API · OptimalControl.jl

OptimalControl.OptimalControl — Module

OptimalControl module.

Lists all the imported modules and packages:

Base
CTBase
CTDirect
CTFlows
CTProblems
Core
DocStringExtensions

List of all the exported names:

OptimalControl.available_methods — Method

available_methods() -> Tuple{Tuple{Vararg{Symbol}}}

Return the list of available methods to solve the optimal control problem.

source

OptimalControl.solve — Method

solve(
    ocp::OptimalControlModel,
    description::Symbol...;
    display,
    init,
    kwargs...
) -> Union{Nothing, OptimalControlSolution}

Solve the the optimal control problem ocp by the method given by the (optional) description.

The (optional) description

You can pass a partial description. If you give a partial description, then, if several complete descriptions contains the partial one, then, the method with the highest priority is chosen. The higher in the list, the higher is the priority.

Keyword arguments:

display: print or not information during the resolution
init: an initial condition for the solver

Warning

There is only one available method for the moment: a direct method which transforms the optimal control problem into a nonlinear programming problem (NLP) solved by Ipopt, thanks to the package ADNLPModels. The direct method comes from the CTDirect package.

Tip

To see the list of available methods, simply call available_methods().
You can pass any other option by a pair keyword=value according to the chosen method.

Examples

julia> sol = solve(ocp)
julia> sol = solve(ocp, :direct)
julia> sol = solve(ocp, :direct, :ipopt)
julia> sol = solve(ocp, :direct, :ipopt, display=false)
julia> sol = solve(ocp, :direct, :ipopt, display=false, init=sol)
julia> sol = solve(ocp, init=(state=[-0.5, 0.2],))
julia> sol = solve(ocp, init=(state=[-0.5, 0.2], control=0.5))
julia> sol = solve(ocp, init=(state=[-0.5, 0.2], control=0.5, variable=[1, 2]))
julia> sol = solve(ocp, init=(state=[-0.5, 0.2], control=t->6-12*t))
julia> sol = solve(ocp, init=(state=t->[-1+t, t*(t-1)], control=0.5))
julia> sol = solve(ocp, init=(state=t->[-1+t, t*(t-1)], control=t->6-12*t))

source

OptimalControl API

Index

Available methods

Documentation