Drake
Drake C++ Documentation

## Detailed Description

Implementations of estimators that operate as Systems in a block diagram.

Algorithms that synthesize controllers are located in State Estimation.

## Classes

class  LuenbergerObserver< T >
A simple state observer for a dynamical system of the form:

$\dot{x} = f(x,u)$

$y = g(x,u)$

the observer dynamics takes the form

$\dot{\hat{x}} = f(\hat{x},u) + L(y - g(\hat{x},u))$

where $$\hat{x}$$ is the estimated state of the original system. More...

## Functions

Eigen::MatrixXd SteadyStateKalmanFilter (const Eigen::Ref< const Eigen::MatrixXd > &A, const Eigen::Ref< const Eigen::MatrixXd > &C, const Eigen::Ref< const Eigen::MatrixXd > &W, const Eigen::Ref< const Eigen::MatrixXd > &V)
Computes the optimal observer gain, L, for the linear system defined by

$\dot{x} = Ax + Bu + w,$

$y = Cx + Du + v.$

The resulting observer is of the form

$\dot{\hat{x}} = A\hat{x} + Bu + L(y - C\hat{x} - Du).$

The process noise, w, and the measurement noise, v, are assumed to be iid mean-zero Gaussian. More...

std::unique_ptr< LuenbergerObserver< double > > SteadyStateKalmanFilter (std::unique_ptr< LinearSystem< double >> system, const Eigen::Ref< const Eigen::MatrixXd > &W, const Eigen::Ref< const Eigen::MatrixXd > &V)
Creates a Luenberger observer system using the optimal steady-state Kalman filter gain matrix, L, as described above. More...

std::unique_ptr< LuenbergerObserver< double > > SteadyStateKalmanFilter (std::unique_ptr< System< double >> system, std::unique_ptr< Context< double >> context, const Eigen::Ref< const Eigen::MatrixXd > &W, const Eigen::Ref< const Eigen::MatrixXd > &V)
Creates a Luenberger observer system using the steady-state Kalman filter observer gain. More...

Eigen::MatrixXd ObservabilityMatrix (const LinearSystem< double > &sys)
Returns the observability matrix: O = [ C; CA; ...; CA^{n-1} ]. More...

bool IsObservable (const LinearSystem< double > &sys, std::optional< double > threshold=std::nullopt)
Returns true iff the observability matrix is full column rank. More...

bool IsDetectable (const LinearSystem< double > &sys, std::optional< double > threshold=std::nullopt)
Returns true iff the system is detectable. More...

## ◆ IsDetectable()

 bool drake::systems::IsDetectable ( const LinearSystem< double > & sys, std::optional< double > threshold = std::nullopt )

Returns true iff the system is detectable.

## ◆ IsObservable()

 bool drake::systems::IsObservable ( const LinearSystem< double > & sys, std::optional< double > threshold = std::nullopt )

Returns true iff the observability matrix is full column rank.

## ◆ ObservabilityMatrix()

 Eigen::MatrixXd drake::systems::ObservabilityMatrix ( const LinearSystem< double > & sys )

Returns the observability matrix: O = [ C; CA; ...; CA^{n-1} ].

 Eigen::MatrixXd drake::systems::estimators::SteadyStateKalmanFilter ( const Eigen::Ref< const Eigen::MatrixXd > & A, const Eigen::Ref< const Eigen::MatrixXd > & C, const Eigen::Ref< const Eigen::MatrixXd > & W, const Eigen::Ref< const Eigen::MatrixXd > & V )

Computes the optimal observer gain, L, for the linear system defined by

$\dot{x} = Ax + Bu + w,$

$y = Cx + Du + v.$

The resulting observer is of the form

$\dot{\hat{x}} = A\hat{x} + Bu + L(y - C\hat{x} - Du).$

The process noise, w, and the measurement noise, v, are assumed to be iid mean-zero Gaussian.

This is a simplified form of the full Kalman filter obtained by assuming that the state-covariance matrix has already converged to its steady-state solution.

Parameters
 A The state-space dynamics matrix of size num_states x num_states. C The state-space output matrix of size num_outputs x num_states. W The process noise covariance matrix, E[ww'], of size num_states x num_states. V The measurement noise covariance matrix, E[vv'], of size num_.
Returns
The steady-state observer gain matrix of size num_states x num_outputs.
Exceptions
 std::exception if V is not positive definite.

 std::unique_ptr > drake::systems::estimators::SteadyStateKalmanFilter ( std::unique_ptr< LinearSystem< double >> system, const Eigen::Ref< const Eigen::MatrixXd > & W, const Eigen::Ref< const Eigen::MatrixXd > & V )

Creates a Luenberger observer system using the optimal steady-state Kalman filter gain matrix, L, as described above.

Parameters
 system A unique_ptr to a LinearSystem describing the system to be observed. The new observer will take and maintain ownership of this pointer. W The process noise covariance matrix, E[ww'], of size num_states x num_states. V The measurement noise covariance matrix, E[vv'], of size num_.
Returns
A unique_ptr to the constructed observer system.
Exceptions
 std::exception if V is not positive definite.

Assuming system has the (continuous-time) dynamics: dx/dt = f(x,u), and the output: y = g(x,u), then the resulting observer will have the form dx̂/dt = f(x̂,u) + L(y - g(x̂,u)), where x̂ is the estimated state and the gain matrix, L, is designed as a steady-state Kalman filter using a linearization of f(x,u) at context as described above.