lds::System #

Linear Dynamical System Type.
#include <lds_sys.h>

Inherited by lds::gaussian::System, lds::poisson::System

Public Functions #

	Name
	System() =default Constructs a new System.
	System(size_t n_u, size_t n_x, size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) constructs a new System
virtual	~System()
void	Filter(const Vector & u_tm1, const Vector & z) Filter data to produce causal state estimates.
virtual const Vector &	Simulate(const Vector & u_tm1) =0 simulates system (single time step)
void	f(const Vector & u, bool do_add_noise =false) system dynamics function
virtual void	h() =0 system output function
size_t	n_u() const Get number of inputs.
size_t	n_x() const Get number of states.
size_t	n_y() const Get number of outputs.
data_t	dt() const Get sample period.
const Vector &	x() const Get current state.
const Matrix &	P() const Get covariance of state estimate.
const Vector &	m() const Get current process disturbance/bias.
const Matrix &	P_m() const Get covariance of process disturbance estimate.
const Vector &	cx() const Get C*x.
const Vector &	y() const Get output.
const Vector &	x0() const Get initial state.
const Vector &	m0() const Get initial disturbance.
const Matrix &	A() const Get state matrix.
const Matrix &	B() const Get input matrix.
const Vector &	g() const Get input gain/conversion factor.
const Matrix &	C() const Get output matrix.
const Vector &	d() const Get output bias.
const Matrix &	Ke() const Get estimator gain.
const Matrix &	Ke_m() const Get estimator gain for process disturbance (m)
const Matrix &	Q() Get process noise covariance.
const Matrix &	Q_m() Get process noise covariance of disturbance evoluation.
const Matrix &	P0() Get covariance of initial state.
const Matrix &	P0_m() Get covariance of initial process disturbance.
void	set_A(const Matrix & A) Set state matrix.
void	set_B(const Matrix & B) Set input matrix.
void	set_m(const Vector & m, bool do_force_assign =false) Set process disturbance.
void	set_g(const Vector & g) Set input gain.
void	set_Q(const Matrix & Q) Set process noise covariance.
void	set_Q_m(const Matrix & Q_m) Set process noise covariance of disturbance evoluation.
void	set_x0(const Vector & x0) Set initial state.
void	set_P0(const Matrix & P0) Set covariance of initial state.
void	set_P0_m(const Matrix & P0_m) Set covariance of initial process disturbance.
void	set_C(const Matrix & C) Set output matrix.
void	set_d(const Vector & d) Set output bias.
void	set_x(const Vector & x) Set state of system.
void	Reset() Reset system variables.
void	Print() Print system variables to stdout.

Protected Functions #

	Name
virtual void	RecurseKe() =0 Recursively recalculate estimator gain (Ke)
void	InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)

Public Attributes #

	Name
bool	do_adapt_m whether to adaptively estimate disturbance m

Protected Attributes #

	Name
std::size_t	n_x_ number of states
std::size_t	n_u_ number of inputs
std::size_t	n_y_ number of outputs
data_t	dt_ sample period
Vector	x_ state
Matrix	P_ covariance of state estimate
Vector	m_ process disturbance
Matrix	P_m_ covariance of disturbance estimate
Vector	cx_ C*x.
Vector	y_ output
Vector	z_ measurement
Vector	x0_ initial state
Matrix	P0_ covariance of initial state estimate
Vector	m0_ initial process disturbance
Matrix	P0_m_ covariance of initial disturbance est.
Matrix	A_ state matrix
Matrix	B_ input matrix
Vector	g_ input gain
Matrix	Q_ covariance of process noise
Matrix	Q_m_ covariance of disturbance random walk
Matrix	C_ output matrix
Vector	d_ output bias
Matrix	Ke_ estimator gain
Matrix	Ke_m_ estimator gain for process disturbance

Public Function Details #

System #

System() =default

System #

System(
    size_t n_u,
    size_t n_x,
    size_t n_y,
    data_t dt,
    data_t p0 =kDefaultP0,
    data_t q0 =kDefaultQ0
)

Parameters:

n_u number of inputs
n_x number of states
n_y number of outputs
dt sample period
p0 diagonal elements for state estimate covariance
q0 diagonal elements for process noise covariance

~System #

inline virtual ~System()

Filter #

void Filter(
    const Vector & u_tm1,
    const Vector & z
)

Parameters:

u_tm1 input at t-minus-1
z_t current measurement

Given current measurement and input, filter data to produce causal state estimates using Kalman filtering, which procedes by predicting the state and subsequently updating.

