Meng Xia

University of Notre Dame, MathWorks (United States)

Papers

Total Citations

H-Index

About

Meng Xia is a control systems researcher whose work centers on adaptive and robust control for nonlinear systems, with a particular focus on handling real-world uncertainties that challenge classical control approaches. His most significant contributions lie in the development of modular indirect adaptive control frameworks, which cleverly separate the challenges of robust stabilization and parameter estimation into tractable, interconnected components. Xia's most cited work (2015, 13 citations) introduced an extremum seeking-based indirect adaptive control strategy capable of managing time-varying parametric uncertainties in nonlinear systems — a notably difficult problem in the field. This was extended in subsequent research to incorporate learning-based iterative methods (2018, 11 citations), demonstrating how repeated interactions with a system can progressively refine control performance. A 2016 paper (6 citations) further consolidated the modular framework for a broader class of nonlinear systems with structured uncertainties. A recurring theme across Xia's publications is the use of input-to-state stability (ISS) as a theoretical backbone, lending mathematical rigor to his designs. While his citation counts reflect a specialized research niche, his methods offer meaningful tools for engineers dealing with uncertain, real-world dynamical systems in robotics, aerospace, and process control applications.