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the variance predicted by P Analysis of residual sequences is one of the best methods to detect model de ciencies In doing so, the analyst should keep in mind that Hk Pk Hk + Rk is predicting the variance of the residual rk over an ensemble of experiments Therefore, for example, the residual sequence of Figure 67 does indicate a model de ciency, but by itself does not indicate that the residual is biased If the experiment was repeated N times, with the residual sequence from the i-th trial run denoted by rki , and all residual sequences tended to drift in the same direction, then the analyst might reconsider the system model to nd the cause of and model the bias AlterN natively, if the average of the residual sequences rk = i=1 rki is essentially zero mean, but the ensemble of residual sequences is not well-modeled by the P , then the analyst must consider possible alternative explanations To illustrate the nature of a residual sequence for a lter designed with a correct model, Figure 68 shows the residual for the lter designed with Qh = 05 The residual is essentially zero mean, has no noticeable trends, and has steady-state variance that closely matches the P

The main references for the presentation of this chapter were [58, 93]

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Figure 68: Residual sequence from a single trial for the lter designed with Qh = 05 The solid line with x s is the residual The solid lines without x s is the lters predicted values for r where r = H P H + R

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