#include <sleipnir/control/ocp.hpp>

Inheritance diagram for slp::OCP:

Public Member Functions
	OCP (int num_states, int num_inputs, std::chrono::duration< double > dt, int num_steps, function_ref< VariableMatrix(const VariableMatrix &x, const VariableMatrix &u)> dynamics, DynamicsType dynamics_type=DynamicsType::EXPLICIT_ODE, TimestepMethod timestep_method=TimestepMethod::FIXED, TranscriptionMethod method=TranscriptionMethod::DIRECT_TRANSCRIPTION)

	OCP (int num_states, int num_inputs, std::chrono::duration< double > dt, int num_steps, function_ref< VariableMatrix(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> dynamics, DynamicsType dynamics_type=DynamicsType::EXPLICIT_ODE, TimestepMethod timestep_method=TimestepMethod::FIXED, TranscriptionMethod method=TranscriptionMethod::DIRECT_TRANSCRIPTION)

template<typename T > requires ScalarLike<T> \|\| MatrixLike<T>
void	constrain_initial_state (const T &initial_state)

template<typename T > requires ScalarLike<T> \|\| MatrixLike<T>
void	constrain_final_state (const T &final_state)

void	for_each_step (const function_ref< void(const VariableMatrix &x, const VariableMatrix &u)> callback)

void	ForEachStep (const function_ref< void(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> callback)

template<typename T > requires ScalarLike<T> \|\| MatrixLike<T>
void	set_lower_input_bound (const T &lower_bound)

template<typename T > requires ScalarLike<T> \|\| MatrixLike<T>
void	set_upper_input_bound (const T &upper_bound)

void	SetMinTimestep (std::chrono::duration< double > min_timestep)

void	SetMaxTimestep (std::chrono::duration< double > max_timestep)

VariableMatrix &	X ()

VariableMatrix &	U ()

VariableMatrix &	dt ()

VariableMatrix	initial_state ()

VariableMatrix	final_state ()

Public Member Functions inherited from slp::Problem
	Problem () noexcept=default

Variable	decision_variable ()

VariableMatrix	decision_variable (int rows, int cols=1)

VariableMatrix	symmetric_decision_variable (int rows)

void	minimize (const Variable &cost)

void	minimize (Variable &&cost)

void	maximize (const Variable &objective)

void	maximize (Variable &&objective)

void	subject_to (const EqualityConstraints &constraint)

void	subject_to (EqualityConstraints &&constraint)

void	subject_to (const InequalityConstraints &constraint)

void	subject_to (InequalityConstraints &&constraint)

ExpressionType	cost_function_type () const

ExpressionType	equality_constraint_type () const

ExpressionType	inequality_constraint_type () const

ExitStatus	solve (const Options &options=Options{})

template<typename F > requires requires(F callback, const IterationInfo& info) { { callback(info) } -> std::same_as<void>; }
void	add_callback (F &&callback)

template<typename F > requires requires(F callback, const IterationInfo& info) { { callback(info) } -> std::same_as<bool>; }
void	add_callback (F &&callback)

void	clear_callbacks ()

Detailed Description

This class allows the user to pose and solve a constrained optimal control problem (OCP) in a variety of ways.

The system is transcripted by one of three methods (direct transcription, direct collocation, or single-shooting) and additional constraints can be added.

In direct transcription, each state is a decision variable constrained to the integrated dynamics of the previous state. In direct collocation, the trajectory is modeled as a series of cubic polynomials where the centerpoint slope is constrained. In single-shooting, states depend explicitly as a function of all previous states and all previous inputs.

Explicit ODEs are integrated using RK4.

For explicit ODEs, the function must be in the form dx/dt = f(t, x, u). For discrete state transition functions, the function must be in the form xₖ₊₁ = f(t, xₖ, uₖ).

Direct collocation requires an explicit ODE. Direct transcription and single-shooting can use either an ODE or state transition function.

https://underactuated.mit.edu/trajopt.html goes into more detail on each transcription method.

Constructor & Destructor Documentation

◆ OCP() [1/2]

slp::OCP::OCP	(	int	num_states,
		int	num_inputs,
		std::chrono::duration< double >	dt,
		int	num_steps,
		function_ref< VariableMatrix(const VariableMatrix &x, const VariableMatrix &u)>	dynamics,
		DynamicsType	dynamics_type = `DynamicsType::EXPLICIT_ODE`,
		TimestepMethod	timestep_method = `TimestepMethod::FIXED`,
		TranscriptionMethod	method = `TranscriptionMethod::DIRECT_TRANSCRIPTION`
	)

inline

Build an optimization problem using a system evolution function (explicit ODE or discrete state transition function).

Parameters

num_states	The number of system states.
num_inputs	The number of system inputs.
dt	The timestep for fixed-step integration.
num_steps	The number of control points.
dynamics	Function representing an explicit or implicit ODE, or a discrete state transition function. Explicit: dx/dt = f(x, u, ) Implicit: f([x dx/dt]', u, ) = 0 State transition: xₖ₊₁ = f(xₖ, uₖ)
dynamics_type	The type of system evolution function.
timestep_method	The timestep method.
method	The transcription method.

