Linear time-periodic dynamical systems: An H2 analysis and a model reduction framework
This work provides a principled model reduction technique for high-order LTP systems, which are common in fluid dynamics, electronics, and structural mechanics, but the contribution is incremental as it generalizes existing LTI methods.
The paper develops a model reduction framework for linear time-periodic (LTP) dynamical systems, extending H2 optimal methods from linear time-invariant systems. The method is validated on two numerical examples, demonstrating its effectiveness.
Linear time-periodic (LTP) dynamical systems frequently appear in the modeling of phenomena related to fluid dynamics, electronic circuits, and structural mechanics via linearization centered around known periodic orbits of nonlinear models. Such LTP systems can reach orders that make repeated simulation or other necessary analysis prohibitive, motivating the need for model reduction. We develop here an algorithmic framework for constructing reduced models that retains the linear time-periodic structure of the original LTP system. Our approach generalizes optimal approaches that have been established previously for linear time-invariant (LTI) model reduction problems. We employ an extension of the usual H2 Hardy space defined for the LTI setting to time-periodic systems and within this broader framework develop an a posteriori error bound expressible in terms of related LTI systems. Optimization of this bound motivates our algorithm. We illustrate the success of our method on two numerical examples.