting the initial guess.

function plot_initial_guess(problem)

    # dimensions
    x_vars = metadata[problem][:state_name]
    u_vars = metadata[problem][:control_name]
    n = length(x_vars) # number of states
    m = length(u_vars) # number of controls

    # import OptimalControl model
    docp = eval(problem)(OptimalControlBackend())
    nlp_oc = nlp_model(docp)

    # solve
    nlp_oc_sol = NLPModelsIpopt.ipopt(nlp_oc; max_iter=0)

    # build an optimal control solution
    ocp_sol = build_ocp_solution(docp, nlp_oc_sol)

    # plot the OptimalControl solution
    plt = plot(
        ocp_sol;
        state_style=(color=1,),
        costate_style=(color=1, legend=:none),
        control_style=(color=1, legend=:none),
        path_style=(color=1, legend=:none),
        dual_style=(color=1, legend=:none),
        size=(816, 220*(n+m)),
        label="OptimalControl",
        leftmargin=20mm,
    )
    for i in 2:n
        plot!(plt[i]; legend=:none)
    end

    # import JuMP model
    nlp_jp = eval(problem)(JuMPBackend())

    # solve
    set_optimizer(nlp_jp, Ipopt.Optimizer)
    set_optimizer_attribute(nlp_jp, "max_iter", 0)
    optimize!(nlp_jp)

    # plot
    t = time_grid(problem, nlp_jp)     # t0, ..., tN = tf
    x = state(problem, nlp_jp)         # function of time
    u = control(problem, nlp_jp)       # function of time
    p = costate(problem, nlp_jp)       # function of time

    for i in 1:n # state
        label = i == 1 ? "JuMP" : :none
        plot!(plt[i], t, t -> x(t)[i]; color=2, linestyle=:dash, label=label)
    end

    for i in 1:n # costate
        plot!(plt[n+i], t, t -> -p(t)[i]; color=2, linestyle=:dash, label=:none)
    end

    for i in 1:m # control
        plot!(plt[2n+i], t, t -> u(t)[i]; color=2, linestyle=:dash, label=:none)
    end

    return plt
end

plot_initial_guess(:space_shuttle)

Solve the problem

OptimalControl model

Import the OptimalControl model and solve it.

# import DOCP model
docp = space_shuttle(OptimalControlBackend())

# get NLP model
nlp_oc = nlp_model(docp)

# solve
nlp_oc_sol = NLPModelsIpopt.ipopt(
    nlp_oc;
    print_level=4,
    tol=1e-8,
    mu_strategy="adaptive",
    sb="yes",
)

Total number of variables............................:     4009
                     variables with only lower bounds:     1002
                variables with lower and upper bounds:     3007
                     variables with only upper bounds:        0
Total number of equality constraints.................:     3009
Total number of inequality constraints...............:        0
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:        0


Number of Iterations....: 111

                                   (scaled)                 (unscaled)
Objective...............:  -5.9587501336062354e-01   -5.9587501336062354e-01
Dual infeasibility......:   5.0870214995865162e-12    5.0870214995865162e-12
Constraint violation....:   1.0156368801528259e-10    1.0156368801528259e-10
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   1.1846058016074259e-11    1.1846058016074259e-11
Overall NLP error.......:   1.0156368801528259e-10    1.0156368801528259e-10


Number of objective function evaluations             = 112
Number of objective gradient evaluations             = 112
Number of equality constraint evaluations            = 112
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 112
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 111
Total seconds in IPOPT                               = 4.157

EXIT: Optimal Solution Found.

The problem has the following numbers of steps, variables and constraints.

push!(data_pb,
    (
        Problem=:space_shuttle,
        Grid_Size=metadata[:space_shuttle][:N],
        Variables=get_nvar(nlp_oc),
        Constraints=get_ncon(nlp_oc),
    )
)

1×4 DataFrame

Row	Problem	Grid_Size	Variables	Constraints
	Symbol	Int64	Int64	Int64
1	space_shuttle	500	4009	3009

JuMP model

Import the JuMP model and solve it.