◆ OCP() [2/2]

slp::OCP::OCP	(	int	num_states,
		int	num_inputs,
		std::chrono::duration< double >	dt,
		int	num_steps,
		function_ref< VariableMatrix(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)>	dynamics,
		DynamicsType	dynamics_type = `DynamicsType::EXPLICIT_ODE`,
		TimestepMethod	timestep_method = `TimestepMethod::FIXED`,
		TranscriptionMethod	method = `TranscriptionMethod::DIRECT_TRANSCRIPTION`
	)

inline

Build an optimization problem using a system evolution function (explicit ODE or discrete state transition function).

Parameters

num_states	The number of system states.
num_inputs	The number of system inputs.
dt	The timestep for fixed-step integration.
num_steps	The number of control points.
dynamics	Function representing an explicit or implicit ODE, or a discrete state transition function. Explicit: dx/dt = f(t, x, u, ) Implicit: f(t, [x dx/dt]', u, ) = 0 State transition: xₖ₊₁ = f(t, xₖ, uₖ, dt)
dynamics_type	The type of system evolution function.
timestep_method	The timestep method.
method	The transcription method.

Member Function Documentation

◆ constrain_final_state()

template<typename T >
requires ScalarLike<T> || MatrixLike<T>

void slp::OCP::constrain_final_state ( const T & final_state )

inline

Utility function to constrain the final state.

Parameters

final_state the final state to constrain to.

◆ constrain_initial_state()

template<typename T >
requires ScalarLike<T> || MatrixLike<T>

void slp::OCP::constrain_initial_state ( const T & initial_state )

inline

Utility function to constrain the initial state.

Parameters

initial_state the initial state to constrain to.

◆ dt()

VariableMatrix & slp::OCP::dt ( )

inline

Get the timestep variables. After the problem is solved, this will contain the timesteps corresponding to the optimized trajectory.

Shaped 1x(num_steps+1), although the last timestep is unused in the trajectory.

Returns: The timestep variable matrix.

◆ final_state()

VariableMatrix slp::OCP::final_state ( )

inline

Convenience function to get the final state in the trajectory.

Returns: The final state of the trajectory.

◆ for_each_step()

void slp::OCP::for_each_step ( const function_ref< void(const VariableMatrix &x, const VariableMatrix &u)> callback )

inline

Set the constraint evaluation function. This function is called num_steps+1 times, with the corresponding state and input VariableMatrices.

Parameters

callback The callback f(x, u) where x is the state and u is the input vector.

◆ ForEachStep()

void slp::OCP::ForEachStep ( const function_ref< void(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> callback )

inline

Set the constraint evaluation function. This function is called num_steps+1 times, with the corresponding state and input VariableMatrices.

Parameters

callback The callback f(t, x, u, dt) where t is time, x is the state vector, u is the input vector, and dt is the timestep duration.

◆ initial_state()

VariableMatrix slp::OCP::initial_state ( )

inline

Convenience function to get the initial state in the trajectory.

Returns: The initial state of the trajectory.

◆ set_lower_input_bound()

template<typename T >
requires ScalarLike<T> || MatrixLike<T>

void slp::OCP::set_lower_input_bound ( const T & lower_bound )

inline

Convenience function to set a lower bound on the input.

Parameters

lower_bound The lower bound that inputs must always be above. Must be shaped (num_inputs)x1.

◆ set_upper_input_bound()

template<typename T >
requires ScalarLike<T> || MatrixLike<T>

void slp::OCP::set_upper_input_bound ( const T & upper_bound )

inline

Convenience function to set an upper bound on the input.

Parameters

upper_bound The upper bound that inputs must always be below. Must be shaped (num_inputs)x1.

◆ SetMaxTimestep()

void slp::OCP::SetMaxTimestep ( std::chrono::duration< double > max_timestep )

inline

Convenience function to set an upper bound on the timestep.

Parameters

max_timestep The maximum timestep.

◆ SetMinTimestep()

void slp::OCP::SetMinTimestep ( std::chrono::duration< double > min_timestep )

inline

Convenience function to set a lower bound on the timestep.

Parameters

min_timestep The minimum timestep.

◆ U()

VariableMatrix & slp::OCP::U ( )

inline

Get the input variables. After the problem is solved, this will contain the inputs corresponding to the optimized trajectory.

Shaped (num_inputs)x(num_steps+1), although the last input step is unused in the trajectory.

Returns: The input variable matrix.

◆ X()

VariableMatrix & slp::OCP::X ( )

inline

Get the state variables. After the problem is solved, this will contain the optimized trajectory.

Shaped (num_states)x(num_steps+1).

Returns: The state variable matrix.

The documentation for this class was generated from the following file:

include/sleipnir/control/ocp.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ OCP() [1/2]

◆ OCP() [2/2]

Member Function Documentation

◆ constrain_final_state()

◆ constrain_initial_state()

◆ dt()

◆ final_state()

◆ for_each_step()

◆ ForEachStep()

◆ initial_state()

◆ set_lower_input_bound()

◆ set_upper_input_bound()

◆ SetMaxTimestep()

◆ SetMinTimestep()

◆ U()

◆ X()