## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter

### Package Content

Name | Description
--- | ---
KF | Discrete State Space block
KF_outer | Discrete State Space block
KF_inner | Discrete State Space block
SRKF_inner | Discrete State Space block
SRKF | Discrete State Space block
SRKF_inner2 | Discrete State Space block
SRKF2 | Discrete State Space block

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.KF

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
ss | Continuous linear system model
sampleTime | Base sample time for discrete blocks [s]
Q[size(dss.B, 2), size(dss.B, 2)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |
dss | Discrete linear system model

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
x_est_pre[size(dss.A, 1)] | Preceding estimated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.KF_outer

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
ss | Continuous linear system model
sampleTime | Base sample time for discrete blocks [s]
dss | Discrete linear system model
wB[size(dss.B, 1), size(dss.B, 2)] | Wheighting matrix for input noise covariance matrix of the previous instant
Q[size(dss.B, 2), size(dss.B, 2)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.KF_inner

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
dss | Discrete linear system model
sampleTime | Base sample time for discrete blocks [s]
wB[size(dss.B, 1), size(dss.B, 2)] | Wheighting matrix for input noise covariance matrix of the previous instant
Q[size(dss.B, 2), size(dss.B, 2)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.SRKF_inner

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
dss | Discrete linear system model
sampleTime | Base sample time for discrete blocks [s]
wB[size(dss.B, 1), size(dss.B, 2)] | Weighting matrix of the input noise covariance matrix of the previous instant
Q[size(dss.B, 2), size(dss.B, 2)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.SRKF

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
ss | Continuous linear system model
sampleTime | Base sample time for discrete blocks [s]
dss | Discrete linear system model
wB[size(dss.B, 1), size(dss.B, 2)] | Weighting matrix of the input noise covariance matrix of the previous instant
Q[size(dss.B, 2), size(dss.B, 2)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.SRKF_inner2

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
dss | Discrete linear system model
sampleTime | Base sample time for discrete blocks [s]
Q[size(dss.A, 1), size(dss.A, 1)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input

## Modelica_LinearSystems2.WorkInProgress.Controller.KalmanFilter.SRKF2

Discrete State Space block

### Information

### Parameters

Name | Description
--- | ---
ss | Continuous linear system model
sampleTime | Base sample time for discrete blocks [s]
dss | Discrete linear system model
Q[size(dss.A, 1), size(dss.A, 1)] | Input or process noise covariance matrix of the previous instant
R[size(dss.C, 1), size(dss.C, 1)] | Output or measurement noise covariance matrix of the previous instant
P0[size(dss.A, 1), size(dss.A, 1)] | Initial state covariance matrix of the previous instant
x_init[size(dss.A, 1)] |

### Connectors

Name | Description
--- | ---
x_est[size(dss.A, 1)] | Current estmated state vector
y[size(dss.C, 1)] | measured output
u[size(dss.B, 2)] | Current input