# import model
nlp_jp = space_shuttle(JuMPBackend())

# solve
set_optimizer(nlp_jp, Ipopt.Optimizer)
set_optimizer_attribute(nlp_jp, "print_level", 4)
set_optimizer_attribute(nlp_jp, "tol", 1e-8)
set_optimizer_attribute(nlp_jp, "mu_strategy", "adaptive")
set_optimizer_attribute(nlp_jp, "linear_solver", "mumps")
set_optimizer_attribute(nlp_jp, "sb", "yes")
optimize!(nlp_jp)

Total number of variables............................:     4009
                     variables with only lower bounds:     1002
                variables with lower and upper bounds:     3007
                     variables with only upper bounds:        0
Total number of equality constraints.................:     3009
Total number of inequality constraints...............:        0
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:        0


Number of Iterations....: 115

                                   (scaled)                 (unscaled)
Objective...............:  -5.9587501342513927e-01   -5.9587501342513927e-01
Dual infeasibility......:   3.4752779650690412e-10    3.4752779650690412e-10
Constraint violation....:   7.0471158353235808e-09    7.0471158353235808e-09
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   1.3610888211002065e-10    1.3610888211002065e-10
Overall NLP error.......:   7.0471158353235808e-09    7.0471158353235808e-09


Number of objective function evaluations             = 116
Number of objective gradient evaluations             = 116
Number of equality constraint evaluations            = 116
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 116
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 115
Total seconds in IPOPT                               = 7.387

EXIT: Optimal Solution Found.

Numerical comparisons

Let's get the flag, the number of iterations and the objective value from the resolutions.

# from OptimalControl model
push!(data_re,
    (
        Model=:OptimalControl,
        Flag=nlp_oc_sol.status,
        Iterations=nlp_oc_sol.iter,
        Objective=nlp_oc_sol.objective,
    )
)

# from JuMP model
push!(data_re,
    (
        Model=:JuMP,
        Flag=termination_status(nlp_jp),
        Iterations=barrier_iterations(nlp_jp),
        Objective=objective_value(nlp_jp),
    )
)

2×4 DataFrame

Row	Model	Flag	Iterations	Objective
	Symbol	Any	Int64	Float64
1	OptimalControl	first_order	111	-0.595875
2	JuMP	LOCALLY_SOLVED	115	-0.595875

We compare the OptimalControl and JuMP solutions in terms of the number of iterations, the $L^2$-norm of the differences in the state, control, and variable, as well as the objective values. Both absolute and relative errors are reported.

Click to unfold and get the code of the numerical comparison.

function L2_norm(T, X)
    # T and X are supposed to be one dimensional
    s = 0.0
    for i in 1:(length(T) - 1)
        s += 0.5 * (X[i]^2 + X[i + 1]^2) * (T[i + 1]-T[i])
    end
    return √(s)
end

function numerical_comparison(problem, docp, nlp_oc_sol, nlp_jp)

    # get relevant data from OptimalControl model
    ocp_sol = build_ocp_solution(docp, nlp_oc_sol) # build an ocp solution
    t_oc = time_grid(ocp_sol)
    x_oc = state(ocp_sol).(t_oc)
    u_oc = control(ocp_sol).(t_oc)
    v_oc = variable(ocp_sol)
    o_oc = objective(ocp_sol)
    i_oc = iterations(ocp_sol)

    # get relevant data from JuMP model
    t_jp = time_grid(problem, nlp_jp)
    x_jp = state(problem, nlp_jp).(t_jp)
    u_jp = control(problem, nlp_jp).(t_jp)
    o_jp = objective(problem, nlp_jp)
    v_jp = variable(problem, nlp_jp)
    i_jp = iterations(problem, nlp_jp)

    x_vars = metadata[problem][:state_name]
    u_vars = metadata[problem][:control_name]
    v_vars = metadata[problem][:variable_name]

    println("┌─ ", string(problem))
    println("│")

    # number of iterations
    println("├─  Number of iterations")
    println("│")
    println("│     OptimalControl : ", i_oc)
    println("│     JuMP           : ", i_jp)
    println("│")

    # state
    for i in eachindex(x_vars)
        xi_oc = [x_oc[k][i] for k in eachindex(t_oc)]
        xi_jp = [x_jp[k][i] for k in eachindex(t_jp)]
        L2_oc = L2_norm(t_oc, xi_oc)
        L2_jp = L2_norm(t_oc, xi_jp)
        L2_ae = L2_norm(t_oc, xi_oc-xi_jp)
        L2_re = L2_ae/(0.5*(L2_oc + L2_jp))

        println("├─  State $(x_vars[i]) (L2 norm)")
        println("│")
        #println("│     OptimalControl : ", L2_oc)
        #println("│     JuMP           : ", L2_jp)
        println("│     Absolute error : ", L2_ae)
        println("│     Relative error : ", L2_re)
        println("│")
    end

    # control
    for i in eachindex(u_vars)
        ui_oc = [u_oc[k][i] for k in eachindex(t_oc)]
        ui_jp = [u_jp[k][i] for k in eachindex(t_jp)]
        L2_oc = L2_norm(t_oc, ui_oc)
        L2_jp = L2_norm(t_oc, ui_jp)
        L2_ae = L2_norm(t_oc, ui_oc-ui_jp)
        L2_re = L2_ae/(0.5*(L2_oc + L2_jp))

        println("├─  Control $(u_vars[i]) (L2 norm)")
        println("│")
        #println("│     OptimalControl : ", L2_oc)
        #println("│     JuMP           : ", L2_jp)
        println("│     Absolute error : ", L2_ae)
        println("│     Relative error : ", L2_re)
        println("│")
    end

