sleipnir::OCPSolver Class Reference

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

#include </home/runner/work/allwpilib/allwpilib/wpimath/src/main/native/thirdparty/sleipnir/include/sleipnir/control/OCPSolver.hpp>

Inheritance diagram for sleipnir::OCPSolver:

Public Member Functions
	OCPSolver (int numStates, int numInputs, std::chrono::duration< double > dt, int numSteps, function_ref< VariableMatrix(const VariableMatrix &x, const VariableMatrix &u)> dynamics, DynamicsType dynamicsType=DynamicsType::kExplicitODE, TimestepMethod timestepMethod=TimestepMethod::kFixed, TranscriptionMethod method=TranscriptionMethod::kDirectTranscription)
	Build an optimization problem using a system evolution function (explicit ODE or discrete state transition function).
	OCPSolver (int numStates, int numInputs, std::chrono::duration< double > dt, int numSteps, function_ref< VariableMatrix(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> dynamics, DynamicsType dynamicsType=DynamicsType::kExplicitODE, TimestepMethod timestepMethod=TimestepMethod::kFixed, TranscriptionMethod method=TranscriptionMethod::kDirectTranscription)
	Build an optimization problem using a system evolution function (explicit ODE or discrete state transition function).
template<typename T > requires ScalarLike<T> || MatrixLike<T>
void	ConstrainInitialState (const T &initialState)
	Utility function to constrain the initial state.
template<typename T > requires ScalarLike<T> || MatrixLike<T>
void	ConstrainFinalState (const T &finalState)
	Utility function to constrain the final state.
void	ForEachStep (const function_ref< void(const VariableMatrix &x, const VariableMatrix &u)> callback)
	Set the constraint evaluation function.
void	ForEachStep (const function_ref< void(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> callback)
	Set the constraint evaluation function.
template<typename T > requires ScalarLike<T> || MatrixLike<T>
void	SetLowerInputBound (const T &lowerBound)
	Convenience function to set a lower bound on the input.
template<typename T > requires ScalarLike<T> || MatrixLike<T>
void	SetUpperInputBound (const T &upperBound)
	Convenience function to set an upper bound on the input.
void	SetMinTimestep (std::chrono::duration< double > minTimestep)
	Convenience function to set a lower bound on the timestep.
void	SetMaxTimestep (std::chrono::duration< double > maxTimestep)
	Convenience function to set an upper bound on the timestep.
VariableMatrix &	X ()
	Get the state variables.
VariableMatrix &	U ()
	Get the input variables.
VariableMatrix &	DT ()
	Get the timestep variables.
VariableMatrix	InitialState ()
	Convenience function to get the initial state in the trajectory.
VariableMatrix	FinalState ()
	Convenience function to get the final state in the trajectory.
Public Member Functions inherited from sleipnir::OptimizationProblem
	OptimizationProblem () noexcept=default
	Construct the optimization problem.
Variable	DecisionVariable ()
	Create a decision variable in the optimization problem.
VariableMatrix	DecisionVariable (int rows, int cols=1)
	Create a matrix of decision variables in the optimization problem.
VariableMatrix	SymmetricDecisionVariable (int rows)
	Create a symmetric matrix of decision variables in the optimization problem.
void	Minimize (const Variable &cost)
	Tells the solver to minimize the output of the given cost function.
void	Minimize (Variable &&cost)
	Tells the solver to minimize the output of the given cost function.
void	Maximize (const Variable &objective)
	Tells the solver to maximize the output of the given objective function.
void	Maximize (Variable &&objective)
	Tells the solver to maximize the output of the given objective function.
void	SubjectTo (const EqualityConstraints &constraint)
	Tells the solver to solve the problem while satisfying the given equality constraint.
void	SubjectTo (EqualityConstraints &&constraint)
	Tells the solver to solve the problem while satisfying the given equality constraint.
void	SubjectTo (const InequalityConstraints &constraint)
	Tells the solver to solve the problem while satisfying the given inequality constraint.
void	SubjectTo (InequalityConstraints &&constraint)
	Tells the solver to solve the problem while satisfying the given inequality constraint.
SolverStatus	Solve (const SolverConfig &config=SolverConfig{})
	Solve the optimization problem.
template<typename F > requires requires(F callback, const SolverIterationInfo& info) { { callback(info) } -> std::same_as<void>; }
void	Callback (F &&callback)
	Sets a callback to be called at each solver iteration.
template<typename F > requires requires(F callback, const SolverIterationInfo& info) { { callback(info) } -> std::same_as<bool>; }
void	Callback (F &&callback)
	Sets a callback to be called at each solver iteration.

