Stability and Signal Generator Agnostic Moment Matching
This talk explores advancements in model reduction that modernize traditional moment matching by introducing a data-driven procedure for unknown signal generators and a closed-loop interpolation framework that extends these techniques to unstable systems.
Overview
Moment matching is a model reduction technique that allows the construction of reduced-order models preserving specific moments. These moments are associated with the steady-state output response of the system to be reduced, interconnected in an open-loop fashion with a signal generator. Model matching thus relies on strong stability properties and on the availability of a model of the signal generator.
To relax these requirements, in the first part of the talk we introduce a data-driven procedure for computing reduced-order models from input-output data generated by an agnostic signal generator. The moments are directly identified from the output of the system to be reduced, and the resulting reduced-order models achieve asymptotic matching.
To relax the stability requirements, in the second part of the talk we revisit the notion of moment matching by introducing the concept of closed-loop interpolation. This notion relies upon the construction of a novel class of signal generators, which are feedback-interconnected with the plant to be reduced, and allows the definition of moments and models for unstable systems. The existence of a family of models that parameterise all, possibly unstable, systems achieving moment matching in a closed-loop fashion is also presented.
Presenters
Brief Biography
Alessandro Astolfi is professor of applied mathematics and computational science at KAUST and a leading scholar and pioneer in nonlinear systems and control. His research focuses on the analysis and design of advanced control strategies for complex, large-scale, and interconnected systems, with applications spanning robotics, power systems, aerospace, and social and biological systems.
Before joining KAUST, he was professor of nonlinear control theory at Imperial College London and also held professorial appointments at the University of Rome Tor Vergata and Politecnico di Milano.
Astolfi’s work delivers mathematically rigorous methods and algorithms that address engineering challenges. He collaborates closely with academic institutions and industry leaders worldwide, advancing technologies that support infrastructure, energy, and intelligent automation.
An accomplished educator and mentor, Astolfi is committed to developing the next generation of engineers and researchers. At KAUST, he leads interdisciplinary research initiatives and provides master’s and Ph.D. students with opportunities to engage in high-impact projects at the forefront of systems and control theory.
