System Identification for H∞-Robust Control Design
In conventional identification techniques a model is proposed which is supposed to be capable of representing the process behavior under study. Parameters are then tuned such that the model outputs correspond according to some criterion for the dominant part of a measured data set. Deviations are thought to be concentrated in some error source in the model, such as output error, prediction error, equation error, and so forth. This artificial error source explains all disturbances acting on the process as well as for all model deviations from the real dynamic behavior of the process. Furthermore, stochastic assumptions have to be proposed concerning the errors leading to the criterion and as a result a “best” model is produced together with some stochastically based range for the parameters and/or dynamic behavior.
KeywordsModel Error Uncertainty Region Weighting Filter True Process Robust Control Design
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