gcop::KalmanPredictor< T, _nx, _nu, _np > Class Template Reference

#include <kalmanpredictor.h>

Inheritance diagram for gcop::KalmanPredictor< T, _nx, _nu, _np >:

Collaboration diagram for gcop::KalmanPredictor< T, _nx, _nu, _np >:

List of all members.

Public Types
typedef Matrix< double, _nx, 1 >	Vectornd
typedef Matrix< double, _nu, 1 >	Vectorcd
typedef Matrix< double, _np, 1 >	Vectormd
typedef Matrix< double, _nx, _nx >	Matrixnd
typedef Matrix< double, _nx, _nu >	Matrixncd
typedef Matrix< double, _nu, _nx >	Matrixcnd
typedef Matrix< double, _nu, _nu >	Matrixcd
typedef Matrix< double, _np, _np >	Matrixmd
typedef Matrix< double, _nx, _np >	Matrixnmd
typedef Matrix< double, _np, _nx >	Matrixmnd
Public Member Functions
	KalmanPredictor (System< T, _nx, _nu, _np > &sys)
virtual	~KalmanPredictor ()
virtual bool	Predict (T &xb, double t, const T &xa, const Vectorcd &u, double h, const Vectormd *p=0, bool cov=true)
Public Attributes
Matrixnd	A
	process Jacobian
Matrixnd	Q
	discrete-time process noise covariance

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>
class gcop::KalmanPredictor< T, _nx, _nu, _np >

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Member Typedef Documentation

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nu, _nu> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixcd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nu, _nx> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixcnd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _np, _np> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixmd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _np, _nx> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixmnd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nx, _nu> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixncd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nx, _nx> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixnd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nx, _np> gcop::KalmanPredictor< T, _nx, _nu, _np >::Matrixnmd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nu, 1> gcop::KalmanPredictor< T, _nx, _nu, _np >::Vectorcd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _np, 1> gcop::KalmanPredictor< T, _nx, _nu, _np >::Vectormd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

typedef Matrix<double, _nx, 1> gcop::KalmanPredictor< T, _nx, _nu, _np >::Vectornd

Reimplemented from gcop::Predictor< T, _nx, _nu, _np >.

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Constructor & Destructor Documentation

template<typename T , int _nx, int _nu, int _np>

gcop::KalmanPredictor< T, _nx, _nu, _np >::KalmanPredictor

(

System< T, _nx, _nu, _np > &

sys

)

References gcop::KalmanPredictor< T, _nx, _nu, _np >::A, gcop::KalmanPredictor< T, _nx, _nu, _np >::Q, and gcop::System< T, _nx, _nu, _np >::X.

template<typename T , int _nx, int _nu, int _np>

gcop::KalmanPredictor< T, _nx, _nu, _np >::~KalmanPredictor

(

)

[virtual]

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Member Function Documentation

template<typename T , int _nx, int _nu, int _np>

bool gcop::KalmanPredictor< T, _nx, _nu, _np >::Predict	(	T &	xb,
		double	t,
		const T &	xa,
		const Vectorcd &	u,
		double	h,
		const Vectormd *	p = 0,
		bool	cov = true
	)		[virtual]

Prediction step.

Parameters:

xb	new belief state
t	time
xa	previous belief state
u	control inputs
h	time-step
p	parameters (optional)
cov	whether to update the covariance as well (true by default)

Returns:

true if success

Implements gcop::Predictor< T, _nx, _nu, _np >.

---

Member Data Documentation

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

Matrixnd gcop::KalmanPredictor< T, _nx, _nu, _np >::A

process Jacobian

Referenced by gcop::KalmanPredictor< T, _nx, _nu, _np >::KalmanPredictor().

template<typename T = VectorXd, int _nx = Dynamic, int _nu = Dynamic, int _np = Dynamic>

Matrixnd gcop::KalmanPredictor< T, _nx, _nu, _np >::Q

discrete-time process noise covariance

Referenced by gcop::KalmanPredictor< T, _nx, _nu, _np >::KalmanPredictor().

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

• lib/est/kalmanpredictor.h