Private API

abstract type AbstractConstraintsModel

Abstract base type for constraint models in optimal control problems.

Subtypes store all constraint information including path constraints, boundary constraints, and box constraints on state, control, and variables.

See also: ConstraintsModel.

abstract type AbstractControlModel

Abstract base type for control variable models in optimal control problems.

Subtypes describe the control space structure including dimension, naming, and optionally the control trajectory itself.

See also: ControlModel, ControlModelSolution.

abstract type AbstractObjectiveModel

Abstract base type for objective function models in optimal control problems.

Subtypes represent different forms of the cost functional: Mayer (terminal cost), Lagrange (integral cost), or Bolza (both).

See also: MayerObjectiveModel, LagrangeObjectiveModel, BolzaObjectiveModel.

abstract type AbstractStateModel

Abstract base type for state variable models in optimal control problems.

Subtypes describe the state space structure including dimension, naming, and optionally the state trajectory itself.

See also: StateModel, StateModelSolution.

abstract type AbstractTimesModel

Abstract base type for combined initial and final time models.

See also: TimesModel.

abstract type AbstractVariableModel

Abstract base type for optimisation variable models in optimal control problems.

Optimisation variables are decision variables that do not depend on time, such as free final time or unknown parameters.

See also: VariableModel, EmptyVariableModel, VariableModelSolution.

struct ControlModelSolution{TS<:Function} <: CTModels.OCP.AbstractControlModel

Represents the control trajectory in a solution.

Fields

• name::String: Name of the control variable (e.g., "u").
• components::Vector{String}: Names of individual control components (e.g., ["u₁", "u₂"]).
• value::TS: A function t -> u(t) returning the control vector at time t.
• interpolation::Symbol: Interpolation type (:constant for piecewise constant, :linear for piecewise linear).

Example

julia> using CTModels

julia> u_traj = t -> [sin(t)]
julia> cms = CTModels.ControlModelSolution("u", ["u₁"], u_traj, :constant)
julia> cms.value(π/2)
1-element Vector{Float64}:
 1.0

struct StateModelSolution{TS<:Function} <: CTModels.OCP.AbstractStateModel

State model for a solved optimal control problem, including the state trajectory.

Fields

• name::String: Display name for the state variable.
• components::Vector{String}: Names of individual state components.
• value::TS: A function t -> x(t) returning the state vector at time t.

Example

julia> using CTModels

julia> x_traj = t -> [cos(t), sin(t)]
julia> sms = CTModels.StateModelSolution("x", ["x₁", "x₂"], x_traj)
julia> sms.value(0.0)
2-element Vector{Float64}:
 1.0
 0.0

abstract type TimeDependence

Abstract base type representing time dependence of an optimal control problem.

Used as a type parameter to distinguish between autonomous and non-autonomous systems at the type level, enabling dispatch and compile-time optimisations.

See also: Autonomous, NonAutonomous.

struct VariableModelSolution{TS<:Union{Real, AbstractVector{<:Real}}} <: CTModels.OCP.AbstractVariableModel

Variable model for a solved optimal control problem, including the variable value.

Fields

• name::String: Display name for the variable.
• components::Vector{String}: Names of individual variable components.
• value::TS: The optimisation variable value (scalar or vector).

Example

julia> using CTModels

julia> vms = CTModels.VariableModelSolution("v", ["tf"], 2.5)
julia> vms.value
2.5

__is_autonomous_set(ocp::CTModels.OCP.PreModel) -> Bool

Return true if the autonomous flag has been set in the PreModel.

__is_consistent(ocp::CTModels.OCP.PreModel) -> Bool

Return true if all the required fields are set in the PreModel.

__is_control_empty(c) -> Bool

Return true if c is an EmptyControlModel.

__is_control_empty(ocp::CTModels.OCP.PreModel) -> Bool

Return true if the control field of the PreModel is an EmptyControlModel.

__is_definition_empty(d) -> Bool

Return true if d is an EmptyDefinition.

__is_definition_empty(ocp::CTModels.OCP.PreModel) -> Bool

Return true if the definition field of the PreModel is an EmptyDefinition.

__is_dynamics_complete(ocp::CTModels.OCP.PreModel) -> Bool

Return true if dynamics cover all state components in the PreModel.

For component-wise dynamics, checks that all state indices are covered.

__is_dynamics_set(ocp::CTModels.OCP.PreModel) -> Bool

Return true if dynamics have been set in the PreModel.

__is_empty(ocp::CTModels.OCP.PreModel) -> Bool

Return true if nothing has been set.

__is_objective_set(ocp::CTModels.OCP.PreModel) -> Bool

Return true if objective has been set in the PreModel.

__is_set(x) -> Bool

Return true if x is not nothing.

__is_state_set(ocp::CTModels.OCP.PreModel) -> Bool

Return true if state has been set in the PreModel.

__is_times_set(ocp::CTModels.OCP.PreModel) -> Bool

Return true if times have been set in the PreModel.

__is_variable_empty(v) -> Bool

Return true if v is an EmptyVariableModel.

__is_variable_empty(ocp::CTModels.OCP.PreModel) -> Bool

Return true if the variable field of the PreModel is an EmptyVariableModel.