Drake

A firstorder, fully implicit integrator with second order error estimation. More...
#include <drake/systems/analysis/implicit_euler_integrator.h>
Public Types  
enum  JacobianComputationScheme { kForwardDifference, kCentralDifference, kAutomatic } 
Selecting the wrong such Jacobian determination scheme will slow (possibly critically) the implicit integration process. More...  
Public Types inherited from IntegratorBase< T >  
enum  StepResult { kReachedPublishTime = 1, kReachedZeroCrossing = 2, kReachedUpdateTime = 3, kTimeHasAdvanced = 4, kReachedBoundaryTime = 5, kReachedStepLimit = 6 } 
Status returned by StepOnceAtMost(). More...  
Public Member Functions  
~ImplicitEulerIntegrator () override=default  
ImplicitEulerIntegrator (const System< T > &system, Context< T > *context=nullptr)  
bool  supports_error_estimation () const override 
The integrator supports error estimation. More...  
int  get_error_estimate_order () const override 
This integrator provides second order error estimates. More...  
Does not allow copy, move, or assignment  
ImplicitEulerIntegrator (const ImplicitEulerIntegrator &)=delete  
ImplicitEulerIntegrator &  operator= (const ImplicitEulerIntegrator &)=delete 
ImplicitEulerIntegrator (ImplicitEulerIntegrator &&)=delete  
ImplicitEulerIntegrator &  operator= (ImplicitEulerIntegrator &&)=delete 
Methods for getting and setting the Jacobian scheme.  
Methods for getting and setting the scheme used to determine the Jacobian matrix necessary for solving the requisite nonlinear system if equations.
 
