Compute Residuals and State Estimation Errors
This example shows how to estimate the states of a discrete-time Van der Pol oscillator and compute state estimation errors and residuals for validating the estimation. The residuals are the output estimation errors, that is, they are the difference between the measured and estimated outputs.
In the Simulink™ model, the Van der Pol Oscillator block implements the oscillator with nonlinearity parameter, mu, equal to 1. The oscillator has two states. A noisy measurement of the first state x1 is available.
The model uses the Unscented Kalman Filter block to estimate the states of the oscillator. Since the block requires discrete-time inputs, the Rate Transition block samples x1 to give the discretized output measurement yMeasured[k] at time step k. The Unscented Kalman Filter block outputs the estimated state values xhat[k|k] at time step k, using yMeasured until time k. The filter block uses the previously written and saved state transition and measurement functions, vdpStateFcn.m and vdpMeasurementFcn.m. For information about these functions, see docid:control_ug.bvf59j3.
To validate the state estimation, the model computes the residuals in the Generate Residual block. In addition, since the true state values are known, the model also computes the state estimation errors.
To compute the residuals, the Generate Residual block first computes the estimated output yPredicted[k|k-1] using the estimated states and state transition and measurement functions. Here, yPredicted[k|k-1] is the estimated output at time step k, predicted using output measurements until time step k-1. The block then computes the residual at time step k as yMeasured[k] - yPredicted[k|k-1].
Examine the residuals and state estimation errors, and ensure that they have a small magnitude, zero mean, and low autocorrelation.
In this example, the Unscented Kalman Filter block outputs xhat[k|k] because the Use the current measurements to improve state estimates parameter of the block is selected. If you clear this parameter, the block instead outputs xhat[k|k-1], the predicted state value at time step k, using yMeasured until time k-1. In this case, compute yPredicted[k|k-1] = MeasurementFcn(xhat[k|k-1]), where MeasurementFcn is the measurement function for your system.