Abstract
Guidelines for specifying design parameters for minimum crest factor multisine signals per the approach of Guillaume et al. are presented. These guidelines are evaluated for the identification of nonlinear process systems. The minimum crest factor multisine signals offer some distinct advantages over both Schroeder phased multisine signals and m-level Pseudo-Random Sequence (m-level PRS) signals with respect to "plant-friendliness" considerations. These signals can be used to reduce the effects of nonlinearity in obtaining an empirical transfer function estimate (ETFE). As an example, the ETFE of a Rapid Thermal Processing (RTP) reactor simulation is constructed. "Plant-friendly" issues are also discussed and illustrated in the identification and control of a CSTR simulation via "Model-on-Demand" estimation. This provides a compelling example, since the "Model-on-Demand" estimator is a data-driven nonlinear identification approach.
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