void  set_reuse (bool reuse) 
Sets whether the integrator attempts to reuse Jacobian matrices and iteration matrix factorizations (default is true ). More...  
bool  get_reuse () const 
Gets whether the integrator attempts to reuse Jacobian matrices and iteration matrix factorizations. More...  
void  set_jacobian_computation_scheme (JacobianComputationScheme scheme) 
Sets the Jacobian computation scheme. More...  
JacobianComputationScheme  get_jacobian_computation_scheme () const 
Cumulative statistics functions.  
The functions return statistics specific to the implicit integration process.  
int64_t  get_num_error_estimator_derivative_evaluations () const 
Gets the number of ODE function evaluations (calls to CalcTimeDerivatives()) used only for the error estimation process since the last call to ResetStatistics(). More...  
int64_t  get_num_derivative_evaluations_for_jacobian () const 
Gets the number of ODE function evaluations (calls to CalcTimeDerivatives()) used only for computing the Jacobian matrices since the last call to ResetStatistics(). More...  
int64_t  get_num_newton_raphson_iterations () const 
Gets the number of iterations used in the NewtonRaphson nonlinear systems of equation solving process since the last call to ResetStatistics(). More...  
int64_t  get_num_jacobian_evaluations () const 
Gets the number of Jacobian evaluations (i.e., the number of times that the Jacobian matrix was reformed) since the last call to ResetStatistics(). More...  
int64_t  get_num_iteration_matrix_factorizations () const 
Gets the number of factorizations of the iteration matrix since the last call to ResetStatistics(). More...  
Errorestimation statistics functions.  
The functions return statistics specific to the error estimation process. Gets the number of ODE function evaluations (calls to CalcTimeDerivatives()) used only for computing the Jacobian matrices needed by the error estimation process since the last call to ResetStatistics().  
int64_t  get_num_error_estimator_derivative_evaluations_for_jacobian () const 
int64_t  get_num_error_estimator_newton_raphson_iterations () const 
Gets the number of iterations used in the NewtonRaphson nonlinear systems of equation solving process for the error estimation process since the last call to ResetStatistics(). More...  
int64_t  get_num_error_estimator_jacobian_evaluations () const 
Gets the number of Jacobian matrix evaluations used only during the error estimation process since the last call to ResetStatistics(). More...  
int64_t  get_num_error_estimator_iteration_matrix_factorizations () const 
Gets the number of factorizations of the iteration matrix used only during the error estimation process since the last call to ResetStatistics(). More...  
Public Member Functions inherited from IntegratorBase< T >  
IntegratorBase (const System< T > &system, Context< T > *context=nullptr)  
Maintains references to the system being integrated and the context used to specify the initial conditions for that system (if any). More...  
virtual  ~IntegratorBase ()=default 
Destructor. More...  
void  set_fixed_step_mode (bool flag) 
Sets an integrator with error control to fixed step mode. More...  
bool  get_fixed_step_mode () const 
Gets whether an integrator is running in fixed step mode. More...  
void  set_target_accuracy (double accuracy) 
Request that the integrator attempt to achieve a particular accuracy for the continuous portions of the simulation. More...  
double  get_target_accuracy () const 
Gets the target accuracy. More...  
double  get_accuracy_in_use () const 
Gets the accuracy in use by the integrator. More...  
void  set_maximum_step_size (const T &max_step_size) 
Sets the maximum step size that may be taken by this integrator. More...  
const T &  get_maximum_step_size () const 
Gets the maximum step size that may be taken by this integrator. More...  
void  Reset () 
Resets the integrator to initial values, i.e., default construction values. More...  
void  Initialize () 
An integrator must be initialized before being used. More...  
void  request_initial_step_size_target (const T &step_size) 
Request that the first attempted integration step have a particular size. More...  
const T &  get_initial_step_size_target () const 
Gets the target size of the first integration step. More...  
StepResult  IntegrateAtMost (const T &publish_dt, const T &update_dt, const T &boundary_dt) 
Integrates the system forward in time by a single step with step size subject to integration error tolerances (assuming that the integrator supports error estimation). More...  
double  get_stretch_factor () const 
Gets the stretch factor (> 1), which is multiplied by the maximum (typically userdesignated) integration step size to obtain the amount that the integrator is able to stretch the maximum time step toward hitting an upcoming publish or update event in IntegrateAtMost(). More...  
void  IntegrateWithMultipleSteps (const T &dt) 
Stepping function for integrators operating outside of Simulator that advances the continuous state exactly by dt . More...  
void  IntegrateWithSingleFixedStep (const T &dt) 
Stepping function for integrators operating outside of Simulator that advances the continuous state exactly by dt and using a single fixed step. More...  
const T &  get_ideal_next_step_size () const 
Return the step size the integrator would like to take next, based primarily on the integrator's accuracy prediction. More...  
const Context< T > &  get_context () const 
