Package edu.wpi.first.math.estimator

Class SteadyStateKalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>

java.lang.Object

edu.wpi.first.math.estimator.SteadyStateKalmanFilter<States,Inputs,Outputs>

Type Parameters:

States - Number of states.

Inputs - Number of inputs.

Outputs - Number of outputs.

public class SteadyStateKalmanFilter<States extends Num,Inputs extends Num,Outputs extends Num>
extends Object

A Kalman filter combines predictions from a model and measurements to give an estimate of the true system state. This is useful because many states cannot be measured directly as a result of sensor noise, or because the state is "hidden".

Kalman filters use a K gain matrix to determine whether to trust the model or measurements more. Kalman filter theory uses statistics to compute an optimal K gain which minimizes the sum of squares error in the state estimate. This K gain is used to correct the state estimate by some amount of the difference between the actual measurements and the measurements predicted by the model.

This class assumes predict() and correct() are called in pairs, so the Kalman gain converges to a steady-state value. If they aren't, use KalmanFilter instead.

For more on the underlying math, read https://file.tavsys.net/control/controls-engineering-in-frc.pdf chapter 9 "Stochastic control theory".

Constructor Summary

Constructors

Constructor	Description
SteadyStateKalmanFilter(Nat<States> states, Nat<Outputs> outputs, LinearSystem<States,Inputs,Outputs> plant, Matrix<States,N1> stateStdDevs, Matrix<Outputs,N1> measurementStdDevs, double dtSeconds)	Constructs a steady-state Kalman filter with the given plant.

Method Summary

Modifier and Type	Method	Description
void	correct(Matrix<Inputs,N1> u, Matrix<Outputs,N1> y)	Correct the state estimate x-hat using the measurements in y.
Matrix<States,Outputs>	getK()	Returns the steady-state Kalman gain matrix K.
double	getK(int row, int col)	Returns an element of the steady-state Kalman gain matrix K.
Matrix<States,N1>	getXhat()	Returns the state estimate x-hat.
double	getXhat(int row)	Returns an element of the state estimate x-hat.
void	predict(Matrix<Inputs,N1> u, double dtSeconds)	Project the model into the future with a new control input u.
final void	reset()	Resets the observer.
void	setXhat(int row, double value)	Set an element of the initial state estimate x-hat.
void	setXhat(Matrix<States,N1> xhat)	Set initial state estimate x-hat.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

SteadyStateKalmanFilter

public SteadyStateKalmanFilter(Nat<States> states,
 Nat<Outputs> outputs,
 LinearSystem<States,Inputs,Outputs> plant,
 Matrix<States,N1> stateStdDevs,
 Matrix<Outputs,N1> measurementStdDevs,
 double dtSeconds)

Constructs a steady-state Kalman filter with the given plant.

See https://docs.wpilib.org/en/stable/docs/software/advanced-controls/state-space/state-space-observers.html#process-and-measurement-noise-covariance-matrices for how to select the standard deviations.

Parameters:

states - A Nat representing the states of the system.

outputs - A Nat representing the outputs of the system.

plant - The plant used for the prediction step.

stateStdDevs - Standard deviations of model states.

measurementStdDevs - Standard deviations of measurements.

dtSeconds - Nominal discretization timestep.

Throws:

IllegalArgumentException - If the system is undetectable.

Method Details

reset

public final void reset()

Resets the observer.

getK

public Matrix<States,Outputs> getK()

Returns the steady-state Kalman gain matrix K.

Returns:

The steady-state Kalman gain matrix K.

getK

public double getK(int row,
 int col)

Returns an element of the steady-state Kalman gain matrix K.

Parameters:

row - Row of K.

col - Column of K.

Returns:

the element (i, j) of the steady-state Kalman gain matrix K.

setXhat

public void setXhat(Matrix<States,N1> xhat)

Set initial state estimate x-hat.

Parameters:

xhat - The state estimate x-hat.

setXhat

public void setXhat(int row,
 double value)

Set an element of the initial state estimate x-hat.

Parameters:

row - Row of x-hat.

value - Value for element of x-hat.

getXhat

public Matrix<States,N1> getXhat()

Returns the state estimate x-hat.

Returns:

The state estimate x-hat.

getXhat

public double getXhat(int row)

Returns an element of the state estimate x-hat.

Parameters:

row - Row of x-hat.

Returns:

the state estimate x-hat at that row.

predict

public void predict(Matrix<Inputs,N1> u,
 double dtSeconds)

Project the model into the future with a new control input u.

Parameters:

u - New control input from controller.

dtSeconds - Timestep for prediction.

correct

public void correct(Matrix<Inputs,N1> u,
 Matrix<Outputs,N1> y)

Correct the state estimate x-hat using the measurements in y.

Parameters:

u - Same control input used in the last predict step.

y - Measurement vector.