Estimation Functions
- Updated2023-03-14
- 3 minute(s) read
Owning Palette: Simulation VIs and Functions
Requires: Control Design and Simulation Module. This topic might not match its corresponding palette in LabVIEW depending on your operating system, licensed product(s), and target.
Use the Estimation functions to estimate the states of a state-space system. The state-space system can be deterministic or stochastic, continuous or discrete, linear or nonlinear, and completely or partially observable. Refer to the LabVIEW Control Design User Manual for more information about using the Estimation functions.
The functions on this palette can return general LabVIEW error codes or specific Simulation error codes. If you use the functions on this palette in a Control & Simulation Loop, LabVIEW sends any errors that these functions return to the Error output on the Output Node of the Control & Simulation Loop.
| Palette Object | Description |
|---|---|
| Continuous Extended Kalman Filter | Calculates the estimated states and estimated outputs of a continuous nonlinear stochastic state-space system. This function also calculates the Kalman gain and associated estimation error covariance matrix for the model. |
| Continuous Kalman Filter | Implements a linear, time-varying Kalman filter for a continuous linear time-invariant (LTI) or linear time-varying (LTV) stochastic state-space model. This function calculates the Kalman filtered state estimates and outputs at time t. This function also calculates the Kalman filter gain and the associated estimation error covariance matrix. |
| Continuous Nonlinear Noisy Plant | Simulates a noisy continuous nonlinear plant model.
If you use the Internal Noise instance of this function, this function generates samples of the noise vectors using the model you wire to the Noise Model input. If you use the External Noise instance of this function, you can use the CD Correlated Gaussian Random Noise VI to generate samples of the noise vectors. You also can generate samples of the noise vectors using other VIs or specify the noise vectors to be deterministic disturbances. |
| Continuous Observer | Implements an observer for a continuous linear time-invariant (LTI) state-space model. |
| Discrete Extended Kalman Filter | Calculates the estimated states, predicted states, and estimated outputs of a discrete nonlinear stochastic state-space system. This function also calculates the Kalman gain and associated prediction error covariance matrix. |
| Discrete Kalman Filter | Implements a discrete-time, linear time-varying, recursive Kalman filter for a discrete linear time-invariant (LTI) or a linear time-varying (LTV) stochastic state-space model. The Discrete Kalman Filter function calculates the predicted state estimates, the corrected state estimates, the corresponding gains used to calculate these estimates, and the associated prediction and estimation error covariances corresponding to these estimates. This function also calculates the estimated output. |
| Discrete Nonlinear Noisy Plant | Simulates a noisy discrete nonlinear plant model.
If you use the Internal Noise instance of this function, this function generates samples of the noise vectors using the model you wire to the Noise Model input. If you use the External Noise instance of this function, you can use the CD Correlated Gaussian Random Noise VI to generate samples of the noise vectors. You also can generate samples of the noise vectors using other VIs or specify the noise vectors to be deterministic disturbances. |
| Discrete Observer | Implements a discrete-time observer for a linear, time-invariant (LTI) state-space system model. |
| Discrete Stochastic State Space | Implements a discrete-time, linear, stochastic state-space system. You define the system model by specifying the input, output, state, and direct transmission matrices. You also specify the matrices relating the process noise to the system states and outputs. |