Returns a const reference to the internallymaintained Context holding the most recent state in the trajectory. More...  
Context< T > *  get_mutable_context () 
Returns a mutable pointer to the internallymaintained Context holding the most recent state in the trajectory. More...  
void  reset_context (Context< T > *context) 
Replace the pointer to the internallymaintained Context with a different one. More...  
const System< T > &  get_system () const 
Gets a constant reference to the system that is being integrated (and was provided to the constructor of the integrator). More...  
bool  is_initialized () const 
Indicates whether the integrator has been initialized. More...  
const T &  get_previous_integration_step_size () const 
Gets the size of the last (previous) integration step. More...  
const ContinuousState< T > *  get_error_estimate () const 
Gets the error estimate (used only for integrators that support error estimation). More...  
IntegratorBase (const IntegratorBase &)=delete  
IntegratorBase &  operator= (const IntegratorBase &)=delete 
IntegratorBase (IntegratorBase &&)=delete  
IntegratorBase &  operator= (IntegratorBase &&)=delete 
void  set_requested_minimum_step_size (const T &min_step_size) 
Sets the requested minimum step size h_min that may be taken by this integrator. More...  
const T &  get_requested_minimum_step_size () const 
Gets the requested minimum step size h_min for this integrator. More...  
void  set_throw_on_minimum_step_size_violation (bool throws) 
Sets whether the integrator should throw a std::runtime_error exception when the integrator's step size selection algorithm determines that it must take a step smaller than the minimum step size (for, e.g., purposes of error control). More...  
bool  get_throw_on_minimum_step_size_violation () const 
Reports the current setting of the throw_on_minimum_step_size_violation flag. More...  
T  get_working_minimum_step_size () const 
Gets the current value of the working minimum step size h_work(t) for this integrator, which may vary with the current time t as stored in the integrator's context. More...  
void  ResetStatistics () 
Forget accumulated statistics. More...  
int64_t  get_num_substep_failures () const 
Gets the number of failed substeps (implying one or more step size reductions was required to permit solving the necessary nonlinear system of equations). More...  
int64_t  get_num_step_shrinkages_from_substep_failures () const 
Gets the number of step size shrinkages due to substep failures (e.g., integrator convergence failures) since the last call to ResetStatistics() or Initialize(). More...  
int64_t  get_num_step_shrinkages_from_error_control () const 
Gets the number of step size shrinkages due to failure to meet targeted error tolerances, since the last call to ResetStatistics or Initialize(). More...  
int64_t  get_num_derivative_evaluations () const 
Returns the number of ODE function evaluations (calls to CalcTimeDerivatives()) since the last call to ResetStatistics() or Initialize(). More...  
const T &  get_actual_initial_step_size_taken () const 
The actual size of the successful first step. More...  
const T &  get_smallest_adapted_step_size_taken () const 
The size of the smallest step taken as the result of a controlled integration step adjustment since the last Initialize() or ResetStatistics() call. More...  
const T &  get_largest_step_size_taken () const 
The size of the largest step taken since the last Initialize() or ResetStatistics() call. More...  
int64_t  get_num_steps_taken () const 
The number of integration steps taken since the last Initialize() or ResetStatistics() call. More...  
void  StartDenseIntegration () 
Starts dense integration, allocating a new dense output for this integrator to use. More...  
const DenseOutput< T > *  get_dense_output () const 
Returns a const pointer to the integrator's current DenseOutput instance, holding a representation of the continuous state trajectory since the last StartDenseIntegration() call. More...  
std::unique_ptr< DenseOutput< T > >  StopDenseIntegration () 
Stops dense integration, yielding ownership of the current dense output to the caller. More...  
const Eigen::VectorXd &  get_generalized_state_weight_vector () const 
Gets the weighting vector (equivalent to a diagonal matrix) applied to weighting both generalized coordinate and velocity state variable errors, as described in the group documentation. More...  
Eigen::VectorBlock< Eigen::VectorXd >  get_mutable_generalized_state_weight_vector () 
Gets a mutable weighting vector (equivalent to a diagonal matrix) applied to weighting both generalized coordinate and velocity state variable errors, as described in the group documentation. More...  
const Eigen::VectorXd &  get_misc_state_weight_vector () const 
Gets the weighting vector (equivalent to a diagonal matrix) for weighting errors in miscellaneous continuous state variables z . More...  
Eigen::VectorBlock< Eigen::VectorXd >  get_mutable_misc_state_weight_vector () 
Gets a mutable weighting vector (equivalent to a diagonal matrix) for weighting errors in miscellaneous continuous state variables z . More...  
Additional Inherited Members  
Protected Member Functions inherited from IntegratorBase< T >  
void  CalcTimeDerivatives (const Context< T > &context, ContinuousState< T > *dxdt) 
Evaluates the derivative function (and updates call statistics). More...  
template<typename U >  
void  CalcTimeDerivatives (const System< U > &system, const Context< U > &context, ContinuousState< U > *dxdt) 
