OptimalControl.jl

The OptimalControl.jl package is part of the control-toolbox ecosystem.

flowchart TD O(<a href='https://control-toolbox.org/OptimalControl.jl/stable/'>OptimalControl</a>) --> B(<a href='https://control-toolbox.org/OptimalControl.jl/stable/api-ctbase.html'>CTBase</a>) O --> D(<a href='https://control-toolbox.org/OptimalControl.jl/stable/api-ctdirect.html'>CTDirect</a>) O --> F(<a href='https://control-toolbox.org/OptimalControl.jl/stable/api-ctflows.html'>CTFlows</a>) F --> B D --> B style O fill:#FBF275

Index

For the developers, here are the private methods.

Available methods

using OptimalControl
available_methods()

(:direct, :adnlp, :ipopt)
(:direct, :adnlp, :madnlp)

Documentation

OptimalControl.OptimalControl — Module

OptimalControl module.

Lists all the imported modules and packages:

Base
CTBase
CTDirect
CTFlows
Core
DocStringExtensions

List of all the exported names:

source

CommonSolve.solve — Method

solve(
    ocp::OptimalControlModel,
    description::Symbol...;
    kwargs...
) -> Bool

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. To get the list of available methods, call available_methods().

Keyword arguments: 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.available_methods — Method

available_methods(

) -> Tuple{Vararg{Tuple{Symbol, Symbol, Symbol}}}

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

source