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

OCPSolver() [1/2]

sleipnir::OCPSolver::OCPSolver ( int numStates, int numInputs, std::chrono::duration< double > dt, int numSteps, function_ref< VariableMatrix(const VariableMatrix &x, const VariableMatrix &u)> dynamics, DynamicsType dynamicsType = DynamicsType::kExplicitODE, TimestepMethod timestepMethod = TimestepMethod::kFixed, TranscriptionMethod method = TranscriptionMethod::kDirectTranscription )

inline

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

Parameters

numStates	The number of system states.
numInputs	The number of system inputs.
dt	The timestep for fixed-step integration.
numSteps	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)
dynamicsType	The type of system evolution function.
timestepMethod	The timestep method.
method	The transcription method.

OCPSolver() [2/2]

sleipnir::OCPSolver::OCPSolver ( int numStates, int numInputs, std::chrono::duration< double > dt, int numSteps, function_ref< VariableMatrix(const Variable &t, const VariableMatrix &x, const VariableMatrix &u, const Variable &dt)> dynamics, DynamicsType dynamicsType = DynamicsType::kExplicitODE, TimestepMethod timestepMethod = TimestepMethod::kFixed, TranscriptionMethod method = TranscriptionMethod::kDirectTranscription )

inline

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

Parameters

numStates	The number of system states.
numInputs	The number of system inputs.
dt	The timestep for fixed-step integration.
numSteps	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)
dynamicsType	The type of system evolution function.
timestepMethod	The timestep method.
method	The transcription method.

Member Function Documentation

ConstrainFinalState()

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

void sleipnir::OCPSolver::ConstrainFinalState ( const T & finalState )

inline

Utility function to constrain the final state.

Parameters

finalState

the final state to constrain to.

ConstrainInitialState()

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

void sleipnir::OCPSolver::ConstrainInitialState ( const T & initialState )

inline

Utility function to constrain the initial state.

Parameters

initialState

the initial state to constrain to.

DT()

VariableMatrix & sleipnir::OCPSolver::DT ( )

inline

Get the timestep variables.

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

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

Returns:

The timestep variable matrix.

FinalState()

VariableMatrix sleipnir::OCPSolver::FinalState ( )

inline

Convenience function to get the final state in the trajectory.

Returns:

The final state of the trajectory.

ForEachStep() [1/2]

void sleipnir::OCPSolver::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 numSteps+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.

ForEachStep() [2/2]

void sleipnir::OCPSolver::ForEachStep ( const function_ref< void(const VariableMatrix &x, const VariableMatrix &u)> callback )

inline

Set the constraint evaluation function.

This function is called numSteps+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.

InitialState()

VariableMatrix sleipnir::OCPSolver::InitialState ( )

inline

Convenience function to get the initial state in the trajectory.

Returns:

The initial state of the trajectory.

SetLowerInputBound()

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

void sleipnir::OCPSolver::SetLowerInputBound ( const T & lowerBound )

inline

Convenience function to set a lower bound on the input.

Parameters

lowerBound

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

SetMaxTimestep()

void sleipnir::OCPSolver::SetMaxTimestep ( std::chrono::duration< double > maxTimestep )

inline

Convenience function to set an upper bound on the timestep.

Parameters

maxTimestep

The maximum timestep.

SetMinTimestep()

void sleipnir::OCPSolver::SetMinTimestep ( std::chrono::duration< double > minTimestep )

inline

Convenience function to set a lower bound on the timestep.

Parameters

minTimestep

The minimum timestep.

SetUpperInputBound()

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

void sleipnir::OCPSolver::SetUpperInputBound ( const T & upperBound )

inline

Convenience function to set an upper bound on the input.

Parameters

upperBound

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

U()

VariableMatrix & sleipnir::OCPSolver::U ( )

inline

Get the input variables.

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

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

Returns:

The input variable matrix.

X()

VariableMatrix & sleipnir::OCPSolver::X ( )

inline

Get the state variables.

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

Shaped (numStates)x(numSteps+1).

Returns:

The state variable matrix.

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

• /home/runner/work/allwpilib/allwpilib/wpimath/src/main/native/thirdparty/sleipnir/include/sleipnir/control/OCPSolver.hpp