Evaluates the derivative function (and updates call statistics). More...  
void  set_accuracy_in_use (double accuracy) 
Sets the working ("in use") accuracy for this integrator. More...  
void  InitializeAccuracy (double default_accuracy, double loosest_accuracy, double max_step_fraction) 
Generic code for validating (and resetting, if need be) the integrator working accuracy for error controlled integrators. More...  
bool  StepOnceErrorControlledAtMost (const T &dt_max) 
Default code for advancing the continuous state of the system by a single step of dt_max (or smaller, depending on error control). More...  
T  CalcStateChangeNorm (const ContinuousState< T > &dx_state) const 
Computes the infinity norm of a change in continuous state. More...  
std::pair< bool, T >  CalcAdjustedStepSize (const T &err, const T &attempted_step_size, bool *at_minimum_step_size) const 
Calculates adjusted integrator step sizes toward keeping state variables within error bounds on the next integration step. More...  
virtual void  DoReset () 
Derived classes can override this method to perform routines when Reset() is called. More...  
virtual std::unique_ptr< StepwiseDenseOutput< T > >  DoStartDenseIntegration () 
Derived classes can override this method to provide a continuous extension of their own when StartDenseIntegration() is called. More...  
StepwiseDenseOutput< T > *  get_mutable_dense_output () 
Returns a mutable pointer to the internallymaintained StepwiseDenseOutput instance, holding a representation of the continuous state trajectory since the last time StartDenseIntegration() was called. More...  
virtual bool  DoDenseStep (const T &dt) 
Derived classes may implement this method to (1) integrate the continuous portion of this system forward by a single step of size dt , (2) set the error estimate (via get_mutable_error_estimate()) and (3) update their own dense output implementation (via get_mutable_dense_output()). More...  
ContinuousState< T > *  get_mutable_error_estimate () 
Gets an error estimate of the state variables recorded by the last call to StepOnceFixedSize(). More...  
void  set_actual_initial_step_size_taken (const T &dt) 
void  set_smallest_adapted_step_size_taken (const T &dt) 
Sets the size of the smalleststeptaken statistic as the result of a controlled integration step adjustment. More...  
void  set_largest_step_size_taken (const T &dt) 
void  set_ideal_next_step_size (const T &dt) 
A firstorder, fully implicit integrator with second order error estimation.
T  The vector element type, which must be a valid Eigen scalar. 
This class uses Drake's inl.h
pattern. When seeing linker errors from this class, please refer to http://drake.mit.edu/cxx_inl.html.
Instantiated templates for the following kinds of T's are provided:
This integrator uses the following update rule:
x(t+h) = x(t) + h f(t+h,x(t+h))
where x are the state variables, h is the integration step size, and f() returns the time derivatives of the state variables. Contrast this update rule to that of an explicit firstorder integrator:
x(t+h) = x(t) + h f(t, x(t))
Thus implicit firstorder integration must solve a nonlinear system of equations to determine both the state at t+h and the time derivatives of that state at that time. Cast as a nonlinear system of equations, we seek the solution to:
x(t+h)  x(t)  h f(t+h,x(t+h)) = 0
given unknowns x(t+h).
This "implicit Euler" method is known to be LStable, meaning both that applying it at a fixed integration step to the "test" equation y(t) = eᵏᵗ
yields zero (for k < 0
and t → ∞
) and that it is also AStable. AStability, in turn, means that the method can integrate the linear constant coefficient system dx/dt = Ax
at any step size without the solution becoming unstable (growing without bound). The practical effect of LStability is that the integrator tends to be stable for any given step size on an arbitrary system of ordinary differential equations. See [Lambert, 1991], Ch. 6 for an approachable discussion on stiff differential equations and L and AStability.
The time complexity of this method is often dominated by the time to form the Jacobian matrix consisting of the partial derivatives of the nonlinear system (of n
dimensions, where n
is the number of state variables) taken with respect to the partial derivatives of the state variables at x(t+h)
. For typical numerical differentiation, f
will be evaluated n
times during the Jacobian formation; if we liberally assume that the derivative function evaluation code runs in O(n)
time (e.g., as it would for multirigid body dynamics without kinematic loops), the asymptotic complexity to form the Jacobian will be O(n²)
. This Jacobian matrix needs to be formed repeatedly as often as every time the state variables are updated during the solution process. Using automatic differentiation replaces the n
derivative evaluations with what is hopefully a much less expensive process, though the complexity to form the Jacobian matrix is still O(n²)
. For large n
, the time complexity may be dominated by the O(n³)
time required to (repeatedly) solve linear systems problems as part of the nonlinear system solution process.
This implementation uses NewtonRaphson (NR) and relies upon the obvious convergence to a solution for g = 0
where g(x(t+h)) ≡ x(t+h)  x(t)  h f(t+h,x(t+h))
as h
becomes sufficiently small. It also uses the implicit trapezoid method fed the result from implicit Euler for (hopefully) faster convergence to compute the error estimate. General implementational details were gleaned from [Hairer, 1996].

