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Dynamical systems, for instance in model predictive control, often containunknown parameters, which must be determined during system operation. Online oron-the-fly parameter identification methods are therefore necessary. Thechallenge of online methods is that one must continuously estimate parametersas experimental data becomes available. The existing techniques in the contextof time-dependent partial differential equations exclude the case where thesystem depends nonlinearly on the parameters.Based on a model referenceadaptive system approach, we present an online parameter identification methodfor nonlinear infinite-dimensional evolutionary system.

A model reference adaptive system approach for nonlinear online parameter identification