Simulate #

virtual const Vector & Simulate(
    const Vector & u_tm1
) =0

Parameters:

u_tm1 input at time t-1

Return: simulated measurement at time t

Reimplemented by: lds::poisson::System::Simulate, lds::gaussian::System::Simulate

f #

inline void f(
    const Vector & u,
    bool do_add_noise =false
)

Parameters:

u input
do_add_noise whether to add simulated process noise

h #

virtual void h() =0

Reimplemented by: lds::poisson::System::h, lds::gaussian::System::h

n_u #

inline size_t n_u() const

n_x #

inline size_t n_x() const

n_y #

inline size_t n_y() const

dt #

inline data_t dt() const

x #

inline const Vector & x() const

P #

inline const Matrix & P() const

m #

inline const Vector & m() const

P_m #

inline const Matrix & P_m() const

cx #

inline const Vector & cx() const

y #

inline const Vector & y() const

x0 #

inline const Vector & x0() const

m0 #

inline const Vector & m0() const

A #

inline const Matrix & A() const

B #

inline const Matrix & B() const

g #

inline const Vector & g() const

C #

inline const Matrix & C() const

d #

inline const Vector & d() const

Ke #

inline const Matrix & Ke() const

Ke_m #

inline const Matrix & Ke_m() const

Q #

inline const Matrix & Q()

Q_m #

inline const Matrix & Q_m()

P0 #

inline const Matrix & P0()

P0_m #

inline const Matrix & P0_m()

set_A #

inline void set_A(
    const Matrix & A
)

set_B #

inline void set_B(
    const Matrix & B
)

set_m #

inline void set_m(
    const Vector & m,
    bool do_force_assign =false
)

set_g #

inline void set_g(
    const Vector & g
)

set_Q #

inline void set_Q(
    const Matrix & Q
)

set_Q_m #

inline void set_Q_m(
    const Matrix & Q_m
)

set_x0 #

inline void set_x0(
    const Vector & x0
)

set_P0 #

inline void set_P0(
    const Matrix & P0
)

set_P0_m #

inline void set_P0_m(
    const Matrix & P0_m
)

set_C #

inline void set_C(
    const Matrix & C
)

set_d #

inline void set_d(
    const Vector & d
)

set_x #

inline void set_x(
    const Vector & x
)

Reset #

void Reset()

Print #

void Print()

Protected Function Details #

RecurseKe #

virtual void RecurseKe() =0

Reimplemented by: lds::poisson::System::RecurseKe, lds::gaussian::System::RecurseKe

InitVars #

void InitVars(
    data_t p0 =kDefaultP0,
    data_t q0 =kDefaultQ0
)

Public Attribute Details #

do_adapt_m #

bool do_adapt_m {};

Protected Attribute Details #

n_x_ #

std::size_t n_x_ {};

n_u_ #

std::size_t n_u_ {};

n_y_ #

std::size_t n_y_ {};

dt_ #

data_t dt_ {};

x_ #

Vector x_;

P_ #

Matrix P_;

m_ #

Vector m_;

P_m_ #

Matrix P_m_;

cx_ #

Vector cx_;

y_ #

Vector y_;

z_ #

Vector z_;

x0_ #

Vector x0_;

P0_ #

Matrix P0_;

m0_ #

Vector m0_;

P0_m_ #

Matrix P0_m_;

A_ #

Matrix A_;

B_ #

Matrix B_;

g_ #

Vector g_;

Q_ #

Matrix Q_;

Q_m_ #

Matrix Q_m_;

C_ #

Matrix C_;

d_ #

Vector d_;

Ke_ #

Matrix Ke_;

Ke_m_ #

Matrix Ke_m_;

Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time

lds::System #

Public Functions #

Protected Functions #

Public Attributes #

Protected Attributes #

Public Function Details #

System #

System #

~System #

Filter #

Simulate #

f #

h #

n_u #

n_x #

n_y #

dt #

x #

P #

m #

P_m #

cx #

y #

x0 #

m0 #

A #

B #

g #

C #

d #

Ke #

Ke_m #

Q #

Q_m #

P0 #

P0_m #

set_A #

set_B #

set_m #

set_g #

set_Q #

set_Q_m #

set_x0 #

set_P0 #

set_P0_m #

set_C #

set_d #

set_x #

Reset #

Print #

Protected Function Details #

RecurseKe #

InitVars #

Public Attribute Details #

do_adapt_m #

Protected Attribute Details #

**n_x_** #

**n_u_** #

**n_y_** #

**dt_** #

**x_** #

**P_** #

**m_** #

**P_m_** #

**cx_** #

**y_** #

**z_** #

**x0_** #

**P0_** #

**m0_** #

**P0_m_** #

**A_** #

**B_** #

**g_** #

**Q_** #

**Q_m_** #

**C_** #

**d_** #

**Ke_** #

**Ke_m_** #

n_x_ #

n_u_ #

n_y_ #

dt_ #

x_ #

P_ #

m_ #

P_m_ #

cx_ #

y_ #

z_ #

x0_ #

P0_ #

m0_ #

P0_m_ #

A_ #

B_ #

g_ #

Q_ #

Q_m_ #

C_ #

d_ #

Ke_ #

Ke_m_ #