strong 
Selecting the wrong such Jacobian determination scheme will slow (possibly critically) the implicit integration process.
Automatic differentiation is recommended if the System supports it for reasons of both higher accuracy and increased speed. Forward differencing (i.e., numerical differentiation) exhibits error in the approximation close to √ε, where ε is machine epsilon, from n forward dynamics calls (where n is the number of state variables). Central differencing yields the most accurate numerically differentiated Jacobian matrix, but expends double the computational effort for approximately three digits greater accuracy: the total error in the centraldifference approximation is close to ε^(2/3), from 2n forward dynamics calls. See [Nocedal 2004, pp. 167169].
Enumerator  

kForwardDifference 
O(h) Forward differencing. 
kCentralDifference 
O(h²) Central differencing. 
kAutomatic 
Automatic differentiation. 

delete 

delete 

overridedefault 

inlineexplicit 

inlineoverridevirtual 
This integrator provides second order error estimates.
Implements IntegratorBase< T >.

inline 

inline 
Gets the number of ODE function evaluations (calls to CalcTimeDerivatives()) used only for computing the Jacobian matrices since the last call to ResetStatistics().
This count includes those derivative calculations necessary for computing Jacobian matrices during the error estimation process.

inline 
Gets the number of ODE function evaluations (calls to CalcTimeDerivatives()) used only for the error estimation process since the last call to ResetStatistics().
This count includes all such calls including (1) those necessary to compute Jacobian matrices; and (2) calls that exhibit little cost (due to results being cached).

inline 

inline 
Gets the number of factorizations of the iteration matrix used only during the error estimation process since the last call to ResetStatistics().

inline 
Gets the number of Jacobian matrix evaluations used only during the error estimation process since the last call to ResetStatistics().

inline 
Gets the number of iterations used in the NewtonRaphson nonlinear systems of equation solving process for the error estimation process since the last call to ResetStatistics().

inline 
Gets the number of factorizations of the iteration matrix since the last call to ResetStatistics().
This count includes those refactorizations necessary during the error estimation process.

inline 
Gets the number of Jacobian evaluations (i.e., the number of times that the Jacobian matrix was reformed) since the last call to ResetStatistics().
This count includes those evaluations necessary during the error estimation process.

inline 
Gets the number of iterations used in the NewtonRaphson nonlinear systems of equation solving process since the last call to ResetStatistics().
This count includes those NewtonRaphson iterations used during the error estimation process.

inline 
Gets whether the integrator attempts to reuse Jacobian matrices and iteration matrix factorizations.

delete 

delete 

inline 
Sets the Jacobian computation scheme.
This function can be safely called at any time (i.e., the integrator need not be reinitialized afterward).

inline 
Sets whether the integrator attempts to reuse Jacobian matrices and iteration matrix factorizations (default is true
).
Forming Jacobian matrices and factorizing iteration matrices are generally the two most expensive operations performed by this integrator. For small systems (those with on the order of ten state variables), the additional accuracy that using fresh Jacobians and factorizations buys which can permit increased step sizes but should have no effect on solution accuracy can outweigh the small factorization cost.

inlineoverridevirtual 
The integrator supports error estimation.
Implements IntegratorBase< T >.