    # variable
    if !isnothing(v_vars)
        for i in eachindex(v_vars)
            vi_oc = v_oc[i]
            vi_jp = v_jp[i]
            vi_ae = abs(vi_oc-vi_jp)
            vi_re = vi_ae/(0.5*(abs(vi_oc) + abs(vi_jp)))

            println("├─  Variable $(v_vars[i])")
            println("│")
            #println("│     OptimalControl : ", vi_oc)
            #println("│     JuMP           : ", vi_jp)
            println("│     Absolute error : ", vi_ae)
            println("│     Relative error : ", vi_re)
            println("│")
        end
    end

    # objective
    o_ae = abs(o_oc-o_jp)
    o_re = o_ae/(0.5*(abs(o_oc) + abs(o_jp)))

    println("├─  objective")
    println("│")
    #println("│     OptimalControl : ", o_oc)
    #println("│     JuMP           : ", o_jp)
    println("│     Absolute error : ", o_ae)
    println("│     Relative error : ", o_re)
    println("│")
    println("└─")

    return nothing
end

numerical_comparison(:space_shuttle, docp, nlp_oc_sol, nlp_jp)

┌─ space_shuttle
│
├─  Number of iterations
│
│     OptimalControl : 111
│     JuMP           : 115
│
├─  State scaled_h (L2 norm)
│
│     Absolute error : 2.990757986902121e-8
│     Relative error : 3.777491252937329e-10
│
├─  State ϕ (L2 norm)
│
│     Absolute error : 1.296165822056119e-8
│     Relative error : 2.981281800173441e-10
│
├─  State θ (L2 norm)
│
│     Absolute error : 4.390275094587482e-9
│     Relative error : 2.9349777010298746e-10
│
├─  State scaled_v (L2 norm)
│
│     Absolute error : 2.00130695151268e-8
│     Relative error : 2.573845022624617e-10
│
├─  State γ (L2 norm)
│
│     Absolute error : 3.938722711290643e-8
│     Relative error : 4.312726121907823e-8
│
├─  State ψ (L2 norm)
│
│     Absolute error : 9.428800855564053e-7
│     Relative error : 2.030973673684248e-8
│
├─  Control α (L2 norm)
│
│     Absolute error : 7.33882393603085e-8
│     Relative error : 5.393983800953857e-9
│
├─  Control β (L2 norm)
│
│     Absolute error : 3.437342039243316e-5
│     Relative error : 9.560666076894881e-7
│
├─  Variable tf
│
│     Absolute error : 7.695930435147602e-7
│     Relative error : 3.831507711074259e-10
│
├─  objective
│
│     Absolute error : 6.451572609478262e-11
│     Relative error : 1.0827056789549444e-10
│
└─

Plot the solutions

Visualise states, costates, and controls from the OptimalControl and JuMP solutions:

# build an ocp solution to use the plot from OptimalControl package
ocp_sol = build_ocp_solution(docp, nlp_oc_sol)

# dimensions
n = state_dimension(ocp_sol)   # or length(metadata[:space_shuttle][:state_name])
m = control_dimension(ocp_sol) # or length(metadata[:space_shuttle][:control_name])

# from OptimalControl solution
plt = plot(
    ocp_sol;
    state_style=(color=1,),
    costate_style=(color=1, legend=:none),
    control_style=(color=1, legend=:none),
    path_style=(color=1, legend=:none),
    dual_style=(color=1, legend=:none),
    size=(816, 240*(n+m)),
    label="OptimalControl",
    leftmargin=20mm,
)
for i in 2:n
    plot!(plt[i]; legend=:none)
end

# from JuMP solution
t = time_grid(:space_shuttle, nlp_jp)     # t0, ..., tN = tf
x = state(:space_shuttle, nlp_jp)         # function of time
u = control(:space_shuttle, nlp_jp)       # function of time
p = costate(:space_shuttle, nlp_jp)       # function of time

for i in 1:n # state
    label = i == 1 ? "JuMP" : :none
    plot!(plt[i], t, t -> x(t)[i]; color=2, linestyle=:dash, label=label)
end

for i in 1:n # costate
    plot!(plt[n+i], t, t -> -p(t)[i]; color=2, linestyle=:dash, label=:none)
end

for i in 1:m # control
    plot!(plt[2n+i], t, t -> u(t)[i]; color=2, linestyle=:dash, label=:none)
end