Abstract

Online parameter identification for dynamical systems is the task of estimating model parameters simultaneously to the time evolution of the physical state and plays a crucial role in adaptive control. The existing techniques for time-dependent partial differential equations exclude the case of partial state observations or require to solve online Ricatti-type operator evolution equations accompanied with high computational costs. In this paper we suggest and analyze an extended state approach to online parameter identification in a class of possibly nonlinear PDEs that avoids Ricatti-type equations but still has the potential to allow for partial state observations. Numerical examples underline the feasibility of our method.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.